Geosaskatoon 2023

Table of contents :
Geo Signage (8.5 x 11 in) (5)
Adajar, Joash - Preliminary results...
Cao, Laifa - Use of CFA piles..
1 introduction
2 Review of CFA Piles in southern ontario
2.1 CFA Pile Resistance
2.2 Published Static Pile Load Tests
3 Case Study
4 discussions
5. conclusions
Chalajour, Sepehr - Instrumentation and..
Liu, Chunhui - Centrifuge modeling
Shah, Imran - Strength Gain..
Sharma, Keshab - Performance of Composite
Geo Signage (8.5 x 11 in) (5)
Dallaire, Catlan - CASE STUDY
Elwood, David - Saskatoon River Valley Geohazards – An Update
Law, Graeme - Design and Construction..
Osback, Jon - The Borden Bridges..
Rodriguez, Jorge - Rockfall Mitigation..
Geo Signage (8.5 x 11 in) (5)
Bathurst, Richard - Solutions for the reinforced..
Blanchette, Maxime - Incremental permanent
Huang, Mian - Field experimental investigation..
1 INTRODUCTION
1.1 Background
1.2 Objectives
2 Construction and Instrumentation
2.1 Pavement construction
2.2 Instrumentation
3 Field tests
3.1 Testing methodology
3.2.2 Trafficking test results
4 Conclusions
5. Acknowledgement
6. References
Landry, Ethan - Evaluating the Performance of...
Lees, Andrew - An investigation of tyre contact area on unbound aggregate
Schwarz, Lois - Vertical deformation analyses...
Geo Signage (8.5 x 11 in) (5)
Barrero, Andres - Soil-Simulator scripts..
Carey, Trevor - Magnitude scaling..
Jahanbakhshzadeh, Abtin - Influence factors..
Liang, Yawu - Investigation of strain...
Mofrad, Mina - Extending dilatancy-based..
Razavi, Seyyed -On the numerical issues..
Geo Signage (8.5 x 11 in) (5)
Belem, Tikou - Assessment of the..
Chai-Onn, Madison - Effects of climate...
Erlandson, Erika - Large-scale laboratory..
Kabwe, Louis - Revisiting Large-Scale ..
Sylvain, Karine - Improving reactive waste..
Williams, Holly - Applying threshold...
Geo Signage (8.5 x 11 in) (5)
Asim, Muhammad - Investigating Shear..
Ayebazibwe, Derrick - Effect of Heating..
Ouhadi, Vahid - Organic contaminants..
Patel, Arazooben - Ageing effects on..
Simo Tenekam, Harold - Creation and segmentation...
Zhang, Yunhai - Evaluation of the effect..
Geo Signage (8.5 x 11 in) (5)
Akbarian, Roham - Geological Model..
Kanungo, Mrinmoy - Forensic Engineering..
Kirstein, Andries - Generation of In Situ...
Osback, Jon - Shear Strength of Lake Saskatchewan...
Vlachopoulos, Nicholas - Historical Background...
Wollenberg-Barron, Taylor - S042 Rock Slope..
Geo Signage (8.5 x 11 in) (5)
Fudge, Ashlee - A TOWED MARINE TIME-DOMAIN...
Melnik, Anatoly - Surface and groundwater...
Geo Signage (8.5 x 11 in) (5)
Diederichs, Kaitlyn - Bridging the Data Gap..
Proceedings Tues-Wed.pdf
Geo Signage (8.5 x 11 in) (6)
Berru Garcia, Ingrid - RTK surveyor..
Daniel, Chris - Design, Installation...
Li, Samuel - A Semi-Empirical Oil Sands...
Marcotte, Bryce - Insights into “medium term”..
Soltanieh, Amir - Landslide Monitoring..
Troncosco Klein, Camila - Dutchman’s Ridge Results..
Geo Signage (8.5 x 11 in) (6)
Byrne, Yasmin - Converting between..
Contreras, Julian - Comparison of the nonlinear modulus..
Karmaker, Ripon - A case study on geotechnical..
Nguyen, Vicki - Geotechnical characterization..
Zafarani, Ali - In-situ and laboratory investigations..
Geo Signage (8.5 x 11 in) (7)
Andersen, Darren - Investigation of small-diameter..
Bracic, Jan - Geotechnical Risk Management...
Chakraborty, Sudipta - Loading-unloading-reloading..
Dhar, Ashutosh - Investigation of buried pipes..
Manicke, Heidi - Performance Review of Strain Gauges..
Geo Signage (8.5 x 11 in) (6)
Abdelghany, Yasser - Performance of DSM walls..
Forero Gaona, John - Preliminary impact force..
Li, Yalin - Numerical Simulation of Rock Bridge..
Moncada, Anibal - Numerical modelling of geosynthetic..
Nasir, Othman - Instrumentation and Deformation..
Nasir, Othman - Numerical analysis of..
Geo Signage (8.5 x 11 in) (6)
Aubertin, Jonathan - Impact of the damage zone..
Deiminiat, Akram - Representation of progressive shear..
Ferg, Daniel - Calf Robe Bridge Abutment Stabilization
Gagnon, Emelie - Challenges and solutions of 2D..
Guo, Ghuyue - The Effects of Temperature on..
Milne, Douglas - Underground Instrumentation Program
Geo Signage (8.5 x 11 in) (6)
Hoyle, William - Laboratory Investigation..
Mohajan, Ankan - Behaviour of Two..
Roustaei, Mahya - Imaging a Sealed Shale..
Swerid, Amy - Considerations for Internal Erosion..
Wallin, Kevin - Utilizing a microfiber-optic pressure..
Zheng, Wenbo - Simulation of solid-fluid interaction..
Geo Signage (8.5 x 11 in) (6)
Chang, Wonjae - Cold-tolerant bioremediation..
Roy, James - Assessing toxicity risk to aquatic life..
Ryan, Cathryn - Radon, atmospheric pressure...
Geo Signage (8.5 x 11 in) (6)
Gelleta, Kevin - Incorporation of measured pore-water..
Gutierrez, Jorge - West Val Marie Dam..
Malonzo, Marvin - Delayed Slope Instabilities..
Sadr Daneshi, Amir Hossein - An Experimental Study..
Geo Signage (8.5 x 11 in) (6)
Almeida, Alexandra - Continuous Flight Augercast..
Hansen, Darcy - Driven Pile Resistance..
Islam, Naveel - Centrifuge Modeling..
Kennedy, Madison - Bi-Directional Static Load Test..
Qu, Greg - An Improved Approach..
Tigchelaar, Mark - Settlement Control..
Geo Signage (8.5 x 11 in) (6)
Bathurst, Richard - The case for true probabilistic
Cunningham, Michael - Design and Construction
Froese, Ken - Bridging of Compressive Soils Using..
Henderson, Mark - Drilled Shaft Installation..
Mills, Bernie - Prediction and mitigation of construction..
Geo Signage (8.5 x 11 in) (6)
Abou-Seido, Faisal - Bedrock Stabilization..
Baba, Hafeez - Design and Construction..
Gonzalez, Fernando - Design And Construction..
Imamura, Fernanda B. T. -Field Research..
Kermani, Mohammad - Evaluation of Creep..
Rahman, Nazmur - Potential Issues on MSE external stability design in light of the recent Canadian and AASHTO Bridge Design Codes
Vannobel, Pierre - Conception et construction des fondations d'une passerelle pour motoneiges sur la rivière Aisley
Geo Signage (8.5 x 11 in) (6)
Baninajarian, Leila - Probabilistic analysis..
Huang, Ellen - Seepage and Stability...
Kanungo, Mrinmoy - Forensic Evaluation..
Ozeritskiy, Konstantin - Understanding the influence..
Pekinasova, Anna - Application of thermo-hydro-mechanical..
Sahragardjuneghani, Faranak - Finite Element..
Siddiqua, Sumi - Modelling of long-term..
Geo Signage (8.5 x 11 in) (6)
Garcia Torres, Samy - Assessment of slurry..
Jahanbakhshzadeh, Abtin - Numerical analysis.
Sadeghi, Niloufarsadat - Geological and geomechanical..
Geo Signage (8.5 x 11 in) (6)
Krifa Krifa, Arij - Numerical study..
Maleksaeedi, Emad - Impact of specimen..
Mayadunna, Bhagya - Influence of Hydraulic...
Shirzadi, Ashkan - Active Earth Pressure.
Wang, Junjie - A framework for estimating...
Geo Signage (8.5 x 11 in) (6)
Chevalier, Corentin - Effect of the degree..
Dos Ramos Souza , Weber Anselmo -Assessing the creep..
El Mekari, Khalil - Establishment of the mechanical..
Goharzay, Maral - A modified framework to describe..
Polito, Carmine - Evaluation of the Relative..
Sagade, S. Aparna - Study of fresh properties..
Saremi , Amirreza -Study of the Impact of..
Geo Signage (8.5 x 11 in) (6)
Eichhorn, Geoffrey - Monitoring of permafrost..
Hoeve, M. Ed -A Review of Ground Temperatures..
Maghoul, Pooneh - A novel triaxial testing system..
Maghoul, Pooneh - Characterization of frozen..
Mohammadi, Zakieh - Estimating Thaw-Settlement..
Picard, M. Zakary - Hydraulic conductivity..
Roghangar, Khatereh - Critical parameters..
Geo Signage (8.5 x 11 in) (6)
Campos, M. Daniel - Advancing Geophysical Imaging..
Dompierre, Kathryn -3D seepage and slope..
Freistadt, Kevin - Study on Borehole Backfill..
Krug, Charlie -Recent Slurry Cutoff Walls
Lees, Andrew- Could regular satellite InSAR
Razavi, Seyyed Kazem - Influence of Material...
Yang, Kun - Application of Aggregate Filled..
Geo Signage (8.5 x 11 in) (6)
Bashir, Rashid -Assessing the Impact..
Evrard Niamke , Kakou Emmanuel - Laboratory Assessment..
Fillion , M. Marc-Alexandre -Design and construction..
Hfaiedh, Yosra- Hydraulic properties..
Makinde, Samuel- Preliminary Assessment .
Matin-Rouhani, Hadi- Test Sample Development...
1 Introduction
2 materials and methods
2.1 Materials
2.2 Sample Preparation and Experimental Methods
2.2.1 Mixing Method
2.2.2 Moisture-Density Relationship
2.2.3 Compressive Strength Test
2.2.4 Hydraulic Conductivity Test
2.3 Testing Plan
3 Results
3.1 Tier 1
3.2 Tier 2
3.2.1 Moisture-Density Relationship
3.2.2 Compressive Strength Results
3.2.3 Hydraulic Conductivity Test Results
4 Conclusions
5 Acknowledgments
6 References
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Boone, Storer - Re-evaluation..
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Nobre, Silvia - Cyclic Peak Particle Velocity..
Weerasekara, Lalinda- Performance of buried..
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Mahmoudipour, Reza- Enhanced wick-drain...
Yang, Beverly- The importance of data..
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Lin, Cui- Feasible Borehole Angles for 3D..
Rostam Sowlat, Iman- A new approach to..
Vadiee, Saeed - Thermal-Hydro-Mechanical..
Zheng, Wenbo- Quantification of..

Citation preview

CONFERENCE PROCEEDINGS

Table of Contents Papers by session

Monday, October 2, 2023

Wednesday, October 4, 2023

10:30 am - 12:00 pm Foundations I Geohazards I Stabilisation and Reinforcementent Tailings and Liquefactions

8:30 am - 10:00 am Case Studies I Foundations III Numerical Models II Tailings and Rock Unsaturated Soils

3:00 pm - 4:30 pm Cover Systems Geoenvironmental II Geohazards II Hydrogeology II REDI (Special Session)

Tuesday, October 3, 2023 10:30 am - 12:00 pm Geohazards III Insitu Investigations Pipeline Geotechnics 1:30 pm - 3:00 pm Numerical Models I Rock Mechanics I 3:30 pm - 5:00 pm Advanced Testing I Contaminant Transport Dams and Embankments I Foundations II

10:30 am - 12:00 pm Advanced Testing II Cold Regions Dams and Embankments II Geoenvironmental III Ground Improvement 1:30 pm - 3:00 pm Advanced Testing III Case Studies II Geohazards IV Innovative Geotechnical Rock Mechanics II

Monday, October 2, 2023

FOUNDATIONS I

Preliminary results from field monitoring of frost heave effects on steel H-piles Joash Bryan Q. Adajar & Marolo C. Alfaro University of Manitoba, Winnipeg, MB, Canada Lukas U. Arenson BGC Engineering Ltd., Vancouver, BC, Canada ABSTRACT Frost heave in soils during winter can significantly affect pile foundations. Piles are prone to experience significant upward displacement due to frost heave forces. A part of these frost heave forces, acting on pile foundations in response to the freezing of the surrounding soil, are contributed by adfreeze stresses along the pile shaft. Research is ongoing to improve the understanding of adfreeze stresses on piles. This paper discusses a unique field instrumentation program designed to quantify adfreeze stresses and to better understand the mechanisms involved with their development on piles due to frost heave. Strain gauges were installed on the pile shaft to measure adfreeze stresses. Piezometers, thermistor strings, magnetic sensors, Shape Accel Arrays, and heaving pins/plates were installed in the vicinity of the pile to monitor parameters that can be beneficial in characterizing the mobilization of adfreeze stresses on piles. Preliminary data collected from the first monitored winter period are also presented in this paper. RÉSUMÉ Le soulèvement par le gel des sols pendant l'hiver peut affecter considérablement les fondations sur pieux. Les pieux sont susceptibles de subir un déplacement vers le haut important en raison des forces de soulèvement dû au gel. Une partie de ces forces de soulèvement par le gel, agissant sur les fondations des pieux en réponse au gel du sol environnant, est due aux contraintes de gel le long du fût du pieu. Actuellement, il existe une lacune dans la recherche dans la compréhension des contraintes de gel sur les pieux. Cet article présente un programme unique d'instrumentation de terrain conçu pour quantifier les contraintes de gel et pour mieux comprendre les mécanismes impliqués dans leur développement sur les pieux. Des jauges de contrainte ont été installées sur le fût du pieu pour mesurer les contraintes de gel. Des piézomètres, des chaînes de thermistances, des capteurs magnétiques, des réseaux Shape Accel et des broches/plaques d'attrape ont été installés à proximité du pieu pour surveiller les paramètres qui peuvent être bénéfiques pour caractériser le développement des contraintes de gel sur les pieux. Les données préliminaires recueillies à partir de la 1ère période hivernale surveillée sont également présentées dans cet article. 1

INTRODUCTION

Piles are among the most common type of foundations used for different structures in the province of Manitoba due to soft clay and silt deposits in the area. When soils within the active layer freeze in winter, adfreezing occurs at the pile-soil interface. As frost heave develops, pile foundations are prone to experience frost heave uplift forces due to adfreeze stresses along the pile shaft that can be enough to induce significant upward movements, known as frost jacking. This uplift movement of the piles decreases the overall stability of the structure and compromises their capacity. Pile foundations in cold regions are designed and built to withstand such adfreeze stresses. If these forces are not properly accounted for during pile design, the structural integrity of the structure will be at risk, and failure may occur once significant upward movement develops. There are different approaches presented in literature to estimate adfreeze in piles. Current available methods only capture a small portion of the entire process in properly measuring these stresses, and they show significant variability. This variability requires a better understanding of the mobilization of adfreeze stresses along the pile shaft. Additionally, the current approach in considering adfreeze stresses for pile design here in Manitoba seems outdated

as it still adopts the method from the Canadian Foundation Engineering Manual, where values were derived from a study 30 years ago. This paper presents the field instrumentation and monitoring program undertaken on a research test pile to quantify adfreeze stresses in the field and to better understand the mechanisms involved with the development of adfreeze stresses on pile foundations in cold regions. Among the methods available to measure adfreeze stresses in the field, the strain gauge method (SGM) was chosen as it presents the advantage of measuring adfreeze stresses at different sections of the pile shaft (Johnson and Buska, 1988). The SGM also avoids underestimating measured adfreeze stresses as the monitored strains are isolated from the shaft resistance below the active layer. The instrumentation and monitoring program are discussed in the succeeding sections, and the preliminary data collected from monitoring during the first winter period are presented. 2

SITE AND SUBSURFACE DESCRIPTION

The project site is a 3x12 m research area in Brandon, Manitoba (49°51'N, 99°58'W). It is situated on the west side of Daly Overpass along the Provincial Trunk Highway

Strain Gauge (SG)

Piezometer (PZ)

Temperature Sensor (TS)

Heaving Pin/Plate (HP)

Snow Rod (SR)

Magnetic Sensor (MHS) Camera

Shape Accel Array (SAA)

TOP VIEW

at 0.5 m sensor interval

CROSS-SECTIONAL VIEW

Figure 1. Instrumentation list and plan (top and cross-sectional view). (PTH) 10. Based on the geotechnical report of the area, which involves borehole logs, cone penetration tests, and pressuremeter tests, the stratigraphy generally consists of clay (till) soil. Index tests have revealed that the moisture content varies from 15% to 25%, with an average specific gravity of 2.69. Atterberg limits tests showed liquid limit values ranging from 32% to 45%, with plasticity indexes from 16 to 26. The soil can be classified as Lean Clay (CL) with a frost susceptibility classification of F3 (medium to high) based on the U.S. Corps of Engineers Frost Design Soil Classification.

3

INSTRUMENTATION

The research test pile is a steel H-pile (HP310x110) with a length of 17.5 m embedded into the ground. The instrument list and layout are shown in Figure 1. The purpose of each instrument is discussed in the succeeding sections. The instrumentation setup was developed to monitor the following parameters: i) adfreeze stress on piles; ii) depth of groundwater table; iii) ground temperatures; iv) frost heave at the surface and at different depths; and v) depth of snow cover.

3.1

Strain Gauges

Strain gauges were attached on the pile shaft to monitor the changes in axial strains on the pile as the soil heaves. Axial strains are converted to axial forces, which are then used to calculate the equivalent adfreeze or tangential heave stresses. A total of 10 strain gauges were installed on the pile shaft. Two strain gauges were installed at each depth for redundancy. Vibrating wire (VW) strain gauges were used as they are significantly more robust compared to other gauges. VW strain gauges have been used in multiple studies to monitor stresses in piles induced by the surrounding soil (Yang et al., 2006; Zhang and Wang, 2007; Budge and Dasenbrock, 2010, 2011; Drbe et al., 2017; Bartz and Blatz, 2022). These strain gauges have been recommended due to their longevity and high survivability rate during pile driving, allowing longer monitoring periods. Figure 2a shows how the VW strain gauge was welded on the pile shaft. A welding blanket was placed over the strain gauge before welding a protective steel angle (3’’x3’’x1/8’’) (Figures 2b and 2c). The welding blanket reduces the risk of burning or melting the strain gauge and cables while welding the protective steel angle. Installing a steel angle over the strain gauge is a protective measure to reduce the potential for damage to the strain gauge during pile driving (Drbe et al., 2017; Bartz and Blatz, 2022). It was proven effective as all strain gauges still function well after driving the pile. A schematic of the cross-section of the pile after the installation of strain gauges and steel angles is shown in Figure 3. The VW strain gauge has a working range of 4000 microstrain (με) and was initially set to experience a slight compression to give it enough space to expand due to tensile uplift forces from adfreeze stresses. 3.2

Piezometers

Vibrating wire piezometers were installed near the pile (Figure 4). The plan was to install two piezometers in a single borehole at different depths. The two piezometers were supposed to monitor the upward flow of groundwater at the frozen fringe. However, only one survived after grouting the borehole. So far, we were able to track the changes in the depth below ground surface of the groundwater table. 3.3

Figure 2. Installation of (a) strain gauge and placement of (b) welding blanket and (c) steel angle as a protective measure.

3’’ x 3’’ x 3/8’’ Steel angle

Cross-sectional area: 0.0164 m2

Surface area per meter: 1.92 m2/m Strain Gauge Steel HP 310x110

Figure 3. Schematic diagram of the cross-section of the hpile after strain gauge and steel angle installation.

Thermistor Strings

Thermistor strings were installed vertically, with the temperature sensors positioned at different depths (Figure 5). A total of two thermistor strings were installed; one is positioned closer to the pile, while the other is farther. Two strings were installed to compare the difference in temperature between the section close and far from the pile. Temperature sensors provided information on changes in ground temperatures with depth. This information was used to determine the frost penetration depth during the winter.

Figure 4. Vibrating Wire Piezometer.

Figure 5. Thermistor Strings

3.4

Shape Accel Array (SAA)

A four-meter Shape Accel Array (SAA) is horizontally installed near the ground surface (0.15 m depth) and oriented radially outward from the pile. The interval length per sensor in the SAA is 0.5 m. The purpose of the SAA is to measure the deformation (heave and settlement) of the soil surface during winter. It is installed radially to monitor the soil deformation with distance from the pile. SAAs have been used to measure ground movements with great success in other research projects (Kurz et al., 2016; De Guzman et al., 2021). Budge and Dasenbrock (2010), for example, specifically used SAAs to measure deformation around driven piles. A survey marker was placed on one end of the SAA. This marker was regularly surveyed to track its vertical displacement and adjust the readings for the other sensors along the SAA accordingly (Figure 6). As a protective measure, the SAA was encased in a 1’’ PVC pipe before burying. Each SAA sensor has a built-in thermistor that provided information on the temperature near the ground surface.

be analyzed from the photos to measure snow cover depth at the time of the photo. Survey points were also attached to the snow rods. This is a redundancy of the SAA for measuring surface heave close to the pile.

Figure 7. Magnetic Sensors

Figure 8. (a) Snow rod and (b) trail camera. 3.7

Figure 6. Shape Accel Array (SAA) enclosed in a PVC pipe with a survey marker. 3.5

Frost Heave Plates and Pins

Complementary survey pins and plates were installed in the surrounding area (Figures 9a and 9b). They were installed at different depths (0.5, 1.0, 1.5, 2.0 m), where their tip serves as a survey point. They serve as a redundancy for the magnetic sensors as they were surveyed regularly to monitor frost heave at different depths.

Magnetic Sensors

A magnetic heave system was installed near the pile. Magnetic sensors were attached around an inclinometer casing at different depths (Figure 7). The position of the magnetic sensors was tracked during winter to monitor frost heave at different depths within the active layer. 3.6

Snow Rods and Trail Camera

Snow rods are simple steel rods with yellow and red reflective stickers attached (Figure 8a). These snow rods were utilized to measure and monitor the depth of snow accumulation/cover on the research site. A trail camera was installed in addition to the snow rods (Figure 8b). The trail camera regularly took photos of the site to monitor changes in snow cover. Reflective sticker marks can easily

Figure 9. Frost Heave (a) plates and (b) pins.

Strain gauges, piezometers, temperature sensors, and Shape Accel Array (SAA) were all connected to a data acquisition system to collect readings at a specific interval. All cables connected from the mentioned instruments were run through a PVC pipe to serve as an additional protective measure before burying. Magnetic sensor reading and general surveying activities were performed at least once a month during winter. 3.9

0

Data Acquisition System Depth below ground surface (m)

3.8

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

1.8

Research Site

A photo of the research site after all the instruments have been installed is shown in Figure 10. The research site was cleared of snow every site visit to maximize the heat loss of the ground and promote maximum frost penetration.

2

Figure 11. Changes in depth below ground surface of groundwater table over time.

0

-10

-5

0

Temperature ( C) 5

10

15

20

25

2

Depth (m)

4

Max frost depth around 1.5 m

6 8

10 12

14

Figure 10. Research site during winter.

7/30/2022 0:00 10/30/2022 0:00 1/21/2023 0:00

16 18

8/30/2022 0:00 11/29/2022 9:00 2/17/2023 0:00

9/30/2022 0:00 12/18/2022 0:00 3/31/2023 9:00

Figure 12. Ground temperatures with depth. PRELIMINARY FIELD MONITORING RESULTS

Presented below are some of the results from the first winter monitoring period (2022 – 2023). Most recent reading included was March 31, 2023. 4.1

Groundwater Depth

The change in the depth below ground surface of the groundwater table over time is plotted in Figure 11. The decrease in depth during seasons before winter matches the instances of significant rainfall, confirming that the piezometer is functioning well. The groundwater table was observed to gradually become deeper during winter. 4.2

Temperature Readings

Figure 12 shows the graph of temperatures at different depths. They are plotted at different times/dates. The trumpet-shaped curves represent the maximum variation or the range of subsurface temperatures observed during the monitoring period. As we get closer to the height of winter, plots start to lean more to the left or toward negative temperatures. From the graph, the maximum frost penetration depth was identified to be around 1.5 m.

Figure 13 shows the air temperature recorded from the weather station near the site, compared with the measured ground temperature closest to the surface, which is 0.15 meters below ground surface (mbgs). The apparent delay of heat loss/extraction or change in temperature on the ground compared to air temperature can be observed as the first negative mean air temperature was recorded on November 2, while the first negative ground temperature was recorded on November 16. This time lag can be amplified if thicker snow cover develops on the site. 40 30

Temperature ( C)

4

20

Air Temperature Ground Temp (0.15 mbgs)

10 0 -10 -20 -30 -40

Figure 13. Comparison of air and ground temperatures.

4.3

Frost Heave

Surface heave measured at different distances from the pile is shown in Figure 14. Based on the most recent reading, the maximum heave ranges from 1.6 to 2.2 cm. The general trend is that surface heave is higher at locations farther from the pile. The existence of the pile and the development of adfreeze bond and stresses at the frozen soil-pile interface provides resistance against the upward movement of the soil layer close to the pile. 29-Nov-22 21-Jan-23

2.4

18-Dec-22 28-Feb-23

4-Jan-23 31-Mar-23

1.8

1.2 0.6 0

-0.6 0.00

away from pile

1.00

2.00

3.00

4.00

Distance from the Pile (m)

5.00

Figure 14. Surface heave at different distances from the pile. Figure 15 shows the measured surface heave in comparison to the heave measured at a depth of 0.5 m. No heaving was measured at deeper locations despite the frost depth penetrating as deep as 1.5 m. Survey data and magnetic sensor readings are still being analyzed to verify and confirm these initial frost heave measurements. 2.5

Frost Heave (cm)

Surface

0.5 mbgs

2 1.5 1

4.5

Adfreeze Forces and Stresses

Adfreeze stresses were determined using the internal axial strains measured from the strain gauges installed on the pile shaft (Johnson and Buska, 1988). First, the internal axial stresses were determined using the axial strains. The axial forces inducing those axial stresses were then calculated. Lastly, the axial force values were utilized to calculate the equivalent adfreeze stresses along specific portions of the pile shaft. It is important to note that the cross-sectional area and surface area utilized in the calculations included the attached steel angle on the H-pile to protect the strain gauges (Figure 3). 160 140

0.5 0

Figure 15. Frost heave at different depths. 4.4

Figure 16. Trail camera photos showing (a) before and (b) after a significant snowfall event.

Snow Accumulation

Figure 16 shows the difference between the trail camera photos taken when there is no snow accumulation and after a significant snowfall. Using the snow rods, the maximum snow cover depth was identified as 20 cm. Snow cover did not accumulate higher than this, as the site was cleared of snow during visits to maximize frost penetration depth in the area.

Axial Forces (kN)

Surface Heave (cm)

3

120 100 80

SG01 (0.5 m) SG02 (1 m) SG03 (1.5 m) SG04 (2.25 m) SG05 (3 m)

60 40 20 0 -20 -40

Figure 17. Axial forces calculated from recorded strains. Figure 17 shows the calculated axial forces based on the strain gauge readings. Temperature corrections were applied to account for the thermal expansion/compression of steel. Positive and negative

force values represent tensile and compressive forces, respectively. The increase in axial forces is apparent during winter. This indicates that the pile is experiencing uplift adfreeze forces as the soil heaves. The first noticeable increase in axial force occurred on November 20, 2022, a few days after the first negative ground temperature was recorded. Peak axial force was measured on February 4, 2023, which coincides with the instance when the coldest ground temperature was recorded. Axial forces start to decrease as the end of March 2023 approaches. Dec. 26, 2022 0

-15

-10

Total Axial Force (kN)

Temperature ( C) -5

0

The temperature, axial force, and tangential stress distribution on the pile with depth at specific times are all plotted in Figure 18. Figures 18a to 18c present information from December 26, 2022. The temperature graph (Figure 18a) shows that frost penetration depth is 1 mbgs. The blue shade in the graphs represents the depth of frost penetration. The total axial force increased within the frost depth and eventually decreased in the unfrozen zone below (Figure 18b). This increase in axial force was caused by the development of adfreeze stresses at the frozen soil-pile interface and soil frost heave within the frost

5

10

-100 0

-50

0

0.00

50

100

Tangential Stress (kPa) 150

200

-100 0

-50

0

50

100

150

69.14 0.5

0.5

1

1

0.5

66.38

1.5 2

1.5

Depth (m)

Depth (m)

Depth (m)

22.99 1

88.45 76.65

2

-12.29 1.5

-9.75

2

62.61

3

3

(a)

3.5

Temperature

Feb. 4, 2023 0

2.5

2.5

2.5

-15

3.5

-5

0

Total Force 3.5

5

10

-100 0

0.5

0.5

1

1

-50

0

0.00

50

100

Uplift Stress

(c)

Total Axial Force (kN)

Temperature ( C) -10

3

30.80

(b)

-22.08

Resisting Stress

Tangential Stress (kPa) 150

200

-100 0

-50

0

50

100

150

103.70 0.5

99.56

1.5 2

1.5

135.62

2

Depth (m)

Depth (m)

Depth (m)

51.73 149.23

1 -14.17 1.5 -13.24

2

116.56 2.5

2.5

3

3

(d)

3.5

Temperature

Mar. 15, 2023 0

-15

-10

2.5

(e)

3.5

0

Total Force

3.5

-30.86

5

10

-100 0

-50

0

0.00

50

100

Uplift Stress

(f)

Total Axial Force (kN)

Temperature ( C) -5

3

72.12

Resisting Stress

Tangential Stress (kPa) 150

200

-100 0

-50

0

50

100

150

67.63 0.5

0.5

1

1

0.5

64.93

1.5 2

1.5

77.11

2

Depth (m)

Depth (m)

Depth (m)

23.54 1

87.54

-10.86 1.5 -10.34

2

62.22 2.5

2.5

3

3

3.5

(g)

Temperature

3.5

2.5

(h)

3

17.39

Total Force 3.5

-31.13

(i)

Uplift Stress Resisting Stress

Figure 18. Temperature, axial force, and tangential stress distribution along the pile shaft with depth at different times: (a-c) Dec. 26, 2022, (d-f) Feb. 4, 2023, and (g-i) Mar. 15, 2023.

depth. The calculated tangential stresses on the pile shaft indicate that adfreeze stresses (uplift) exist within the frost depth, and resisting stresses below (Figure 18c). This trend of axial force and tangential stress in relation to the frost depth was observed most times during winter. A peak adfreeze stress of 103.7 kPa was recorded on February 4, 2023 (Figure 18f). This was calculated within the soil layer closest to the ground surface (0 – 0.5 mbgs). The average adfreeze stress when considering the entire frost depth in the calculation is 77.8 kPa. It is worth noting that during this time, despite the monitored frost depth being at 1.25 mbgs, no increase in axial force (no uplift adfreeze stress) was observed in the soil layer between 1.0 to 1.5 mbgs. A possible reason is that despite the development of adfreeze bonds in the area, the soil layer might not have heaved yet. Additionally, we are evaluating the possibility that this may be related to the calibration and temperature correction factors used for the force and stress calculations. On March 15, 2023, as ground temperature increase, axial forces and adfreeze stresses eventually decrease (Figure 18g to 18i). There was a decrease in axial force and adfreeze stress despite some frost heave still being recorded nearby. This can imply that the soil freezing temperature significantly impacts the magnitude of adfreeze stresses at the frozen soil-pile interface. Soil freezing temperatures may have affected the lateral earth pressure applied on the pile shaft by the frozen soil; which, in turn, affected the magnitude of adfreeze stresses as they are sensitive to the normal pressure applied at the interface (Ladanyi and Theriault, 1990; Aldaeef and Rayhani, 2019). The shear rate and displacement may also play a role in this observed force and stress decrease. 5

SUMMARY AND CONCLUSIONS

An instrumentation program was developed to quantify adfreeze stresses on pile foundations in the field and identify parameters that may characterize their mobilization on piles. After one winter, the installed instruments have reasonably worked well in determining and monitoring adfreeze stress, depth of groundwater table, ground temperatures, frost heave at the surface and at different depths, and snow cover. For the first monitored winter period, a maximum frost penetration depth of 1.5 mbgs was recorded based on the temperature readings. A surface heave of 1.6 to 2.2 cm was measured at different radial distances from the pile. Smaller surface heave was measured closer to the pile as the pile resisted upward displacement. Peak and average adfreeze stress values of 103.7 kPa and 77.8 kPa were calculated, respectively. The peak value is close to the recommended values of the Canadian Foundation Engineering Manual (CFEM) for steel piles, which is 100 kPa. Further analysis is currently being done on the collected field data. Instrumentation monitoring will continue in the succeeding years to get more data and better understand the mobilization of adfreeze stresses on pile foundations.

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ACKNOWLEDGMENT

This research work was funded by Research Manitoba. The authors would like to acknowledge the personnel, technicians, and engineers from Manitoba Transportation and Infrastructure (MTI), TREK Geotechnical Inc., Green Infrastructure Partners Inc. (GIPI), and RST Instruments Ltd. for their assistance and expertise in the field instrumentation activities. The services of Kerry Lynch, Liam Soufi, Sohit Garg, and Marvin Malonzo on all the fieldwork done are also highly appreciated. 7

REFERENCES

Aldaeef, A. A. and Rayhani, M. T. 2019. Interface shear strength characteristics of steel piles in frozen clay under varying exposure temperature, Soils and Foundations. Japanese Geotechnical Society, 59(6): 2110–2124. Bartz, J. R. and Blatz, J. A. 2022. Considerations for measuring residual stresses in driven piles with vibrating wire strain gauges, Canadian Geotechnical Journal, 59(3): 441–446. Budge, A. S. and Dasenbrock, D. D. 2011. Performance Data Collected from Instrumentation on a Mn/DOT Bridge Abutment Foundation Subject to Downdrag, Geo-Frontiers 2011, ASCE, Reston, VA, USA, 273– 282. Budge, A. S. and Dasenbrock, D. E. 2010. Installation of Downdrag Instrumentation on a Bridge Abutment Foundation: Lessons Learned, GeoFlorida 2010, ASCE, Reston, VA, USA, 1237–1245. Drbe, O. et al. 2017. Instrumentation Monitoring Program to Measure the Magnitude, Distribution and Time Dependency of Drag Load on Abutment Piles: A Case Study, GeoOttawa2017, CGS, Ottawa, ON, Canada. De Guzman, E. M. B. et al. 2021. Performance of highway embankments in the Arctic constructed under winter conditions, Canadian Geotechnical Journal, 58(5): 722–736. Johnson, J. B. and Buska, J. S. 1988. Measurement of frost heave forces on H-piles and pipe piles, CRREL-88-21, U.S. Army Cold Regions Research and Engineering Laboratory. Kurz, D. et al. 2016. Seasonal deformations under a road embankment on degrading permafrost in Northern Canada, Environmental Geotechnics, 7(3): 163–174. Ladanyi, B. and Theriault, A. 1990. A Study of Some Factors Affecting the Adfreeze Bond of Piles in Permafrost, Geotechnical Engineering Congress GSP, 213–224. Yang, J. et al. 2006. Observed Performance of Long Steel H-Piles Jacked into Sandy Soils, Journal of Geotechnical and Geoenvironmental Engineering, 132(1): 24–35. Zhang, L. M. and Wang, H. 2007. Development of Residual Forces in Long Driven Piles in Weathered Soils, Journal of Geotechnical and Geoenvironmental Engineering, 133(10): 1216–1228.

Use of CFA piles in Southern Ontario – review and case studies Laifa Cao WSP, Toronto, Ontario, Canada

ABSTRACT High-capacity continuous flight auger (CFA) piles have been successfully applied to support building foundations in Southern Ontario. The paper reviews the design methods for CFA piles and summarizes results of pile load tests published in the literature. Two compressive load tests conducted in Markham are presented. The ultimate resistance of CFA piles estimated from semi-empirical analysis are compared with that measured from the pile load tests. Methods used to estimate the factored resistances of CFA piles at the ultimate limit states (ULS) and serviceability limit states (SLS) are recommended. RÉSUMÉ Des pieux à tarière à vol continu (CFA) de grande capacité ont été appliqués avec succès pour soutenir les fondations de bâtiments dans le sud de l'Ontario. L'article passe en revue les méthodes de conception des pieux CFA et résume les résultats des essais de charge de pieux publiés dans la littérature. Deux essais de charge en compression effectués à Markham sont présentés. La résistance ultime des pieux CFA estimée à partir d'une analyse semi-empirique est comparée à celle mesurée à partir des essais de charge de pieux. Les méthodes utilisées pour estimer les résistances pondérées des pieux CFA aux états limites ultimes (ELU) et aux états limites de service (ELS) sont recommandées. 1

INTRODUCTION

The need for pile foundations results from soft or loose soil conditions at shallow depth that would result in unacceptable settlement or inadequate bearing resistance from shallow foundations. The pile size and length are designed based on the ultimate limit states (ULS, which considers the load resistance) and serviceability limit states (SLS, which considers the deformations or settlements), and the selection of the pile type is usually based on subsurface conditions as well as local experience and practice. When the site is accessible, removal and disposal of spoils material generated from pile installation is allowable, and strict quality control and supervision are available, continuous flight auger (CFA) piles can be considered due to several advantages such as the relatively low cost, faster installation, higher load capacity, and lower levels of vibration and noise. High-capacity CFA piles, which have been successfully installed in Southern Ontario, could not be designed using conventional methods. The paper reviews the design methods for CFA piles and summarizes results of pile load tests published in the literature. Two compressive load tests conducted in Markham are presented. The ultimate resistances of CFA piles estimated from semi-empirical analysis are compared with that measured from the pile load tests. Methods used to estimate the factored resistances of CFA piles at the ultimate limit states (ULS) and serviceability limit states (SLS) are recommended.

2

REVIEW OF CFA PILES IN SOUTHERN ONTARIO

CFA piles are cast-in-situ concrete piles. During installation, a hollow-stem continuous flight auger is rotated and pushed into the ground at controlled speeds to the target depth and then concrete/grout is continuously pumped under a positive pressure through the hollow stem of the auger to fill the borehole from auger toe during auger retrieval. A reinforcing cage is lowered by its self-weight into the fresh concrete/grout to the designed depth once auger retrieval. With availability of self-compacting concrete and high strength grout, development of powerful CFA drill rigs, application of data acquisition system, 500 to 610 mm diameter CFA piles installed 9 to 29 m below pile cap level have been successfully used as deep foundations to support high-rise buildings in Southern Ontario. 2.1

CFA Pile Resistance

For the design of CFA piles, the geotechnical pile ultimate resistance or capacity (Ru) is usually estimated by summing the shaft friction and end bearing capacity as follows: Ru = qsAs + qtAt - Wp

[1]

where qs is unit shaft friction, As is pile embedded shaft area, qt is end bearing capacity, At is pile toe area, and Wp is pile weight. The qs value may be estimated from effective in-situ vertical stress (σ’v) times shaft resistance factor (β). The β value is related to the soil type and strength, ranging from 0.25 to 1.2 (FHWA, 2007). For cohesive soil, qs may be

estimated from the undrained shear strength (su) times adhesion coefficient (α). The α value is 0.55su for su ≤ 150 kPa and varies linearly from 0.55su to 0.45su for su values varying from 150 kPa to 250 kPa (FHWA, 2007). The qs value may be equal to 57.3N60 for N60 ≤ 75 and 4300 kPa for N60 > 75 for cohesionless soils, where N60 is is the standard penetration test (SPT) N-value at 60% hammer efficiency. For cohesive soil, the qb value might be estimated from su times the bearing capacity factor (Nc) values. The Nc ranges from 6.5 to 9, increasing with su values varying from 25 kPa to 200 kPa (FHWA, 2007). The qs and qb values might also be estimated from SPT N vales using Decourt’s method (1995) recommended in CGS (2006) as follows: qs = α(2.8N60 +10 ) (kPa)

[2]

qb = KbNb

[3]

where α is equal to 1 for clay and 0.5 to 0.6 for granular soil, N60 is the average SPT N-value at 60% hammer efficiency along pile shaft, Nb is the average SPT-N value at 60% hammer efficiency in the vicinity of the pile toe, and Kb is 165 for sand, 115 for sandy silt, 100 for clayey silt and 80 for clay. The CFA pile resistance should be confirmed by static load tests due to uncertainty in the soil parameters used to estimate the pile resistance. The static load test procedure and set-up for CFA pile generally follow the test methods recommended by ASTM (2020). The acceptance criterion for CFA pile static load test is that the geotechnical ultimate limit state is defined as the load resistance at a displacement equal to 5% of the pile diameter (FHWA, 2007), considering that the skin friction capacity is fully mobilized with relatively small pile settlement, typically 5 to 10 mm and the settlement required to fully mobilize toe capacity can be assumed to be approximately 5% of the pile diameter (Reese and O’Neil, 1988; AASHTO, 2006). Davisson criterion (or offset limit load) commonly used for driven pile, which is sometimes underestimate the ultimate resistance, is not appropriate for CFA piles. It should be noted that the pile resistance at the geotechnical ultimate limit state in FHWA (2007) is not considered as the factored pile resistance at the ULS. A resistance factor of 0.6 should be used for a factored ULS resistance if a pile load test is conducted as recommended in CGS (2006). The creep criterion recommended by ASTM (2020) is generally applied for CFA piles, which requires maintaining the maximum test load for 12 hours until the axial movement measured over a period of 1 hour does not exceed 0.25 mm. 2.2

Published Static Pile Load Tests

Zhu and Peaker (2018) presented a case study using CFA piles to support a multi-storey residential building with one level of basement approximately 3 m below ground surface in Markham. The soils below the basement level consisted of a 6 m thick layer of firm (standard penetration test, SPT N-value ranging from 3 to 6 and undrained shear strength ranging from 24 to 52 kPa) silty clay deposits overlying dense to very dense (SPT N-value > 30) sandy silty to silty

sand deposits. The groundwater table was at approximately 3 m below the existing grade. A 510 mm diameter CPA pile with 35 MPa grout (1 part of Portland cement, 2 parts of sand, and 2 parts of water) was installed 12.7 m below the basement level including 6.7 m into the dense to very dense sandy silty to silty sand deposits and tested to a maximum load of 3800 kN with a settlement at pile top of 11.6 mm. The creep settlement of the pile under the maximum test load was 3.2 mm over a period of 18 hours. The settlement at the maximum test load is less than 26 mm, i.e. 5% of the pile diameter (FHWA, 2007) and thus test pile was not considered failure under the maximum load. Almeida, et al. (2021) presented seven load test results on sever CFA piles at four sites as follows: (1) At a 11-sotorey building site in Markham, the subsurface consists of 3.6 to 6.6 m thick fill and very soft to stiff silty clay till over very loose to very dense silty sand till. 500 mm diameter CFA piles were installed 18 to 20 m below grade to support the building. Two static load tests were conducted on 17 m long CFA piles to maximum test loads of 6000 and 7250 kN, respectively with settlement at the pile top of 36 and 20 mm. The test pile with a settlement of 36 mm was considered as failure. This pile settled 25 mm (5% of the diameter) at the test load of 5000 kN which was considered as the ultimate resistance. (2) At a 29-storey building site in Toronto, 500 mm diameter CFA piles were installed to shale bedrock to support the podium foundations. The subface consists of 0.7 to 6.8 m thick very soft to firm/loose fill over stiff to hard silty clay underlain by shale bedrock. Static load test on a 15.2 m long CFA pile was tested to a maximum load of 3000 kN, at which the settlement at pile top was 9.2 mm. (3) At a 21-storey building site in Toronto, a firm to hard (SPT N-value of 5 to 36) clayey silt extended to a depth of 11 m below the ground surface and underlain by a dense to vey dense (SPT N-value of 33 to greater than 50) sand till with occasionally interbeds of loose to compact (SPT N-value of 6 to 19) sand. The groundwater level was near the ground surface. Static load test was conducted on 600 mm diameter CPA piles installed 21 to 29 m below ground surface, respectively. A 6 m debonded casing was installed in the upper section for the 21 m long to account for the effective pile length of 15m. The maximum test load was 6000 kN for the two load tests and the settlement at the pile top was 28 and 21 mm, respectively for the 15 and 21 m long CFA piles. The settlement at the maximum test load included 4.5 mm and 2.5 mm creep movement from 1 to 60-minute maintenance for the 21 and 29 m long CFA piles, respectively. (4) At Barrier site, the subsurface soils consist of 8 m thick loose to compact sand over dense to vey dense sand. 500 mm diameter CFA piles were installed into the dese to very dense sand to a 10sorey building. Two static load tests were conducted on 9 and 15 m long CFA piles to a maximum test load of 4500 kN, at which the

settlement at the pile top was 37 and 20 mm, respectively for the 9 and 15 m long CFA piles. The 9 m long pile settled 25 mm (5% of the diameter) at the load of 3900 kN which was considered as the ultimate resistance. Table 1 summarizes the CFA pile resistance based on published results of static load tests on CFA piles (Zhu and Peaker, 2018; Almeida, et al. 2021) as described previously and the cases described in this paper. The factored resistances at the ultimate limit state (ULS) using a resistance factor of 0.6 as recommended in CGS (2006) are also shown in Table 1. Table 1. CFA Pile Resistances in South Ontario Location

L (m)

D (m)

Markham

12.71 17.02 17.02 27.53 22.03 15.22 15.02 29.02 92 152

510 500 500 610 610 500 600 600 500 500

Toronto2 Barrie

TL (KN) STL (mm) 3800 14.8 6000 36.0 7250 22.0 7200 12.4 5000 38.8 3000 9.2 6000 28.0 6000 21.0 4500 37.0 4500 20.0

R (kN) >2280 3000 >4350 >4320 3000 >1800 3600 >3600 2340 >2700

SR (mm) 4.8°C suggesting that minor

Figure 8: Changes in pore pressure and small-strain stiffness / shear modulus with time during depressurization. 3.3.3

Evolution of stiffness with hydrate saturation

Figure 9 presents the reduction in stiffness as a function of hydrate saturation for specimens dissociated through Thermal Stimulation (HBS-TI-10°C) and Depressurization (HBS-DI-3.0MPa). During thermal stimulation, the shear modulus reduced to that of the host sand after less than 30% of the hydrate volume had been lost. In contrast, when

6

depressurization is used, the specimen exhibits a higher stiffness (>4.0 GPa) after the same amount of hydrate is dissociated .

in peak shear strength although the pressure-temperature conditions are within the bulk hydrate stability region. The peak stress reduces from ~5.9 MPa to ~4 MPa and occurs at a slightly higher axial strain (Ԑa(peak)~ 0.8%) compared to the ~0.4% for HBS sheared after complete hydrate formation. For only a minor loss of hydrate (Hdis = 4%), a ~30% reduction in strength occurs, which is comparable with the loss in stiffness (Figure 7).

Figure 9: Changes in small-strain stiffness with hydrate saturation during dissociation for HBS; Thermal stimulation (red circles) and Depressurization (green diamond shapes) 3.4

Triaxial Shear Strength Tests

As highlighted in Section 3.3.1, the formation of hydrate within coarse-grained soil using the “excess-gas” method leads to significant increase in small-strain stiffness (Gmax) while dissociation by depressurization or thermal stimulation causes a rapid reduction in Gmax. To consider the impact of these conditions on the shear strength of sand specimens, triaxial shear tests were conducted on specimens before and after hydrate formation, as well as after defined changes in hydrate saturation due to hydrate dissociation. 3.4.1

Tests on HBS after Thermal dissociation

Figures 10 highlights the stress-strain response of a sand before and after hydrate formation, as well as after subsequent dissociation of hydrate through thermal stimulation. It can be seen that the formation of hydrates within the specimen (solid-blue line) leads to a significant increase in peak deviatoric stress when compared to the base sand (Figure 10a, solid black line). In addition, this peak stress (shear strength) occurs at axial strains of ~0.5%, after which a large degree of strain softening occurs. The onset of strain softening coincides with rapid dilation (Figure 10b), with the specimen reaching failure (local radial LVDT reaching its limit) at around 1.3% axial strain. In contrast, the base sand exhibits more strain hardening behavior with a minor peak shear stress identified at an axial strain of ~ 5% (Figure 10a) with significant compressional volumetric strain (Figure 10b) occurring before dilation which occurs well before the peak stress. This behavior is similar to the stressstrain response obtained from many studies on HBS (Priest and Hayley, 2019). Figure 10 also presents the stress-strain behaviour for HBS specimens subjected to different degrees of thermal induced hydrate dissociation. Raising the temperature of an HBS specimen to 7C leads to a reduction

Figure 10. (a) Stress-strain response of HBS specimens after thermal simulation to different temperatures: a) Deviatoric Stress vs. Axial Strain, (b) Volumetric strain vs Axial Strain. Increasing temperatures results in greater hydrate dissociation with a corresponding loss in strength. Reduced peak deviatoric stress occurs at increasingly higher axial strains for higher degrees of hydrate dissociation (i.e.; 𝐻𝑑𝑖𝑠 = 55%, Ԑa(peak)~ 2%, & 𝐻𝑑𝑖𝑠 = 76%, Ԑa(peak)~ 4.5%), with increasing volumetric compression occurring before the onset of dilation (Figure 10b). It is interesting to note that as temperature increases the reduction in stiffness (Figure 7) becomes much greater than strength (Figure 10a), although the strength does approach the behaviour of the base sand with increasing degree of hydrate dissociation. 3.4.2

HBS after dissociation by depressurization

Figure 11 (a) and (b) present the stress-strain behaviour of HBS after undergoing hydrate dissociation to different degrees by depressurization, along with the results from the

7

base sand and HBS sand without dissociation for comparison. Similar to the thermal stimulation, the reduction in strength is observed to be higher for HBS specimens that were depressurised to 3.5MPa than 4.0 MPa, and at the same temperature of ~ 2.5°C, all within the hydrate stability region.

sands. At the onset of hydrate formation, a sudden increase in damping ratio along with a rapid uptake of methane gas is indicative of the start of hydrate formation. The small strain stiffness lags the methane consumption and damping, indicating that significant hydrate growth is required before hydrate impacts stiffness. Stiffness continues to evolve throughout the hydrate incubation period, along with the damping ratio gradually reducing even though changes in methane consumption are minor, suggesting a ripening of the hydrate. Increasing specimen temperature to initiate dissociation leads to a reduction in stiffness even when the specimen pressure and temperature are still within the bulk hydrate stability conditions. The reductions in stiffness correspond with a rapid increase in damping, similar to that observed during initial hydrate formation. The higher damping ratios remained when the temperature was kept constant above the hydrate incubation temperature, suggesting that minor hydrate dissociation within the bulk hydrate stability region had occurred, releasing water for enhanced squirt flow. This rise in damping ratio was also observed during depressurization to induce hydrate dissociation even when the specimens remained within the bulk hydrate stability region. Once outside the bulk hydrate stability region, hydrate dissociation was more evident, resulting in a significant increase in pore pressure under constant PVC volume (undrained conditions). Improved understanding of the behavior of HBS during dissociation, both within and outside the hydrate stability boundary, is crucial for accurate predictions of hydrate reservoir behavior during long-term methane production. Future research in this area will contribute to improved modeling and management of hydrate reservoirs. 5

Figure 11. (a) Stress-strain response of HBS specimens after depressurization to different back pressures: a) Deviatoric Stress vs. Axial Strain, (b) Volumetric strain vs Axial Strain. Further reduction in pressure, i.e.; as observed for HBS-DI3.0MPa and HBS-DI-2.5MPa (higher degrees of dissociation) led to lower peak shear strength occurring at increasingly higher axial strains. Minor differences in peak stress, and the axial strains at which these occur, are observed for the specimens undergoing depressurization when compared to the specimens undergoing thermal stimulation (Figure 10). However, these differences may arise due to inherent differences between specimens rather than an effect of the dissociation method. Further studies are required to confirm or authenticate these conclusions. 4

CONCLUSIONS

This paper reports on a series of tests that provide valuable insights into the mechanical behavior of hydratebearing sands (HBS) when subjected to thermal stimulation and depressurization. Similar to previous research findings, the formation of hydrate has a significant influence on small strain stiffness, damping ratio, and the shear strength of hydrate bearing

ACKNOWLEDGEMENT

We would like to thank the University of Calgary, Natural Sciences and Engineering Research Council of Canada (NSERC), the Canadian Research Chairs Program and the IBET PhD Program for their financial support. 6

REFERENCES

Boswell, R., & Collett, T. (2006). The gas hydrates resource pyramid, Fire in the Ice, US Department of Energy, Office of Fossil Energy, National Energy Technology Laboratory, Vol. 6, Issue 3, pp. 5 – 7 Collett, T., S. (1992). Well log evaluation of natural gas hydrates. United States of America (USA) https://doi.org/10.2172/6824342 Hovland, M., & Gudmestad, O.T.(2001). Potential Influence of Gas Hydrates on Seabed Installations. In Natural Gas Hydrates: Occurences, Distribution and Detection (eds C.K.Paull and W.P. Dillon). Hyodo, M. Li, Y., Yoneda, J., Nakata, Y., Yoshimoto, N., & Nishimura, A. (2014). Effects of dissociation on the shear strength and deformation behavior of methane hydratebearing sediments. Journal of Marine and Petroleum Geology, Vol. 51, Elsevier, Amsterdam, Netherlands, pp. 52 – 62. https://doi.org/10.1016/j.marpetgeo.2013.11.015 Hyodo, M., Li, Y., Yoneda, J., Nakata, Y., Yoshimoto, N., Nishimura, A., & Song, Y. (2013). Mechanical behavior of gas-saturated methane hydrate-bearing sediments.

8

Journal of Geophysical Research: Solid Earth, Vol. 118, Issue 10, pp. 5185 – 5194 Kneafsey, T. J., Tomutsa, L., Moridis, G. J., Seol, Y., Freifeld, B. M., Taylor, C. E., & Gupta, A. (2007). Methane hydrate formation and dissociation in a partially saturated corescale sand sample. Journal of Petroleum Science And Engineering, Vol. 56, Issues 1 – 3, pp. 108–126. Minagawa, H., Ohmura, R., Kamata, Y., Nagao, J., Ebinuma, T., Narita, H. & Masuda, Y. (2009). Water permeability of porous media containing methane hydrate as controlled by the methane-hydrate growth process. AAPG Memoir. Vol. 89, pp 734-739. Nixon, M. F., & Grozic, J. L. H. (2007). Submarine slope failure due to gas hydrate dissociation: A preliminary quantification. Canadian Geotechnical Journal, Vol. 44, Issue 3, pp. 314 – 325. Pandey M.R., Priest, J.A., & Hayley, J.L., (2022). Comparison of small-strain stiffness of laboratory synthesized hydrate bearing sands for different formation methods In Proceedings of the 75th Canadian Geotechnical Conference, Calgary, Canada. Paull, C. K., Ussler, I., & Holbrook, W. S. (2011). Tracking the decomposition of submarine permafrost and gas hydrate under the shelf and slope of the Beaufort Sea. In Proceedings of the 7th International Conference on Gas Hydrates (ICGH 2011), Edinburgh, Scotland. Peng, D., & Robinson, D. (1976). New Two-Constant Equation of State. Industrial & Engineering Chemistry Fundamentals. Vol. 15. https://doi.org/10.1021/i160057a011. Priest, J. A., & Hayley, J. L. (2019). Strength of laboratory synthesized hydrate-bearing sands and their relationship to natural hydrate-bearing sediments. Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 12, pp. 12556 – 12575 Priest, J.A.; Hayley, J.L.; Smith,W.E.; Schultheiss, P.; Roberts, J. (2018) PCATS triaxial testing: Geomechanical properties of sediments from pressure cores recovered from the Bay of Bengal during expedition NGHP-02. Marine and Petroleum Geology Journal, Vol. 108, pp. 424–438. Priest, J. A., Rees, E. V. L., & Clayton, C. R. I. (2009). Influence of gas hydrate morphology on the seismic velocities of sands. Journal of Geophysical Research Atmospheres, Vol. 114, Issue B11, pp. B11205/1 – 13 Priest, J. A., Best, A. I., & Clayton, C. R. I. (2005). A laboratory investigation into the seismic velocities of methane gas hydrate-bearing sand. Journal of Geophysical Research: Solid Earth, Vol. 110, Issue B4, pp. B04102/1-13. Sloan, E.D. Jr., Koh, C.A., & Koh, C.A. (2007). Clathrate Hydrates of Natural Gases (3rd ed.). CRC Press. https://doi.org/10.1201/9781420008494 Sultan, N., Foucher, J.P., Cochonat, P., Tonnerre, T., Bourillet, J.F., Ondreas, H., Cauquil, E., & Grauls, D. (2004). Dynamics of gas hydrate: Case of the Congo continental slope, Marine Geology, Volume 206, Issues 1 –4 Sultaniya, A. K., Priest, J. A., & Clayton, C. R. I. (2015). Measurements of the changing wave velocities of sand during the formation and dissociation of disseminated methane hydrate, Journal of Geophysical Research: Solid Earth, Vol. 120, Issue 2, pp. 778 – 789. Sultaniya, A. K., Priest, J. A., & Clayton, C. R. I. (2017). Impact of formation and Dissociation Conditions on

Stiffness of a Hydrate Bearing Sand, Canadian Geotechnical Journal, Vol. 55, pp. 988 – 998. Waite, W. F., Santamarina, J. C., Cortes, D. D., Dugan, B., Espinoza, D. N., Germaine, J., Jang, J., Jung, J. W., Kneafsey, T. J., Shin, H., Soga, K., Winters, W. J., & Yun, T. S. (2009). Physical properties of hydrate-bearing sediments. Reviews of Geophysics, Vol. 47, Issue 4 [RG4003]. Wesbrook, G. K., Thatcher, K. E., Rohling, E. J., Piotrowski, A. M., Pälike, H., Osborne, A. N., Nisbet, E. G., Minshull, T. A., Lanoisellé, M., James, R. H., Hühnerbach, V., Green, D., Fisher, R. E., Crocker, A. J., Chabert, A., Bolton, C., Beszczynska-Möller, A., Berndt, C., & Aquilina, A. (2009). Escape of methane gas from the seabed along the West Spitsbergen continental margin, Journal of Geophysical Research Letters, Vol. 36, Issue 15, pp. L15608/1 – 5 Wright, J.F., Chuvilin, E.M., Dillimore, S.R. Yakushev, V.S. and Nixon, F.M. (1998) Methane hydrate formation and dissociation in fine sands at temperatures near 0 °C, In Proceedings of the 7th International Conference on Permafrost Yellowknife (Canada) Collection Nordicana No. 55. Yamamoto, K., Boswell, R., Collett, T.S., Dallimore, S. R. & Lu, H. (2022). Review of Past Gas Production Attempts from Subsurface Gas Hydrate Deposits and Necessity of Long-Term Production Testing, Energy & Fuels, Vol. 36, Issue 10, pp. 5047 – 5062. https://doi.org/10.1021/acs.energyfuels.1c04119 Yoneda, J., Oshima, M., Kida, M., Kato, A., Konno, Y., Jin, Y., Jang, J., Waite, W. F., Kumar, P., & Tenma, N. (2019c). Permeability variation and anisotropy of gas hydratebearing pressure-core sediments recovered from the Krishna–Godavari basin, offshore India. Journal of Marine and Petroleum Geology, Vol. 108, pp. 524 – 536.

9

Heavy metals and organic contaminants impact on smectite behaviors at a similar double layer thickness Ouhadi, V.R. Civil Eng. Department, Bu-Ali Sina Univ., Hamedan, Iran Yaghoubi, M. Civil Eng. Department, University College of Omran_Toseeh, Hamedan, Iran ABSTRACT Despite extensive research about soil contaminant interaction, there needs to be more research on the geoenvironmental behavior of smectite with different contaminants while the double layer thickness is remained similar. The main objective of this paper is to study the mechano-chemical behavior of smectite samples contaminated with different organic components at an equal double-layer thickness. The experimental study includes a series of unconfined compression tests, Atterberg limits, and XRD tests to address contaminated smectite's microstructural changes and engineering properties. The results suggest that even though, according to the double layer theory, the dielectric constant is the primary factor that controls the soil-organic interaction process, the influence of other characteristics of organic compounds, such as molecule weight is the dominant controlling factor. This indicates some of the significant limitations of the double layer theory for clay-organic contaminant interaction. RÉSUMÉ Malgré des recherches approfondies sur l'interaction des contaminants du sol, il existe peu de recherches sur le comportement géo-environnemental de la smectite dans le processus d'interaction avec différents contaminants alors que l'épaisseur de la couche double est considéré similaire. L'objectif principal de cet article est d'étudier le comportement mécano-chimique d'échantillons de smectite qui sont contaminés par différents composants organiques à une épaisseur de la couche double similaire. L'étude expérimentale considère une série d'essais de compression simple, de limites d'Atterberg, de XRD et d'essais de pH pour traiter les changements microstructuraux et les propriétés mécaniques des échantillons de smectite contaminés. Les résultats suggèrent que même si, selon la théorie de la couche double, la constante diélectrique est la principale facteur qui contrôle le processus d'interaction sol-organique, l'influence d'autres caractéristiques des composés organiques telles que le poids de la molécule est le facteur de contrôle dominant. Cela indique une partie de la limitation majeure de la théorie de la couche double pour l'interaction argile-contaminant organique. 1

INTRODUCTION

Soil contamination is one of the common and controversial environmental issues all over the world (Nazari et al., 2020). The response of soil to the contaminants depends on the type of soil and nature of organic contaminants (Yong, 2000). Leachate of contaminant through soil layers may significantly affect the geotechnical and geo-environmental performance of soil (Sunil et al. 2009; Rosli et al. 2019; Wang et al., 2020). Organic chemicals are produced in different industrial projects such as fuel refining, petrochemical complexes, plastics manufacturing, and detergent industry. The improper use of organic chemicals is another source of environmental contamination (Estabragh et al., 2020). Cohesive soils are commonly used in geotechnical and geo-environmental projects. Therefore, engineers require a fundamental understanding of cohesive soil behaviour as well as qualitative and quantitative prediction methods of their behavior (Kyokawa et al., 2020). Clays are the most important components of cohesive soils, which are classified as fine-grained soils in geotechnical engineering (Yukselen-Aksoy et al., 2008;

Koupai et al., 2020). Solute transport in compacted cohesive and clay liners is receiving particular attention in the contexts of waste storage and the design of materials with physical properties since the interaction of solute and clay particles can significantly affect soil properties (Dabat et al., 2020). In other words, pore fluid chemistry is a significant factor in how soil behaves (Muththalib & Baudet, 2019). Soil pore water quality would affect the properties of soils even for a short period of time and consequently have noticeable impact upon solute and contaminant transport in soil (Oztoprak and Pisirici 2011; Wang et al., 2018; Yan et al., 2018; Shen et al., 2019; Koupai et al., 2020). Among organic contaminant, methanol and dichloromethane are important basic industrial chemicals (Ouhadi et al., 2017; Liu et al., 2020). Methanol is used as raw material or solvent in the production of pesticide, medicine, dye, perfume, coating and synthetic materials. In addition to synthetic ammonia, methanol is the only large-scale chemical synthesized by coal gasification and natural gas reforming (Xia, 2020). In addition, dichloromethane (DCM) is a toxic volatile compound which is found in the ground waters and wastewaters of

the pharmaceutical, chemical, textile, metal-working and petroleum industries (Shestakova and Sillanpää, 2013). To prevent contaminant transport in soils, cohesive clays rich in montmorillonite are used due to their contaminant retention, self-healing properties, and low permeability (Kyokawa et al., 2020). Smectite is widely used as an appropriate material for contaminant retention in clay liners (Ouhadi et al., 2021). It is well known that electrochemical phenomena on the surface of clay mineral crystals considerably affect their macro-structural behavior (Kyokawa et al., 2020) and soil properties are sensitive to preferred orientations of the microscopic plate-like clay particles (Wensrich et al., 2018). The DDL is an ionic phenomenon that describes the variation of electric potential near a charged surface, such as clay (Yong et al., 1992; Koupai et al., 2020). Generally, there are two factors controlling the volume change characteristics of clays, namely shearing resistance and DDL repulsive forces, in which, the DDL repulsive forces play an important role in explaining the compressibility and plasticity behaviour, especially for smectite mineral (Yong and Mulligan, 2004; Thanh Duong & Van Hao, 2020). The amount of physicochemical interaction in soil can be explained by the diffuse double layer theory. The charged surface and the distributed charge in the adjacent phase are together termed the diffuse double layer. The thickness of double layer, 1/K, (equation 1) which is the distance from the surface of a clay particle to the centroid of the area of the diffuse layer, represents the expected soil structure of clay particles due to the soil solute interaction (Mitchell & Soga, 2005). 1 𝐾

=(

𝜀° 𝐷𝑘𝑇 2𝑛° 𝑒 2 𝜈 2

)

0.5

[1]

In the above equation, 𝑛° : concentration of ions, k: Boltzmann constant, T: temperature (K), 𝑒: electronic charge, 𝜀° : static permittivity of the medium, D: dielectric constant, and 𝜈 is the valence of cations of soil pore fluid. The above equation shows that the thickness of double layer varies inversely with the valence and the square root of the concentration. In addition, it is directly varying with the square root of the dielectric constant and temperature, assuming other factors remaining constant. Generally, reduction or shrinkage of the double layer thickness produces a flocculated structure while an increase in its thickness results in a dispersed structure (Ouhadi et al., 2021). Furthermore, interaction of clay minerals with organic chemicals with dielectric constants lower than water will result in the development of thinner interlayer spacing because of the contraction of the soilelectrolyte system (Yong, 2000). This causes a reduction in liquid limit (LL) due to soil contamination (Yong et al., 1992). It is shown that this reduction is dependent on the value of dielectric constant of pore fluid; decrease in dielectric constant causes reduction in the value of liquid limit (Estabragh et al., 2020). Decreasing the dielectric constant and increasing the organic fluid/water ratio decreased the liquid limit and plasticity index (Evangeline & John, 2010; Olgun & Yildiz, 2010). The research studies have shown that the clay soil structure changes to the

aggregated structure due to the soil solute interactions. In such a case the soil behaves like silty–sandy soils when the dielectric constant of the pore fluid is considerably lower than that of water (Kaya & Fang, 2005; Moavenian & Yasrebi, 2008). The addition of heavy metals and salt to pore water can affect the behaviour of the soil by influencing the electrochemical forces exist between the solid, liquid and dissolved phases (Ike, 2020). From mechanical point of view, researches have shown that the unconfined compressive strength increases as the NaCl and CaCl2 salt concentration increase. Furthermore, it is reported that the failure strain increases in NaCl added specimens, thus becoming more ductile (Vanda, 2014). However, the amount of bentonite expansion can be significantly reduced with an increase in the calcium concentration in the clay double layer (Mitchell and Soga, 2005; Lu et al., 2018; Rowe et al., 2019; Chai & Prongmanee, 2020). According to the Gouy-Chapman theory (1910-1913), at specific concentration of organic and salt concentrations the achievement of similar thickness of double layer is possible. However, the question which remains to answer is as follows: If smectite samples with different soil pore fluids have similar thickness of double layer, do they show similar geo-environmental behaviour? It should be emphasized that according to the GouyChapman theory the dielectric constant is the only parameters that governs the clay-organic interaction. In other words, other properties of organic electrolytes such as molecule size, molecule weight, and viscosity are not considered in this theory. According to the above discussion, the main objective of this study is to investigate on the some of the limitations of double layer theory in addressing the process of smectite-different organic contaminants interaction at similar thickness of double layer. For this purposes, impact of two different organic contaminants on the plasticity behaviour, microstructural changes, and unconfined compressive strength of smectite at similar thickness of double layer are investigated and discussed.

2. MATERIALS AND METHODS 2.1. Materials Bentonite soil sample was used in this study which is taken from Iran Barit Company. A part of geotechnical and geo-environmental properties of bentonite sample is shown in Table 1. According to the results of Table 1, sodium ions takes around 61% of exchangeable sites of clay fraction of bentonite. In other words, the initial structure of soil is relatively oriented structure due to the existence of sodium ions as the major exchangeable cation of double layer. In order to address the mechano-chemical behaviour of bentonite at the presence of different organic and salt concentrations three different electrolyte was used. Methanol and dichloromethane was used as organic contaminants. Sodium chloride was used as an alkaline metal to preserve similar double layer thickness of

contaminated smectite samples with methanol and dichloromethane. Table 1. Some of the physical and geo-environmental properties of bentonite sample. Soil Properties Liquid Limit

Quantity 330

Soil Properties CEC (cmol/kg-soil)

Quantity 68.03

Plastic Limit

80

Exchangeable sodium (cmol/kg-soil)

41.93

Plasticity Index

250

Exchangeable calcium (cmol/kg-soil)

24.10

Carbonate Content

12.5%

Exchangeable Magnesium (cmol/kg-soil)

1

pH (in 1:30 soil: water ratio)

9.8

Exchangeable potassium (cmol/kg-soil)

1

Electrical conductivity (µZ/cm)

0.1

Soil Classification

CH

Table 2 shows the general characteristics of water and organic materials of this study. The data for distilled water are presented for comparison purposes. By the use of distilled water and these two organic materials a wide range of dielectric constant from 9.1 to 80.1 is achieved. Table 2. Some of the characteristics of distilled water and organic materials at 20 degrees centigrade (Albright & Gosting, 1946; Wang and Anderko, 2001). Material Name

Dielectric constant

Viscosity (mPa/s)

Distilled water

80.1

1.002

100% Methanol

33.1

30% Methanol

Bulk density (g/cm3)

0.998

Molecule weight (g/mol)

18.02

Molecule diameter (nm)

0.597

0.79

32.04

0.44

72.8

1.539

-

-

-

100% Dichloromethane

9.1

0.43

1.33

85

0.92

30% Dichloromethane

23

-

-

-

-

0.32

2.2. Sample Preparation and Experimental Methods To calculate the thickness of double layer, the equation 1 is used. For calculation of thickness of double layer, the initial electrolyte concentration of soil sample is not taken into account. This is due two reasons. First, the bentonite

sample has very large CEC and low soluble electrolyte concentration. Since for sample preparation the applied electrolyte concentrations were very high, therefore the influence of initial soluble salt concentration of soil upon its double layer behaviour was ignorable. Secondly, since the bentonite sample for all of the experiments was similar, therefore the background of soil samples in terms of the initial concentration of soluble salts was similar. Therefore, the impact of initial concentration of soluble salts for all samples upon thickness of double layer was the same. In other words, the influence of initial interactive forces of clay particles was similar for all samples. In sample preparation process, in order to have different ranges of dielectric constant, as the Table 2 shows, in addition to the pure organic, mixtures of 30% organic materials and 70% of distilled water are used. According to the previous researches (Yong et al., 1992), the plasticity index variations of soil samples are a very significant indicator which shows the extent of interaction of clay-electrolyte systems. For this reason, a series of the Atterberg limits tests were performed on different mixtures of soil samples and electrolyte. For this purpose, soil samples were initially mixed with the calculated electrolyte quantity whish had a specific concentration of organic chemical for similar thickness of double layer. In sample preparation stage, soil suspensions were mixed for two hours on a mechanical shaker in every 24 hours for a period of 96 hours. After this so called equilibrium condition, soil suspensions were dried in 60 degrees centigrade in oven for 24 hours. After drying, soil samples were grinded and passed through sieve number #200. Then, these organic contaminated bentonite samples were exposed to 23 and 72 coml/kgsoil of sodium chloride. Once again, the previously mentioned equilibrium concentration process was applied to soil suspension samples. After another drying process, Atterberg limits tests were performed on soil samples based on ASTM4318-00. To address the behaviour of soil samples from microstructural point of view, a series of XRD experiments were performed upon soil sample. By the use of achieved XRD spectra, the position and intensity for the major basal spacing of minerals are reported. XRD analysis was carried out based on the method proposed in the study stated by other researches (Moore and Reynolds, 1989; Ouhadi and Yong, 2003; Nikolic et al., 2018). For sample preparation, in each case prepared samples were dried, pulverized, and passed through sieve #200. Then, 0.2 g of contaminated dry sample was mixed with distilled water in a 10 ml volumetric flask. After achieving equilibrium condition, 10 drops of soil suspension were poured on a 3x3 cm glass slide by the use of precise pipet. The XRD spectra were obtained after the glass slides were air dried. Those slides were scanned in the 2θ range 4 to 60 degrees by the use of Siemens-Diffractometer D8 Advance with Cu-Kα radiation. The X-pert High Score Plus software based on PDF-2/ICDD (2011 released) was used to evaluate the data. In order to comprehensively investigate the impact of microstructure of smectitecontaminant mixtures, series of scanning electron microscope photographs were prepared. The SEM photographs were taken from prepared cubic

contaminated samples with 1 cm dimension. Samples were covered with thin layer of gold and then the SEM photographs were taken by VEGA//TESCAN-LMU in Tehran Razi Research Center. Finally, to investigate the soil-contaminant from mechanical point of view, a series of unconfined compression test were performed upon different soil samples. Experiments were performed based on ASTM D2166-87. In sample preparation process, dried contaminated bentonite samples were mixed with 30% distilled water. After complete mixing, the prepared soil samples were passed through sieve #10. Then the homogenized sample was compacted into the compression mold of 33 mm diameter and 60 mm height by the use of Harvard Miniature Apparatus in three layers, subjected to 30 blows per layer. By series of try and error, it was found that this condition of soil compaction is required to achieve a dry density of 1.5 g/cm 3. The soil samples were extracted from unconfined compression mold with the use of extractor of Harvard Miniature apparatus. Then, the extracted soil samples were air dried for 7 days. To prevent soil cracking during drying period, samples were dried in a closed container to be dried gradually. Finally the unconfined compressive strength of samples were measured based on ASTM standard (ASTM D 2166-00). 3.

DISCUSSIONS

To investigate the impact of organic electrolyte and alkaline metals upon plasticity behaviour of bentonite at similar thickness of double layer, series of Atterberg limit tests was performed upon bentonite samples at different concentration of different electrolyte. As was previously addressed, sodium chloride, Methanol, and Dichloromethane was used to achieve similar thickness of double layer at different concentrations of these electrolytes. Table 3 shows the sample combinations, sample names and impact of different electrolyte combination upon plasticity behaviour of bentonite samples. Table 3. Atterberg limits of bentonite samples at the presence of organic and alkaline metals contaminants. Sample Description

Sample Name

Sodium concentration (cmol/kg-soil)

Bentonite

B

-

Weight percentage of organic material -

Liquid Limit

Plastic Limit

Plasticity Index

330

80

250

Bentonite+ Sodium

BS-72

72

-

229

82

147

Bentonite+ Methanol

BM

-

30

161

80

81

Bentonite+ Methanol+ Sodium

BMS-72

72

30

176

81

95

Bentonite+ Sodium

BS-23

23

-

250

98

152

Bentonite+ Dichloromethane

BD

-

30

269

102

167

Bentonite+ Dichloromethane+ Sodium

BDS-23

23

30

230

85

145

In Table 3, BMS-72 and BDS-23 samples have similar thickness of double layer. For these two samples the calculated thickness of double layer was 18.5 angstrom. For a comparison purposes, the plasticity index for bentonite samples exposed to each of these electrolyte also is presented in Table 3. For the above mentioned two samples, the bentonite sample was initially exposed to diluted organic electrolytes in accordance to the concentrations presented in Table 3, and then samples were exposed to different sodium chloride concentrations according to the concentration shown in the Table 3 for BMS-72 and BDS-23 samples. Giving to the results of Table 3, all of these electrolytes at their individual concentrations have caused a noticeable decrease in plasticity index of bentonite. For organic contaminants, these effects mainly come from impact of low dielectric constant of electrolyte upon liquid limit of samples, whereas the plastic limits of samples have relatively small variations. According to the results of Table 3, the addition of 30% methanol and 30% dichloromethane has decreased liquid limits 51% and 18% respectively. In other words, these two electrolytes at similar concentration have significant difference upon soil plasticity index. The results show that methanol has more impact on soil structure than that of dichloromethane. This reduction of plasticity index can be attributed to the formation of flocculated structure at the presence of electrolytes with low dielectric constant. Even though methanol has caused more flocculated structure in bentonite, according to Gouy-Chapman theory it is normally expected to have lower plasticity index for mixture of dichloromethane and bentonite due to the smaller dielectric constant of dichloromethane. However, the mixture of bentonite and methanol has shown lower plasticity index at similar electrolyte concentration with dichloromethane. In other words, according to the achieved results of Table 3, for BMS-72 and BDS-23 samples which have similar thickness of double layer, the combination of methanol and sodium has shown more impact upon reduction of plasticity index of bentonite. In fact, while the presence of 30% methanol and 72 cmol/kgsoil of sodium chloride has caused a 62% reduction in bentonite plasticity index, the electrolyte concentration of 30% dichloromethane and 23 cmol/kg-soil of sodium chloride has caused only 42% reduction in bentonite plasticity index. These results are in agreement with results of plasticity index for BM and BD samples. One of the reasons that the above mentioned results are not suited with double layer theory can be attributed to the different viscosity of methanol and dichloromethane. Other researcher have shown that at the presence of ethanol, methanol, and acetic acid at the concentration range of 10% to 40%, the viscosity and dielectric constant have contradictory effects (Ouhadi et al., 2017). In one aspect, a smaller dielectric constant (i.e. in case of dichloromethane) causes much more reduction in double layer thickness which reduces the water adsorption of clay particles. Therefore, a part of retained water behaves as free water; consequently the liquidity behaviour of soil happens in lower water content. However, in another aspect, the organic material with larger molecule weight (i.e. in case of dichloromethane) causes a resistance

against liquidity behaviour of soil, consequently the soil having organic material with larger molecule weight tends to show greater liquid limit. According to the results of Table 2 and Table 3, the second phenomenon controls the liquid limit variations of dichloromethane contaminated bentonite. Commonly, organic contaminants can form hydrogen bonds with clay particles. These are electrostatic or ionic bonds. In general, organic contaminants can interact with clay particles through three different mechanisms: 1) adsorption to the clay surface through hydrogen bonding or exchangeable ions. 2) the adsorption of large molecules by van der Waals forces and entrance between silicate layers. 3) influence of weight of organic molecules in interaction with clay particles (Yong, 2000). In addition, the retention of organic contaminants may happen through the entrance of organic molecules between clay layers or by retention of organic molecules in edges of clay particles (Lagaly, 1981). In acidic conditions the hydroxyl groups have more access to the H+ ions which causes an increase in positive charge of clay particles in their edges. This causes a reduction in repulsive forces. Consequently clay particles get closer to each other. Consequently a reduction in water adsorption happens and the liquid limit reduces (Tajudin, 2016). The initial pH of methanol and dichloromethane are 7.9 and 9.2, respectively. This difference provides a more oriented structure for dichloromethane contaminated smectite prompting larger liquid limit. On the other hand it is reported that the large molecules of organic contaminant cannot easily enter to the clay double layer. Therefore, if every condition remains similar, dichloromethane with large molecule weight in comparison to methanol (around 2.5 times) has lower impact upon double layer. The larger molecule diameter of dichloromethane (2 times in comparison with methanol diameter) contributes to this phenomenon as well. Therefore in spite of similar thickness of double layer which achieves from calculation, ethanol has more impact upon smectite double layer. This could be the main reason for achieving lower liquid limit and plasticity index for bentonite sample containing methanol in comparison to mixture of bentonite and dichloromethane, assuming similar electrolyte consideration and despite of lower dielectric constant of dichloromethane. To prove this discussion, scanning electron microscope photograph was taken from methanol contaminated bentonite and dichloromethane contaminated bentonite at similar concentration (Figure 1) and at similar thickness of double layer (Figure 2). As the SEM photographs of BM and BD samples show, in spite of lower dielectric constant for BD sample in comparison to BM sample, the BD sample shows a more oriented structure which verifies low interaction of dichloromethane with smectite particles. Furthermore, Figure 2 presents the SEM photographs of mixture of bentonite with 30% dichloromethane and 23 cmol/kg-soil of sodium chloride (i.e. BDS-23 sample) and mixture of bentonite with 30% methanol and 72 cmol/kg-soil of sodium chloride (i.e. BMS-72 sample). Even though in these two samples the thicknesses of double layer are equal, the sample that contains methanol shows a more flocculated structure.

This over again proves that molecules of ethanol have shown more interaction with montmorillonite particles.

Figure 1. The SEM photograph of BM (Top) and BD (Bottom) samples. To make more precise investigation on this interaction process, the x-ray diffraction spectrum of BMS-72 and BDS-23 samples were examined. Figures 3 and 4 show the XRD results of BDS-23 and BMS-72 and samples, respectively. According to the results of Figures 3 and 4, the interaction of smectite and pore fluid electrolyte has caused a noticeable decrease in the peak intensity of smectite basal spacing. In other words, the reduction in the thickness of double layer causes a reduction on the repulsive forces of clay particles. Consequently, clay particles get closer to each other and a face to edge and edge to edge structure forms. In BDS-23 sample (Figure 3), since a flocculated structure shows less reflection of XRD radiation, therefore, around 63% reduction of the intensity of major basal spacing of montmorillonite is observed.

2000 Bentonite Bentonite + 30% Dichlromethane + 23 cmol/kg-soil NaCl

Peak Intensity (CpS)

1600

1200

800

400

0

4

14

24

34 2-Theta-Scale

44

54

Figure 3. The XRD diffraction spectrum of BDS-23 sample. 2000

Bentonite

1800 1600 Peak Intensity (CpS)

1400 1200

1000 800 600 400 200 0

4

14

24

34 2-Theta-Scale

44

54

Figure 4. The XRD diffraction spectrum of BMS-72 sample.

Figure 2. The SEM photograph of BMS-72 (Top) and BDS-23 (Bottom) samples. However when the x-ray diffraction spectrum of BMS72 sample was measured (Figure 4), in spite of similar thickness of double layer, much more reduction for the intensity of basal spacing of montmorillonite (83%) was observed. This again supports the above mentioned points in which the sample which contains methanol shows more flocculated structure. In order to investigate the impact of the above mentioned microstructural changes between those two samples upon the mechanical behaviour of them, series of unconfined compression tests were performed. Table 4 presents the results of unconfined compression tests of bentonite and mixtures of bentonite and different electrolytes which are used in this study.

As the results of Table 4 shows the bentonite and mixture of bentonite and dichloromethane has shown relatively similar strength (0.5 and 0.6 MPa, respectively). On the other hand a mixture of bentonite and methanol has shown much larger strength (i.e. 1.14 MPa). Furthermore, for BDS-23 sample the achieved strength is 0.79 MPa while the measured strength for BMS-23 sample is 1.06 MPa. In one aspect since the dichloromethane has lower dielectric constant than that of methanol sample, according to the double layer theory the thickness of double layer is smaller for BD sample in comparison with BM sample. In fact, due to the lower dielectric constant of dichloromethane in comparison to dielectric constant of methanol, one expects to achieve a lower cohesion and higher internal friction angle for BD sample. However, the SEM photographs (Figure 1) showed a more oriented structure for BD sample in comparison with BM sample. This proves the above mentioned discussion that in spite of lower dielectric constant for dichloromethane, the more oriented soil

structure of BD sample can be attributed to the role of larger molecules of dichloromethane which have not been able to enter to the diffuse double layer of smectite particles as much as molecules of methanol. Table 4. Unconfined compressive strength of bentonite and contaminated bentonite with different electrolytes. Sample Description

Sample Name

Sodium concentration (cmol/kg-soil)

Weight% of organic material

Unconfined compressive strength (MPa)

Bentonite

B

-

Bentonite+ Sodium

BS-72

72

-

0.70

Bentonite+ Methanol

BM

-

30

1.14

Bentonite+ Methanol+ Sodium

BMS-72

72

30

1.06

Bentonite+ Sodium

BS-23

23

-

0.67

Bentonite+ Dichloromethane

BD

-

30

0.60

Bentonite+ Dichloromethane+ Sodium

BDS-23

23

30

0.79

-

0.50

According to the results of Table 4, the larger unconfined compressive strength of BMS-72 sample in comparison with BDS-23 sample (1.06 MPa and 0.79 MPa, respectively), proves that in spite of similar thickness of double layer for these two samples, due to the much more reduction in repulsive forces and achievement of larger internal friction angle for BMS-23 sample, a larger unconfined compressive strength is achieved. 4.

CONCLUDING REMARKS

The following conclusions can be addressed from the experimental results of this study: The results of Atterberg limit tests, SEM, XRD, and unconfined compressive strength of organic contaminated bentonite samples are in highly regarded agreement which show a correlation between micro-structural and macro-structural response of organic contaminated bentonite. According to the diffuse double layer theory the organic contaminant with lower dielectric constant should have more impact upon achieving a flocculated structure. However, the results of this study show that the methanol contaminated bentonite with larger dielectric constant than dichloromethane appears a more flocculated structure. This conflicting result between expectation from double layer theory and experimental study can be attributed to the larger molecule weight and diameter of dichloromethane which cause lower interaction between electrolyte and montmorillonite particles. Even though the double layer theory significantly helps to evaluate the soil-contaminant interaction, impact of different organic contaminants on smectite behaviors at

similar double layer thickness proves that the dielectric constant cannot entirely interpret the process of clay – organic contaminant interaction. From the results of this study in spite of lower dielectric constant for dichloromethane contaminated bentonite (BD) than that of methanol contaminated bentonite (BM), the BD sample shows a more oriented structure which verifies the lower interaction of dichloromethane with smectite particles. 5.

References

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Mitchell, J. K. & Soga, K. 2005. Fundamentals of soil behavior, New York: John Wiley & Sons. Moavenian, M. H. and Yasrobi, S. S. 2008. Volume change behavior of compacted clay due to organic liquids as permeant. Applied Clay Science, 39(1-2), 60-71. Moore, D. M. and Reynolds, R. C. 1989. X-ray Diffraction and the Identification and Analysis of Clay Minerals, 332: Oxford University Press New York. Muththalib, A. and Baudet, B. A. 2019. Effect of heavy metal contamination on the plasticity of kaolinbentonite clay mixtures and an illite-smectite rich natural clay. In E3S Web of Conferences, Vol. 92, p. 10005. EDP Sciences. Nazari Heris, M. Aghajani, S. Hajialilue-Bonab, M. and Vafaei Molamahmood, H. 2020. Effects of Lead and Gasoline Contamination on Geotechnical Properties of Clayey Soils. Soil and Sediment Contamination: An International Journal, 29(3), 340-354. Nikolic, V. Komljenović, M. Džunuzović, N. Miladinović, Z. 2018. The influence of Pb addition on the properties of fly ash-based geopolymers. Journal of Hazardous Materials 350, 98-107. Olgun, M. and Yıldız, M. 2010. Effect of organic fluids on the geotechnical behavior of a highly plastic clayey soil. Applied Clay Science, 48(4), 615-621. Ouhadi, V.R. Fakhimjoo, M.S. and Omid Naeini, S.T. 2017. The Comparison of Plastic and Permeability Behavior of Bentonite in the Presence of Organic and Heavy Metal Contaminants. Journal of Civil and Environmental Engineering Volume 46, Issue 4, pp. 25-36. Ouhadi, V. & Yong, R. 2003. Experimental and theoretical evaluation of impact of clay microstructure on the quantitative mineral evaluation by XRD analysis. Applied Clay Science, 23(1-4), 141-148. Ouhadi, V. R. Yong, R. N. and Deiranlou, M. 2021. Enhancement of Cement‐Based Solidification/Stabilization cement-based solidification/stabilization of a lead-contaminated smectite clay. Journal of Hazardous Materials, 403, 123969. Oztoprak, S. and Pisirici, B. 2011. Effects of micro structure changes on the macro behaviour of Istanbul (Turkey) clays exposed to landfill leachate. Engineering Geology, 121(3-4), 110-122. Rosli, R. N. Selamat, M. R. and Ramli, M. H. 2019. Shear strength and permeability properties of lateritic soils from northwest Malaysia due to extended compaction. Materials Today: Proceedings, 17, 630639. Rowe, R. K. Garcia, J. D. D. Brachman, R. W. I. & Hosney, M. S. 2019. Chemical interaction and hydraulic performance of geo-synthetic clay liners isothermally hydrated from silty sand subgrade. Geotextiles and Geo-membranes, 47(6), 740-754. Shen, P. Tang, H. Huang, L. & Wang, D. 2019. Experimental study of slaking properties of red-bed mudstones from the Three Gorges Reservoir area. Marine Geo-resources & Geo-technology, 37(8), 891-901.

Shestakova, M. and Sillanpää, M. 2013. Removal of dichloromethane from ground and wastewater: A review, Chemosphere, Vol. 93, Issue 3, pp. 12581267. Sunil, B. M. Shrihari, S. and Nayak, S. 2009. Shear strength characteristics and chemical characteristics of leachate-contaminated lateritic soil. Engineering Geology, 106(1-2), 20-25. Tajudin, S. A. A. Azmi, M. A. M. Shahidan, S. Abidin, M. H. Z. and Madun, A. 2016. Relationship of physical parameters in Pb-contaminated by stabilization/solidification method. MATEC Web of Conferences. Thanh Duong, N. and Van Hao, D. 2020. Consolidation Characteristics of Artificially Structured KaolinBentonite Mixtures with Different Pore Fluids. Advances in Civil Engineering. Vanda, Y. 2014. Effect of Pore Water Chemistry on Hydro-Mechanical Behavior of Compacted Expansive Clay. Eastern Mediterranean University (EMU)-Doğu Akdeniz Üniversitesi (DAÜ). Wang, P. and Anderko, A. 2001. Computation of dielectric constants of solvent mixtures and electrolyte solutions. Fluid Phase Equilibria, 186(1-2), 103-122. Wang, X. Huang, L. Yan, C. & Lian, B. 2018. HKCV rheological constitutive model of mudstone under dry and saturated conditions. Advances in Civil Engineering. Wang, Q. Chen, J. Liu, J. Yu, M. Geng, W. Wang, P. and Wu, Z. 2020. Relationships between shear strength parameters and microstructure of alkalinecontaminated red clay. Environmental Science and Pollution Research, 1-15. Wensrich, C. Pineda, J. Luzin, V. Suwal, L. Kisi, E. and Allameh-Haery, H. 2018. Deformation and Fabric in Compacted Clay Soils. Physical Review Applied, 9(5), 054003. Xia, W. 2020. Comparison of Several Methods for Industrial Methanol Production. Journal of New Energy Developments, 1(1), 12-15. Yan, C. Xu, X. & Huang, L. 2018. Identifying the impact factors of the dynamic strength of mudded intercalations during cyclic loading. Advances in Civil Engineering Yong, R. N. 2000. Geoenvironmental engineering: Contaminated soils, pollutant fate, and mitigation: CRC press. Yong, R. N. Mohamed, A. M. O. Warkentin, B. P. Principles of Contaminant Transport in Soils, Elsevier, 1992,. Yong, R.N. and Mulligan, C. 2004. Natural Attenuation of Contaminants in Soils, Lewis Publishers, CRC Press, Florida, 319 p. Yukselen-Aksoy, Y. Kaya, A. and Ören, A. H. 2008. Seawater effect on consistency limits and compressibility characteristics of clays. Engineering Geology, 102(1-2), 54-61.

Ageing effects on consolidation behaviour of soft soils Arazooben Patel, Narges Gheisari & Paul H. Simms Department of Civil and Environmental Engineering, Carleton University, Ottawa, Ontario, Canada ABSTRACT The management of young materials such as dredged sediments or clayey tailings is dominated by their consolidation properties, which govern the magnitude and rate of settlement of such soft deposits. Conventionally, the consolidation behaviour of these materials is designed employing large strain consolidation theory. Due to prolong consolidation time associated with young materials, however, the time-dependent effects such as creep and structuration (ageing) may substantively affect the consolidation behaviour. Ageing, in particular, by increasing the pre-consolidation, can potentially decrease the time required before reclamation can start but can also reduce the average residual shear strength of the deposit. The ageing process, however, is not well understood, nor easy to predict from any kind of standard geotechnical measurements. This study aims to understand the ageing effects and develop correlations to estimate the magnitude and rate of ageing expected under different deposition scenarios. Subsequently, the experimental findings are incorporated into a large strain consolidation analysis to account for the time-dependent effects. RÉSUMÉ La gestion des matériaux jeunes tels que les sédiments de dragage ou les résidus argileux est dominée par leurs propriétés de consolidation, qui régissent l'ampleur et la vitesse de tassement de ces dépôts mous. Classiquement, le comportement de consolidation de ces matériaux est conçu en utilisant la théorie de la consolidation des grandes déformations. Cependant, en raison du temps de consolidation prolongé associé aux matériaux jeunes, les effets dépendant du temps tels que le fluage et la structuration peuvent affecter considérablement le comportement de consolidation. Le vieillissement, en particulier, en augmentant la pré-consolidation, peut potentiellement diminuer le temps nécessaire avant que la remise en état puisse commencer, mais peut également réduire la résistance résiduelle moyenne au cisaillement du dépôt. Le processus de vieillissement, cependant, n'est pas bien compris, ni facile à prédire à partir de n'importe quel type de mesures géotechniques standard. Cette étude vise à comprendre les effets du vieillissement et à développer des corrélations pour estimer l'ampleur et le taux de vieillissement attendus dans différents scénarios de dépôt. Par la suite, les résultats expérimentaux sont incorporés dans une analyse de consolidation des grandes déformations pour tenir compte des effets dépendant du temps. 1

INTRODUCTION

In the thick deposits of remoulded or dredged soils and clayey mine tailings, their low hydraulic conductivity can lead to consolidation times from years to decades for practical deposit depths. In such circumstances the consolidation of young material may be substantially affected by time-dependent effects such as creep (Watabe et al., 2008 & Jeeravipoolvarn et al., 2009) and ageing. The ageing phenomenon can induce an increase in peak strength while reducing compressibility. The conventional practice designs deposition plans of young material employing large strain-consolidation theory, which assumes a constant compressibility function. Therefore, the influence of ageing on the consolidation process could be substantial and bear on efforts to predict settlement and strength development. Ageing is a phenomenon that occurs in a wide variety of materials ranging from rocks to ideal colloidal suspensions. It has also been extensively studied in natural soils by researchers including Skempton and Northey (1952), Leonard and Altschaeffl (1964), Schmertman (1991), Mitchell and Soga (2005) as well as for different kinds of tailings and dredged sediments by Banas (1991), Suthaker and Scott (1997); Jeeravipoolvarn (2005), Jeeravipoolvarn et al., (2009), Miller (2010), Igbinedion

(2020), Salam et al. (2023), Patel et al., (2021), Zeng et al., (2015), Shahriar et al., (2018), Alam et al., (2021), Ren et al., (2022). In addition to laboratory scale studies, signs of ageing have also been observed for larger scales of studies e.g., in pilot studies and in real field deposits (Zhang et al., 2022, Dunmola et al., 2022). Additionally, the magnitude of ageing has been observed to depend on certain factors such as water content (Skempton and Northey, 1952; Banas, 1991; Seng and Tanaka, 2012; Shahriar and Jadid, 2018), pore water chemistry (Miller, 2010; Perret et al., 1996), in tailings flocculation protocols (Aladeef and Simms, 2020; Patel et al., 2021) and, polymer dosage (Salam et al.,2023), plasticity index (Yang and Anderson,2016; Shahriar et al., 2018; Alam et al.,2021), mineralogy and activity (Skempton and Northey, 1952; Shahriar et al., 2018), particle size (Miller, 2010; Patel et al., 2020), etc. Recently, Ren et al. (2021) reviewed numerous studies to understand the thixotropic behavior in soft clays and attempted to develop a correlation between thixotropic behavior and some index properties of soft soils. However, no well-established relationships were proposed. Moreover, the rate of aging is also important for freshly deposited tailings, dredged sediments, or similar materials as they are “young” materials by nature and yet to age. Therefore, information on their ageing behaviour may be

required to correctly estimate their long-term consolidation behaviour. Researchers have proposed empirical relationships to quantify the rate of thixotropic strength gain in remoulded natural soils (Zhang et al., 2017; Tang et al.,2020). However, those empirical relationships require information on sensitivity and/or the final ageing strength which is not readily available for the young materials. Recently, Salam et al. (2023) adapted concepts developed to understand the ageing behaviour in ideal colloidal suspensions and proposed the concept of the dual mode of ageing for soft soils. They suggested that it may be possible to predict the maximum likely magnitude of ageing in soft soils based on the strength measured at Early Equilibrium Strength (EES). Based on these findings, Patel and Simms (2022) proposed a relationship to quantify the rate of ageing and applied it to various soils, including remoulded natural soils and oil sands tailings. They observed that the rate of ageing is fairly constrained for young materials. Furthermore, studies conducted on wellstudied remoulded Leda clay material demonstrated that the rate of ageing remains nearly constant in self-weight consolidated samples regardless of their initial water contents (Patel and Simms, 2022), similar to the finding reported for ideal silica gels. Hence, this study builds on the previous study (Patel and Simms, 2022) conducted on well-studied Leda clay material. It aims to further validate the independence of the rate of ageing under different loading conditions. To achieve this, soft Leda clay samples were subjected to different consolidation stresses using two different test set ups, and ageing was tracked by monitoring the evolution of elastic shear modulus and apparent pre-consolidation pressure. Based on experimental findings on Leda clay, the large strain consolidation model was modified to incorporate ageing effects. 2

MATERIALS AND METHODS

2.1

2.2

Sample preparation

Samples were prepared with different initial gravimetric water contents (GWC) (higher than its liquid limit) by adding a predetermined amount of deionized water to the natural Leda clay samples. Natural undisturbed Leda clay was placed on a metal dish and disturbed through kneading for 50-55 minutes to obtain a soft plastic remoulded state and then a pre-determined amount of deionized water was added to reach the targeted water contents and hand-mixed again for another 50-55 minutes. Samples were then stored in airtight containers for 48-72 hours to achieve a homogenous distribution of water. After that samples were again hand-mixed for 15-30 minutes to eliminate the effects of consolidation during the storage period and bring the sample to the well-reproducible initial state. This process ensured that the samples were in a well-defined, reproducible state before being deposited into containers of different sizes and heights depending on the experimental setup as shown in Table 2. Table 2 Experimental details for ageing tests Initial GWC1

1.88 LL 1.53 LL 1.95 LL 1.52 LL

Leda clay

The Leda clay used in this study was collected from the Navan Landfill in Ottawa (Aladeef and Rayhani, 2017).. The properties of the Leda clay are summarized in Table 1 Table 1 Characteristics of Leda clay Characteristics (%)

Values

Natural water content

64

Natural solids content

56

Liquid limit

55

Plastic limit

25

Specific gravity

2.7

Clay content

71

D90, D60, D10 (μm)

6,1, 100 με – frequency determined by detailed assessment but recommend near-real time monitoring; and 8. Trench backfill procedures should: a. Minimize the potential for install damage, and

b.

7

Minimize settlements to avoid inducing excessive backfill strains on the pipeline.

CONCLUSIONS

The following conclusions are given: • SGs are a complementary monitoring tool amongst other monitoring techniques that can provide regular information about impacts on a pipeline; however, their limitations should be understood. • SGs that are monitored in near real-time can complement other near real-time ground monitoring to provide alerts for landslide reactivations and/or accelerations. • SGs should not be relied on as the sole source of information of monitoring soil to pipeline interactions based on their limited coverage. • SGs can be used to complement ILI IMU GEO and Axial Strain technologies for pipeline monitoring. • For strain relief, SGs should be used to confirm expected strain responses during work and to assess the relative effectiveness of the strain relief. • SGs are more useful in zones of tension than zones of compression due to failures in compressions being localized buckles and wrinkles that are difficult to target and that SGs cannot effectively assess. • SGs do not provide a reliable indication of FFS. The exact residual, construction stress, movement and thermal related stress prior to install are unknown and can only be estimated. 8

FURTHER RESEARCH AND WORK

Additional work to characterize SG signatures, failure mechanisms and correlating to ground movement or other causes. 9

ACKNOWLEDGEMENTS

Anne-Marie Gagnon for abstract translation. Beverly Grey for proofreading and formatting services. Count Von Heinrich (Heinz) and Baroness Genoa-Krimpli-Chorizo missed numerous walks during the preparation of this paper. However, Mittens the cat enjoyed many desk naps in the company of her human during this time. 10

REFERENCES

Babcock, J., Dewar, D, Webster, J, And Lich, T 2020. Deer Mountain Case Study: Integration of Pipe and Ground Monitoring Data with Historical Information to Develop a Landslide Management Plan. Proceedings 13th International Pipeline Conference, Calgary. Bukovansky, M.B. and Major, G. 2002. Twenty Years of Monitoring Pipelines in Landslides. Proceedings of the First European Conference on Landslides, Rybar, J., Stemberk. J. and Wagners, P. (Eds). A.A Balkema, Lisse. p.507 to 516.

Cruden, D.M. and Varnes, D.J. 1996. Landslide Types and Processes. Chapter 3, Landslides Investigation and Mitigation. Special Report 247. Transportation Research Board. National Academy Press Washington, D.C. USA: 36-75. Dewar, D., Tong, A., McClarty, E. and Van Boven, G. 2016. Technical and Operational Guidelines When Using Strain Gauges to Monitor Pipelines in Slow Moving Landslides. Proceedings 11th International Pipeline Conference, Calgary. Dewar, D., Tong, A., and McClarty, E. 2017. Assessing and Monitoring the Impacts of Very Slow Moving DeepSeated Landslides on Pipelines. Proceedings 75th Canadian Geotechnical Conference, Ottawa, Ontario. Dewar, D. 2019. A Suggested Soil and/or Rock to Pipeline Landslide Interaction Classification System. Proceedings 77th Canadian Geotechnical Conference, St. John’s. Dewar, D, 2020. Incorporating Inline Inspection Internal Measurement Unit Data Analysis into Integrity Management Programs. Proceedings 13th International Pipeline Conference, Calgary. Dewar, D., ElSeify, M., Van Boven, G., Bjorn, P, and Bruce, N. 2018. Operational Experiences with Axial Strain Inline Inspection Tools. Proceedings of the 11th International Pipeline Conference, Calgary. Katebi, M., Maghoul, P. and Blatz, J. 2019. Numerical Analysis if Pipeline Response to Slow Landslides: Case Study” Canadian Geotechnical Journal, 56:1779-1788. Hart, J.D., Czyz, J.A., and Zulfiqar, N. 2019. Review of Pipeline Inertial Surveying for Ground MovementInduced Deformations. Proceedings of the Conference on Asset Integrity Management – Pipeline Integrity Management under Geohazard Conditions, AIM-PIMG2019-1009, Houston. Liu, B., Wang, YY, and Chex, X. 2022. Application of Strain-Based Assessment in Support of Operational and Mitigation Decisions. Proceedings 13th International Pipeline Conference, Calgary. Murray, C.M. and Guthrie, R 2016. 16TAN North Saskatchewan River Crossing – Geotechnical Investigation Report (Final). Report to Husky Energy. Murray, C.M., Navjeeb, A., Jailitian, E., Onwude, L. and Hossain, T. 2022. High Fidelity Distributed Fiber Optic Sensing for Landslide Detection Proceedings 13th International Pipeline Conference, Calgary. United States Department of Transportation, Pipeline and Hazardous Materials Safety Administration (USDOT) 2022. Pipeline Safety: Potential for Damage to Pipeline Facilities Caused by Earth Movement and Other Geological Hazards. Pipeline and Hazardous Materials Safety Administration updated Bulletin. United States Department of Transportation, Safety Board 2022a. Enbridge Inc. Natural Gas Pipeline Rupture. Pipeline Investigation Report: PIR-22/01. Wang, YY., West, D., Dewar, D., Hart, J., McKenzieJohnson, A. and Gray, D. 2016. Management of Ground Movement Hazards for Pipelines. J.I.P. report prepared by Center for Reliable Energy Systems, Dublin, Ohio.

Tuesday, October 3, 2023

NUMERICAL MODELS I

Performance of DSM walls on shoring excavation subjected to influence of nearby high building loads and rapid drawdown in the lower mainland, Vancouver, BC Sounik Banerjee Ph.D. & Yasser Abdelghany Ph.D., P.Eng., PMP EXP Services Inc., Burnaby, British Columbia, Canada ABSTRACT The use of deep soil mixing (DSM) as a method of shoring for excavations is a common practice in engineering. This study presents a finite element 2D model in Plaxis to analyze the support of a 4.3 m excavation for a new high-rise development using a DSM wall reinforced up to a depth of 10m, while the rest remains without reinforcement up to another 22 m below ground level. The design of the DSM wall aims to protect the excavation against ground collapse from the self-weight and the presence of a 16-storey existing high-rise building. To consolidate the ground, stone columns were constructed up to the less permeable soil layers for soil densification, and a preload height of 3.5m was applied. The DSM wall was numerically designed with respect to the superstructure, consolidation settlement for the soft clay layer, and the rapid drawdown during the excavation process. Additionally, the horizontal deflection and vertical settlement of both the excavation wall and the high-rise building were analyzed based on different water content for DSM cement mixing with two target layers viz., the loose sand and clayey silt. The study provides valuable insights into the design of DSM walls for excavations, taking into consideration various factors that can affect the stability of the structure. RÉSUMÉ L'utilisation du mélange de sol profond (MSP) comme méthode d'étayage pour les excavations est une pratique courante dans l'ingénierie. Cette étude présente un modèle 2D à éléments finis dans Plaxis pour analyser le soutien d'une excavation de 4,3 m pour un nouveau développement en hauteur à l'aide d'un mur DSM renforcé jusqu'à une profondeur de 10 m, tandis que le reste reste demeure sans renforcement jusqu'à 22 m sous le niveau du sol. La conception du mur DSM vise à protéger l'excavation contre l'effondrement du sol dû au poids propre et à la présence d'un immeuble de grande hauteur de 16 étages présent depuis environ 50 ans. Pour consolider le sol, des colonnes de pierre ont été construites jusqu'aux couches de sol les moins perméables pour densifier le sol, et une hauteur de précharge de 3,5 m a été appliquée. Le mur DSM a été conçu numériquement en ce qui concerne la superstructure, le tassement de consolidation pour la couche d'argile molle, et l'abaissement rapide pendant le processus d'excavation. En outre, la déflexion horizontale et le tassement vertical du mur d'excavation et de l'immeuble de grande hauteur ont été analysés. L'étude fournit des indications précieuses sur la conception des parois DSM pour les excavations, en tenant compte de divers facteurs susceptibles d'affecter la stabilité de la structure. Keywords: Soil Mechanics, Numerical Modeling, Deep Soil Mixing, Geohazards

1

INTRODUCTION

Deep soil mixing (DSM) is a popular ground improvement technique used to enhance soil properties and increase soil strength. DSM walls have been used in numerous civil engineering projects for excavation support, foundation construction, and environmental remediation. The use of DSM walls can help to reduce permeability, increase stability, and prevent seepage in soil conditions where conventional construction methods are not feasible. However, DSM walls can also face challenges in certain soil conditions, such as those with seepage, ground heaving, and the presence of loose sands and silts. Previous studies have investigated the performance of DSM walls in challenging soil conditions. A study by Han and Ye (2002) evaluated the effectiveness of DSM walls in preventing seepage in a riverbank. The study found that the DSM walls were effective in reducing seepage, but that the design of the wall and the binder type and dosage were critical factors in achieving optimal performance. Similarly, a study by Rutherford et al. 2007 investigated the use of DSM walls for excavation support in a site with loose sands

and silts. Yapage et al. 2014 studied the effect of soft ground of the performance of DSM walls. Other studies have focused on the use of quality control measures during DSM wall construction. A study by Ishibashi et al. (2017) investigated the use of a continuous mixing method for DSM walls to improve quality control and ensure the uniform distribution of the binder. The study found that the continuous mixing method was effective in achieving consistent binder distribution and that the resulting DSM walls had higher strength and durability compared to those constructed using conventional methods. Despite these findings, there is still a need for further research to address the specific challenges posed by the effect of water content in the DSM mix for cohesive and non-cohesive materials. Overall, the literature suggests that DSM walls can be an effective solution for challenging soil conditions, but that careful consideration must be given to factors such as soil type, binder type and dosage, mixing depth, and quality control measures during construction. The findings of this paper contribute to this area of research by presenting a

comprehensive evaluation of DSM wall performance in an excavation project with seepage, ground heaving, and loose sands and silts, providing valuable insights into the factors that contribute to successful DSM wall construction in these conditions.

2013). This value ensures an accurate representation of the stiffness and behavior of the DSM walls in the analysis.

2

The construction process for the study can be divided into eight stages. The detailed stages are described in Table 2 and also the important stages are shown from the Plaxis model in Figure 2. Stage 1 involves establishing virgin soil conditions with zero displacement. In Stage 2, the high-rise building is constructed. Once construction is complete, Stage 3 begins, which marks the end of consolidation at the position under building load. Stage 4 is reached at the end of consolidation after preload. In Stage 5, preload removal is complete, and consolidation continues. At this point, the lateral deflection and vertical settlement values are determined. Stage 6 marks the end of the first stage of excavation, while Stage 7 marks the end of the second stage of excavation. Stage 8 includes the end of the third stage of excavation and final consolidation. The lateral deflection and vertical settlement values discussed in the results and discussions section were measured at the end of consolidation, which is at the end of Stage 8. These stages provide a clear timeline for the construction process and help to contextualize the findings of the study.

METHODOLOGY

In this study, the soil is modeled using 15 noded plain strain elements in Plaxis 2D. The soil layers used in the simulation are representative of those typically found in the Richmond area near the Brighouse Station. The soil layers include a top layer of fill, followed by layers of loose sand, clayey silt, and a compact layer of sand. Since the loading is static in nature and the problem is not expected to undergo large deformation, a simple Mohr-Coulomb material model is used for all the soil layers. The analysis also considers possible consolidation of all the soil layers. The DSM column is designed such that each cylindrical column has a diameter of 0.6 m and an area of mutual overlap. The center-to-center distance between the DSM columns is considered as 1.3 m. In each alternative column, an I section reinforcement is considered to better resist the flexural load from the soil layer and the nearby structures. Table 1 provides more details about the reinforcement design. The DSM section is checked against moment and shear resistances based on Figure 1, but these details are not explained in detail in this paper for the sake of brevity.

2.1

Stages of construction

Table 2. Stages of construction followed in the tests Stages S1

Description Establishment of virgin soil conditions (zero displacement)

S2

Construction of the high-rise building

S3

End of consolidation the position under building load End of consolidation after preload End of Preload removal+ End of consolidation after preload removal End of first stage of excavation End of second stage of excavation End of third stage of excavation + final consolidation

S4 S5 S6 S7 S8 Figure 1. Details of the DSM wall configuration showing the cross-section of the DSM mix and the I section reinforcement in alternate columns.

Building UDL

Table 1. Details of the I section used for the reinforcement of DSM walls. Metric

Depth (mm)

Width (mm)

Thickness (mm)

460× 260

509

289

40.4

Web thickness (mm) 22.6

Preload

Section Modulus (mm3) 5650

The DSM walls in this study are modeled as elastic plate elements, incorporating appropriate interface elements. The elastic modulus (E value) for the DSM walls is determined based on the recommended value of 380 times (as per the FHWA for Embankments and Foundations,

(a)

Table 4. Soil stratification considered in the current study Soil layers Excavation

(b)

Case 1 Case 2

Case 3

Case 4

Case 5

Sand Fill

0

0

0

0

0

Clayey Silt

0.8

0.8

0.8

0.8

0.8

Loose Sand 3.9 Compact 4.5 Sand

3.9

3.9

4.9

5.9

5.5

6.5

5.5

6.5

Difference between top of each layer gives layer thickness. Case 1 to 3 assumes variations in the Loose Sand layer across the site, whereas Case 1, 4 and 5 represents changes in the Clayey Silt Layer. 1

Figure 2. Model diagrams in Plaxis showing (a) preloading after construction of the DSM wall, (b) excavated section and steady state removal of water. 2.2

Top1 (m)

(a)

Material Properties

The material properties of the soil layers are presented in Table 3. The soil layers are composed of a medium dense sand fill, clayey silt which has relatively low cohesion and low friction angle, followed by loose sand and followed by a till-like layer which here is presented as a compact sand. Table 3. Strength properties of the soil layers Characteristics

Sand Fill

Clayey Silt Loose Sand

Compact Sand

Young’s Modulus, E in kN/m2

71.3e3

21.45e3

25e3

85e3

Poisson’s Ratio 0.30

0.39

0.31

0.29

Liquid limit

3

35

-

3

Plastic limit

-

22

-

-

Friction angle ∅ in °

32

22

30

36

Dilation angle Ѱ in °

1

0

0

3

Cohesion c in kPa

0

20

0

0.5

2.3

(b)

Variation in stratigraphy

Assuming that the typical DSM wall extends approximately 50 m across the plane, it is reasonable to assume that the two critical layers, Loose Sand and Clayey Silt, will vary across the site. In order to model the soil profile in 2D, Table 4 describes the possible different sections that need to be considered. Cases 1, 2, and 3 consider the depth of the Loose Sand layer to be 0.6 m, 1.6 m, and 2.6 m, respectively. Cases 1, 4, and 5 capture the variation in the Clayey Silt layer at 3.1 m, 4.1 m, and 5.1 m, respectively. It should be understood that the variation in these important layers might significantly affect the serviceability of the DSM wall and, therefore, must be considered in the analysis. Specifically, the variation in these layers might affect the lateral movement of the DSM wall, its settlement, and the settlement of the 16-storey building with a total uniform load of 210 kN/m2, which is assumed to be at a fixed distance of 7 m from the excavation.

Figure 3. Variation of compressive strength of soil-cement mix at the end of setting for (a) sand, (b) clays (modified from Topolnickli 2016) 2.4

Water content

Apart from the variation in the thickness of the soil layers, it is also important to focus on the water content of the design mix which uses soil from these layers to prepare the DSM wall. It was pointed out by Topolnickli (2016), that the effect of water content on the 28 day Uniaxial Compressive Strength (UCS) of sands are different from that observed in clays when they are mixed with cement. Figure 3 shows that increase in the total water content TWC can significantly and monotonically decrease the UCS values

of concrete when sand is used in mixed design for DSM walls. This trend is different in clays, where increase in WC might increase the UCS value up to an optimum maximum value in the mix strength is obtained beyond which there is steady decline in its strength. In the current study, we assume that the design of the DSM walls will involve different combinations of the WC in the loose sand layer and the clayey silt layer. The WC in the fill and the compact sand layer are considered as constant. It is interesting to note how the DSM wall bends inwards from the combined effect of excavation and the lateral force exerted by the 16 storey building. The values of UCS can be usually connected with the Young’s Modulus E value of a DSM deep-mixed material empirically as 380 times the UCS value of the specimen (Topolnickli (2016)), 3

RESULTS AND DISCUSSIONS

The investigation focused on the construction of the Deep Soil Mixing (DSM) wall and subsequent excavation. The variation in the soil profile across a 50 m out-of-plane length excavation was captured using five different 2D plane strain sections, namely Case 1 (Sand & Clay), Case 2, Case 3, Case 4, and Case 5. These variations had significant effects on the lateral deflection (u) of the DSM wall nearest to the high-rise building, as well as the settlement of the high-rise building (v). u

Figure 3. However, as the thickness of the loose-soil layer increased, as represented by Case 2 and Case 3, the deflection exhibited a gradual increase but appeared to saturate after a certain depth. In the case of clay layers, there seemed to be a specific design TWC that resulted in the least effect on the u value. Moreover, the impact of TWC on the reduction of the u value was relatively lower when compared to the loose sand mix. Furthermore, the influence of increased clay layer thickness, as depicted in Case 4 and Case 5, exhibited diminishing effects on the u value. The changes in u became progressively lesser as the clay layer thickness increased. Understanding these settlement patterns is crucial for the design and construction of structures in similar geotechnical conditions. It allows engineers to anticipate and mitigate potential settlement issues, enabling the development of more robust and resilient foundation systems for high-rise buildings.

v

Figure 5. Lateral deflection of the top right corner of the DSM wall in response to varying water content in the DSM cement-soil mix design for loose sand and clayey-silt layers.

Figure 4. The deformed mesh showing the lateral deflection of the DSM wall and the vertical settlement of the close building. Note : Settlements are exaggerated 50 times for better visualization Figure 5 illustrates the lateral deflection of the DSM wall at the top right corner, providing insights into the behavior of the wall under different scenarios. An envelope of curves was plotted, considering variations in water content exclusively for the sand layer. It was observed that there exists a monotonic, almost power-law relationship between the increase in total water content (TWC) and the lateral deflection. For instance, at TWC = 20%, the u value was found to be 53 mm, while for TWC increased to 32%, the u value increased significantly to 94 mm. This trend closely aligns with the decrease in compressive strength observed in cement-sand mixtures as TWC increases, as shown in

Figure 6 illustrates the impact of changes in the unconfined compressive strength (UCS) of the soil mix and the increased consolidation resulting from the heightened clay layers on the settlement of the left corner of the high-rise building. These settlement observations are a consequence of the excavation and redistribution of in-situ stresses. The influence of UCS reduction on the vertical settlement (v) of the building is prominently observed in the sand layer, where consolidation has no effect. The settlement in this layer is primarily governed by the reduction in UCS. However, the behavior of the clay layer differs, as it deviates from the initial loss of strength curve, which exhibited an inverted "V" shape. As the thickness of the clay layer increases, the settlement of the building due to layer consolidation becomes more significant than the loss of lateral stress caused by the excavation.

consolidation of the silty clay layer played a relatively smaller role. Furthermore, the settlement of the high-rise building was influenced by both the excavation process and consolidation of the soil layers affected by the excavation. In sections where the thickness of the silty clay layers exceeded 2 m, vertical settlement was predominantly governed by consolidation rather than the effect of the DSM wall. These findings highlight the importance of considering water content and variations in soil layers when designing and constructing DSM walls. The results also emphasize the need for careful monitoring and analysis during the construction process to mitigate potential settlement issues and ensure the stability and performance of both the DSM wall and adjacent structures. 6 Figure 6. Vertical settlement of the left corner of the highrise building in response to varying water content in the DSM cement-soil mix design for loose sand and clayey-silt layers. These findings highlight the contrasting behaviors of the sand and clay layers. The sand layer primarily experiences settlement driven by the reduction in UCS, whereas the clay layer exhibits settlement influenced by both consolidation and lateral stress reduction. The deviation from the original loss of strength curve suggests the complex interaction between the clay layer and the surrounding soil during the excavation process.

REFERENCES

Algulin, J.O.E.L. and Pedersen, B.J.Ö.R.N., 2014. Modelling of a piled raft foundation as a plane strain model in PLAXIS 2D. Master of Science, Department of Civil and Environment Eng, Division of Geo-Engineering, Chalmers University, Sweden. Bruce, M.E.C., Berg, R.R., Filz, G.M., Terashi, M., Yang, D.S., Collin, J.G. and Geotechnica, S., 2013. Federal highway administration design manual: Deep mixing for embankment and foundation support (No. FHWA-HRT-13046). United States. Federal Highway Administration. Offices of Research & Development.

Overall, the results demonstrate the significant influence of soil variations on the behavior of the DSM wall. The relationship between water content, soil layer thickness, and lateral deflection provides valuable insights for the design and construction of DSM walls, highlighting the importance of considering these factors to ensure the stability and performance of such geotechnical structures.

Han, J., Zhou, H.T. and Ye, F., 2002. State-of-practice review of deep soil mixing techniques in China. Transportation research record, 1808(1), pp.49-57.

5.

Rutherford, C.J., Biscontin, G., Koutsoftas, D. and Briaud, J.L., 2007. Design process of deep soil mixed walls for excavation support. ISSMGE International Journal of Geoengineering Case Histories, 1(2), pp.56-72.

CONCLUSIONS

In this study, a comprehensive analysis of the Deep Soil Mixing (DSM) wall construction and its interaction with the surrounding soil and nearby high-rise building was conducted using Plaxis 2D fully coupled simulation. The construction process was divided into eight phases, including wall preparation, preloading, and excavation of the inner soil in three stages. The variability in the soil layers around the site was carefully considered, focusing on two critical layers: loose sand and clayey silt. Five different sections were analyzed, accounting for the variations in these target layers. The results of the study revealed that the water content and differences in thickness between the sand and silt layers significantly influenced the performance of the DSM wall. Specifically, the lateral deflection of the wall and settlement of the nearby high-rise building were found to be greatly affected by these factors. The lateral settlement primarily resulted from unloading due to excavation, while

Topolnicki, M., 2016, February. General overview and advances in Deep Soil Mixing. In XXIV geotechnical conference of torino design, construction and controls of soil improvement systems (pp. 25-26).

Taki., O..and Yang, D. 1991, Soil Cement Mixed Wall Technique, Geotechnical Engineering Congress, ASCE, New York, Specia l Publication, 27: 298-203 Yapage, N.N.S., Liyanapathirana, D.S., Kelly, R.B., Poulos, H.G. and Leo, C.J., 2014. Numerical modeling of an embankment over soft ground improved with deep cement mixed columns: case history. Journal of Geotechnical and Geoenvironmental Engineering, 140(11), p.04014062.

Preliminary impact force estimation of the Deschaillons Landslide, in Québec, using the Material Point Method John Forero, Félix St-Pierre and Ariane Locat Département de génie civil et de génie des eaux – Université Laval, Québec, Canada Floriane Provost Ecole et Observatoire des Sciences de la Terre [EOST] – Université de Strasbourg, Strasbourg, France Jacques Locat Département de géologie et de génie géologique – Université Laval, Québec, Canada Scott McDougall Department of Earth, Ocean and Atmospheric Sciences – University of British Columbia, British Columbia, Canada Pascal Locat and Rémi Mompin Ministère des transports et de la Mobilité durable du Québec, Québec, Canada ABSTRACT In eastern Canada, superficial landslides are a frequent type of landslides in clay slopes. On April 27, 2019, such a landslide occurred in Deschaillons-sur-le-Saint-Laurent along the Saint-Laurent River in Québec. To investigate the post-failure impact force of this case, a model based on the Material Point Method (MPM), MPM-PUCRio, and another based on Smoothed Particle Hydrodynamics (SPH), DAN3D, are used in this study. The pre- and post-failure surfaces used in the models are generated from available Digital Elevation Models (DEM). The clay material that constitutes the sliding mass is considered viscoplastic and is simulated using the Bingham model. The results from the MPM-PUCRio software are compared with the results from the DAN3D software in order to define and validate the impact force estimation for the Deschaillons superficial landslide. RÉSUMÉ Dans l'est du Canada, les glissements de terrain superficiels sont le type de glissement de terrain fréquents dans les pentes argileuses. Le 27 avril 2019, un tel glissement de terrain s'est produit à Deschaillons-sur-le-Saint-Laurent, le long du fleuve Saint-Laurent au Québec. Pour étudier la force d'impact lors de la post-rupture de ce cas, un modèle basé sur la méthode Material Point Method (MPM), MPM-PUCRio, et un autre basé sur le Smoothed Particle Hydrodynamics (SPH), DAN3D, est utilisé dans cette étude. Le pré- et post-rupture utilisées dans les modèles sont générées à partir des modèles numériques d'élévation (DEM) disponibles. Le matériau argileux qui constitue la masse de débris est considéré comme viscoplastique et est simulé à l'aide du modèle de Bingham. Les résultats du logiciel MPM-PUCRio du logiciel DAN3D sont comparés afin de définir et de valider l'estimation de la force d'impact pour le glissement superficiel de Deschaillons. 1

INTRODUCTION

Superficial landslides are fast and can reach large distances in relatively short periods of time. These landslides occur mainly in areas of steep slopes (more than 20°) and can be triggered by heavy rainfall. The sliding mass can contain a mixture of water and sediments of various sizes (Causes 2001). In Eastern Canada, Alaska, and Norway, the source of landslides can often contain sensitive clay (Geertsema et al. 2018). According to Cruden et al. (1996), the volume of the displaced mass, the type of movement, and the height and inclination of the slope are factors that determine the velocity of the debris. The velocity of the displaced mass also strongly depends on the thickness of the mass and the width of the flow channel. These factors are frequently used in hydrodynamic methods to determine the impact force of debris. As reported by Tan et al. (2019), the hydrodynamic impact force estimation method first proposed by Hungr et al. (1984) is calculated as:

𝐹 = 𝛼𝜌𝜈2 ℎ𝑤

[1]

Where 𝜌 is the bulk density of the material (𝑘𝑔⁄𝑚3 ), 𝛼 is the dynamic coefficient, 𝑣 is the velocity (𝑚⁄𝑠), and ℎ (𝑚) and 𝑤 (𝑚) denote the debris flow depth and the channel width, respectively. A detailed literature review for the estimation of impact force was conducted by (Provost et al. 2022). In recent years, advanced numerical methods have been developed to allow the analysis of engineering problems involving large deformations (e.g. Troncone et al. 2020). Numerical particle-based methods are frequently used to solve large-deformation problems. Currently, the most relevant are the Smoothed Particle Hydrodynamics method (SPH) developed by Lucy (1977) and Gingold et al. (1977), the discrete element method (DEM) developed by Cundall et al. (1979) and the Material Point Method (MPM) developed by Sulsky et al. (1994). SPH is a mesh-free technique based on a pure Lagrangian description. It has been used to solve problems such as fluid-structure interaction (FSI) (e.g. Dai et al.

2017) and large-scale landslide motion (e.g. Dai et al. 2014; Peng et al. 2022). Several software programs have incorporated viscoplastic models, such as the Frictional and Bingham models, based on the SPH method. For instance, Cuomo et al. (2017) utilized the Frictional model to investigate the propagation of debris avalanches, while Dai et al. (2017) employed the Bingham model in clays to investigate fluid-structure interaction and debris flow impact estimation. MPM, which was developed for simulating large deformations with a free mesh, has been applied in several studies for post-rupture slip simulations, including LlanoSerna (2016), Soga et al. (2016), and Conte et al. (2019). Additionally, MPM has been utilized to calculate the impact forces of landslides on barriers, as demonstrated in studies by Cuomo and Martinelli (2022), Cuomo et al. (2021), and Ceccato et al. (2017). Several authors have employed the Mohr-Coulomb constitutive model in MPM for landslide modelling. For instance, Bandara (2016) used it to determine the hydro-mechanical behaviour, while Nguyen et al. (2021) performed a parametric study of shallow landslides using this model. Andersen et al. (2008) also used it to model landslides in fine-grained soils induced by rainfall, based on the generalized interpolation material point (GIMP) method. Conversely, Llano-Serna et al. (2016) and Xu et al. (2019) modelled landslide run-out processes using MPM with the von Mises model and Drucker-Prager model with viscosity. To the best of our knowledge, no studies have yet simulated landslides in sensitive clays using the Bingham viscoplastic model with MPM. Therefore, this work aims to contribute to the field by applying the Material Point Method to model superficial landslide runout in sensitive clays using the Bingham model. Additionally, this work aims to define and validate the calculation of the impact force, by comparing results from DAN3D and MPM-PUCRio. To achieve this, the Deschaillons landslide that occurred on April 27th 2019, is used in this study (Figures 1 and 2). The debris, made of clay and vegetation, impacted buildings of a marina located near the toe of the slope, fortunately causing no deaths. Information about the conditions before and after the landslide was also acquired by Québec Ministry of Transportation engineers and made available to Université Laval for this research project.

Figure 1. Deschaillons landslide located on the south shore of the St. Lawrence River east of Québec City. (Québec Ministry of Transportation -MTQ) Figure 1 shows a photo of the debris at the base of the slope resulting from the Deschaillons landslide.

Additionally, the observed damage to the structures provides valuable insights. The approximate thickness of the debris was determined using a scale. This measured thickness is later used for comparison with the thickness obtained through numerical modelling using DAN3D and MPM-PUCRio software.

Figure 2. Topography of the site before the Deschaillons landslides, showing the location of cross section A-A’ and of two observation points located near buildings affected by the landslide: point 1 near the toe of the slope and point 2 on the side of a building impacted by the flow. 2

THE DAN3D AND MPM-PUCRIO SOFTWARE

The software MPM-PUCRio, was developed by Fernández (2020) based on the Material Point Method. The domain of MPM is represented by Lagrangian points called MPs and an Eulerian mesh. The method was developed to solve large deformation problems. The MPs are moving through an Eulerian computational mesh. The MPs carry all physical properties of the continuum, such as velocities, stresses, strains, density, momentum, material parameters, and other state parameters, whereas the computational mesh is used to solve the balance equations without storing any permanent information. Figure 3 illustrates a schematic of the MPM method. On Figure 3a, the variables are transferred from the particles to the nodes by mapping functions. The balance equations are later solved at the nodes (Figure 3b). On Figure 3c, subsequently, the solution is interpolated back to the particles. The position of the particles and the variables are updated (Figure 3d). This process is repeated at each time step until the end of the simulation.

Figure 3. Schematic of the MPM method showing: (a) transfer of information from moving particles to fixed nodes, (b) solution of balance equations at nodes (c) transfer of information back from nodes to particles and (d) update to particle positions.

were imposed, which allowed particles to only move in the direction of the slide (Y axis). The limits of the Eulerian mesh were set to (0,0,0) for the initial coordinates (𝑋0 , 𝑌0 , 𝑍0 ) and (50,110,50) for the final coordinates (𝑋𝑛 , 𝑌𝑛 , 𝑍𝑛 ). During the simulation, a contact method with a friction coefficient of 0.55 was employed. Using lower coefficients resulted in an increase in particle velocity. The buildings were not considered in this case. In DAN3D, the 3D model was constructed based on the same topography and source as in MPM-PUCRio. However, the construction of the 3D model in DAN3D differs, as it does not require a particle distribution or generation of a base volume. The software interface allows for the use of previously generated (.grd) files from LiDAR data as input. This method allows for a computationally faster and more efficient model. Additionally, the interface enables the use of a Digital Elevation Model (DEM) as a background image, aiding in locating debris at each time step. DAN3D also provides a friendly and easy-to-use interface. The simulation employed 1000 particles. The simulation duration was set to 60 seconds. DAN3D software enables data export at specific time steps, offering information on node and particle thickness and velocity. Similar to the MPM-PUCRio model, the presence of structures at points 1 and 2 was not considered.

The software DAN3D, developed by McDougall and Hungr (2004), is based on the SPH method. SPH uses a set of particles that interact with each other through a smoothing function, which allows the calculation of fluid properties at any point in the simulation domain. Each particle has a volume, position, and velocity, and these properties are updated based on the forces acting on them. 2.1

Numerical 3D model

The numerical model representing the Deschaillons superficial landslide was constructed using digital elevation information based on LiDAR data. Two sets of data were required, one from 2017 and the other from 2019, taken before and after the landslide occurred, respectively. The difference between these elevation points was used to determine the source of the landslide. Additionally, both data sets were combined to obtain the topography along which the landslide flows in the numerical analysis. Figure 4 shows the 3D model of the Deschaillons superficial landslide obtained from these data sets and used in the MPM-PUCRio software. The surface for MPM-PUCRio was constructed using elevation points spaced at 0.25 m intervals. A volume was generated from the surface, where each cell had dimensions of 1 m x 1 m x 1 m and contained 16 material points. Decreasing the dimensions of this configuration caused convergence issues during the simulation. The same particle distribution was used to construct the source. Two materials were considered for the numerical simulation: the base volume generated from the topography and the volume of the source (171 𝑚3 ). To perform this work, both ArcGIS software and the Python programming language were utilized. The simulation involved a total of 852,075 particles. Contour conditions

Figure 4. A 3D model of the Deschaillons superficial landslide was created using the MPM-PUCRio software. The landslide is represented in red and the surface material is represented in blue. 2.2

Bingham rheological parameters

The Bingham model is a rheological model used to describe the behaviour of viscoplastic fluids, which are those that exhibit a threshold or yield stress before they begin to flow. According to Dai et al. (2017), in this model, the relationship between shear stress and shear strain rate of a viscoplastic fluid is described using the following equation:

𝜏 = (𝜇𝐵 + (𝐷

𝜏𝑜 1/2 2𝑑 )

[2]

)𝐷

Where 𝜏 is the shear stress, 𝜏𝑜 is the yield stress, 𝜇𝐵 is the viscosity, 𝐷 is the tensor of strain rates, and 𝐷2𝑑 is the second invariant of the tensor of strain rates. The Bingham viscoplastic model was utilized in this study to represent the behaviour of sensitive clays. The rheological parameters were obtained from Locat et al. (1988), who presented a relationship between viscosity and yield stress. Linear interpolation was employed to determine the values listed in Table 1. Laboratory tests should be carried out in future studies to validate these parameters. Table 1. Parameters of the Bingham model Software

𝑬 (𝒌𝑷𝒂)

𝒗

DAN3D MPMPUCRio

3

5.e3

0.3

𝝆 (𝒌𝒈 ⁄𝒎𝟑 )

𝝉𝒐 (𝒌𝑷𝒂)

𝝁𝑩 (𝒌𝑷𝒂 ∙ 𝒔)

1938

5

0.005

1938

5

0.005

RESULTS

Figure 5 illustrates the area impacted by the Deschaillons landslide (light blue line) compared with the simulations using DAN3D and MPM-PUCRio. In both cases, the particle cluster representing the debris covers the actual landslide area shown by the light blue dashed line. The debris from the DAN3D analysis goes slightly past

the actual landslide limit and the debris from the MPMPUCRio analysis stays inside it. The simulated velocities, thicknesses and impact forces along cross-section (A-A') and points 1 and 2 were also analyzed. These points were located near the buildings to estimate the impact forces on structures (see Figure 2 for locations). Figure 6a illustrates the maximum velocity at a given distance obtain from the analysis along cross-section A-A' using DAN3D and MPM-PUCRio. The coordinate (0,0) is the intersection between the line A-A' and the light blue line, at the top. In the DAN3D simulation, the maximum velocity is 12.7 m/s, occurring at 27.5 m and 3 seconds (dashed red line). Similarly, the MPM-PUCRio software shows a maximum velocity of 13.3 m/s at 26 m, reached within 5 seconds (dashed blue line). However, the velocities do not reach zero by the end of the simulation, primarily due to the stopping criteria of the software. Generally, the maximum time value is considered as the stopping criterion in numerical modelling, rather than a specific variable value. In this case, a time greater than 60 seconds was not considered. Figure 6b depicts the maximum thickness at a given distance along cross-section A-A' using DAN3D and MPMPUCRio. The maximum thickness observed in both cases are consistent. A maximum thickness of 0.75 m is observed approximately at 30 m, coinciding with the maximum velocity observed in Figure 6(a). In addition, it is observed that the maximum peak is close to the red and blue dashed lines that indicate the moment at which the maximum velocity peak is reached. Between 50 m and 80 m, an average maximum thickness of 0.30 m is observed.

Figure 5. Area of impact of the final debris, comparing the DAN3D and the MPM-PUCRio software and the area of actual impact indicated by a dashed light blue line.

The approximate thickness (observed data) depicted in Figure 1 was added to Figure 6b (red dots) to facilitate comparison with the numerical models. Figure 6c displays the maximum impact force at a given distance along cross-section A-A' using maximum velocities and thicknesses from DAN3D and MPMPUCRio, with a hydrodynamic coefficient of α = 0.9 in both cases. The α value is based on Wendeler (2016), who used a value of 0.7 for low-density clay debris flows. The density of sensitivity clay from the Deschaillons landslide is higher, as shown in Table 1, therefore the selected value of α is higher. However, these values are preliminary and must be corroborated in the laboratory or in the field. Notably, the maximum impact force occurs at approximately 30 m, close to the peak of the maximum velocity observed in Figure 6a. These results indicate a significant risk factor for any structure located at this distance along cross-section A-A'. It is important to reiterate that the impact force is not directly calculated by the DAN3D and MPM-PUCRio programs themselves. Instead, it is derived from the analysis of the pre-existing thickness and velocity data provided by these programs. These data are utilized in the study to estimate the impact force using the traditional hydrodynamic equation. Figure 7a depicts the velocity at points 1 and 2 (see Figure 1 for locations). These velocities are specific to these points and do not correspond to the maximum velocities recorded along cross-section A-A’ in Figure 6a. In Figure 7a, a peak velocity of 1.0 m/s for DAN3D and 2.2 m/s for MPM-PUCRio is observed at point 1 within 15 seconds. The peak velocities occur after about 30 s at point 2. In both cases, the velocity decreases over time. Figure 7b presents the thickness at points 1 and 2. It is observed that the results obtained using MPM-PUCRio are adequately adjusted to those obtained using DAN3D. At point 1, the average thickness measures 0.25 m, while at point 2, it measures 0.120 m. In both cases the average thickness is less than the observed thickness of 0.70 m. However, the observed thickness includes the accumulation of debris in the structures, whereas the thickness calculated by DAN3D and MPM-PUCRio does not take into account the presence of structures. In Figure 7c, the impact force at points 1 and 2 is displayed. Point 2 has a lower impact force than point 1. A value of the hydrodynamic coefficient of α = 0.9 was used for DAN3D and MPM-PUCRio. At point 1, the maximum peak of the impact force, determined by the velocity and thickness calculated using MPM-PUCRio, is nearly three times higher compared to DAN3D. This difference can be primarily attributed to the velocity estimation depicted in Figure 7(a). At point 2, the impact force values are similar. This is because both DAN3D and MPM-PUC Rio produce consistent results in terms of velocity and thickness calculations. However, the impact force value is sensitive to the hydrodynamic factor. For sensitive clays, this factor is not yet well-defined in the literature. The hydrodynamic factor considers the angle between the velocity vector and the infrastructure (barrier), which must be measured in the field or in the laboratory. An analysis of the influence of the hydrodynamic factor on the impact force is presented in Figure 8. The results show that

as the hydrodynamic factor decreases, the results from the two software converge.

Figure 6. Comparation between DAN3D and MPM-PUCRio along cross-section A-A': (a) maximum velocity, (b) maximum thickness, (c) maximum impact force. Solid black lines are used for the DAN3D results and dashed black lines are used for the MPM-PUCRio results.

Figure 8. Comparison between DAN3D and MPM-PUCRio with various hydrodynamic factors (α) used to estimate impact forces at points 1 and 2. 4

Figure 7. Comparison between DAN3D and MPM-PUCRio at points 1 (black lines) and 2 (blue lines): (a) velocity, (b) thickness, (c) impact force. Solid lines are used for the DAN3D results and dashed lines are used for the MPMPUCRio results.

DISCUSSION

The results presented in Figure 6 only represent the maximum velocities, maximum thicknesses and maximum impact forces simulated along cross-section A-A'. They do not provide information about the overall extent of the landslide, leaving uncertainty about the specific areas where the velocities, thicknesses, and impact forces are highest. However, it is evident that between 20 and 40 metres, the impact force reaches potentially dangerous maximum values, ranging from 200 KN/m to 250 KN/m. On Figure 6b, the observed data (red dots) are not exact representations of the actual thicknesses, but rather approximations. Ideally, precise measurements would have been obtained from digital information systems; however, this was not feasible due to the removal of a portion of the material deposited at the slope base prior to the digital data collection using LiDAR. Furthermore, this observed data may exhibit variations depending on the image location and projection. Nevertheless, this approach was employed effectively for the purpose of comparing the DAN3D and MPM-PUCRio software. The simulation utilized the contact method implemented in MPM-PUCRio, which effectively prevents particle interpenetration between different materials by controlling particle velocities through a designated function. For the specific case of sensitive clays, a coefficient of friction of 0.55 was employed. Additional simulations, which are not presented in this study, revealed that a decrease of the coefficient of friction leads to higher particle velocities. In Figure 6c, the calculated impact forces are similar throughout the entire process in both models, indicating consistent results between the two software programs despite their numerical differences. In Figure 7, as anticipated, the impact force is lower at point 2 due to its distance from the starting point of the landslide. The calculation of the impact force in Figures 6 and 7 utilized the hydrodynamic force, while the hydrostatic force was disregarded, as it is typically lower, as noted by Tan et al. (2019).

The objective was to estimate the impact forces during the post-failure phase, and the influence of material accumulation at points 1 and 2 was not taken into account. It would be worth incorporating structures into the simulations and assessing the impact forces using various calculation methods depending on the type of structure and whether it is rigid or flexible, as suggested by Tan et al. (2019). However, this is not easy to implement in a depth averaged model like DAN3D. The estimation of impact force was conducted using both DAN3D and MPM-PUCRio software. The magnitudes of the impact forces obtained from both software programs are consistent. However, it should be noted that the results for velocity and thickness are still preliminary. In order to obtain more accurate results using MPM-PUCRio, simulations with models employing different particle discretization techniques need to be performed. Additionally, the Bingham model utilized in MPM-PUCRio should be validated using experimental data. The findings of this study contribute to the protection of people and the safe design of infrastructure. The current project did not include a parametric analysis of the independent input variables, but it is crucial for future studies to consider estimating velocity based on various scenarios, including factors such as mass, height, slope, and thickness, as recommended by Cruden et al. (1996). Additionally, it is important to estimate the rheological parameters through laboratory tests instead of relying on linear projections, as done in this study. By incorporating these recommendations into future research, a more comprehensive understanding of the process can be achieved, leading to improved prevention strategies and more accurate design of protective measures. 5

CONCLUSION

The DAN3D and MPM-PUCRio software utilize different formulations, based on the SPH and MPM methods, respectively. However, both 3D models used the same input topography, source locations and rheological properties. The preliminary results of velocity, thickness, and impact force simulated by the two programs are reasonably consistent. This demonstrates the potential of the Material Point Method for calculating the impact force of flow slides on infrastructure. However, DAN3D offers greater ease in obtaining the output variables, such as thickness, compared to MPM-PUCRio. In contrast, utilizing the MPM-PUCRio requires more effort to achieve the desired thickness. Therefore, the process of obtaining thickness using the MPM-PUCRio software is more labourintensive and may involve additional steps when compared to DAN3D. Additionally it is concluded that the coefficient of friction of the contact method used in the MPM-PUCRio can influence the results of particle velocity. Regarding the Bingham viscoplastic model and its parameters, it can be concluded that they adequately represent the behaviour in the case of Deschaillons. The parameters used for this model will be confirmed by upcoming laboratory tests. Further analyses are also needed to confirm which of the methods allows the most accurate impact force estimation.

6

ACKNOWLEDGEMENTS

Special thanks are extended to the Ministry of Transportation (MTQ) for providing the databases and Fabricio Fernández author of MPM-PUCRio for provided excellent feedback and the authors for special technical advice. This research was carried out with the financial help of Cadre pour la prevention des sinistres du Gouvernement du Québec (CPS 21-22-21). 7

REFERENCES

Andersen, S. M., and Andersen, L. (2008). Modeling of landslides with the material point method. In Proceedings of the Sixth International Conference on Engineering Computational Technology. Civil-Comp Press. Bandara, S., Ferrari, A., & Laloui, L. (2016). Modelling landslides in unsaturated slopes subjected to rainfall infiltration using material point method. International Journal for Numerical and Analytical Methods in Geomechanics, 40(9), 1358-1380. Causes, L. (2001). Landslide types and processes. US Geological Survey: Reston, VA, USA. Ceccato, F., Simonini, P., di Prisco, C., and Redaelli, I. (2017). The effect of the front inclination on the impact forces transmitted by granular flows to rigid structures. In Advancing Culture of Living with Landslides: Volume 2 Advances in Landslide Science (pp. 593-599). Springer International Publishing. Conte, E., Pugliese, L., and Troncone, A. (2019). Postfailure stage simulation of a landslide using the material point method. Engineering Geology, 253, 149-159. Cuomo, S, and Martinelli, M. (2022). Empirical formulation for debris flow impact and energy release. Geoenvironmental Disasters, 9.1. Cuomo, S., Di Perna, A., and Martinelli, M. (2021). MPM modelling of buildings impacted by landslides. Understanding and Reducing Landslide Disaster Risk: Volume 5 Catastrophic Landslides and Frontiers of Landslide Science 5th, 245-266. Cuomo, S., Cascini, L., Pastor, M., and Petrosino, S. (2017). Modelling the propagation of debris avalanches in presence of obstacles. In Advancing Culture of Living with Landslides: Volume 3 Advances in Landslide Technology (pp. 469-475). Springer International Publishing. Cundall, P. A., and Strack, O. D. (1979). A discrete numerical model for granular assemblies. Géotechnique, 29(1), 47-65. Crozier, M. J., and Glade, T. (2005). Landslide hazard and risk: issues, concepts and approach. Landslide hazard and risk, 1-40. Cruden, D. M., and Varnes, D. J. (1996). Landslides: Investigation and Mitigation. Chapter 3 Landslides Types and Processes. Transportation research board special report, 247. Dai, Z., Huang, Y., Cheng, H., and Xu, Q. (2014). 3D numerical modeling using smoothed particle hydrodynamics of flow-like landslide propagation triggered by the 2008 Wenchuan earthquake. Engineering Geology, 180, 21-33.

Dai, Z., Huang, Y., Cheng, H., and Xu, Q. (2017). SPH model for fluid–structure interaction and its application to debris flow impact estimation. Landslides, 14, 917928. Provost, F., Locat, A., (2022). Estimation de la force d’impact des débris des glissements superficiels (PACC 20-21-02). Ministère de la sécurité publique du Québec. Fernández, F., (2020). Modelagem Numérica De Problemas Geotécnicos De Grandes Deformações Mediante O Método Do Ponto Material. Pontifícia Universidade Católica de Rio de Janeiro. https://doi.org/10.17771/pucrio.acad.52771 Geertsema, M., Blais-Stevens, A., Kwoll, E., Menounos, B., Venditti, J. G., Grenier, A., and Wiebe, K. (2018). Sensitive clay landslide detection and characterization in and around Lakelse Lake, British Columbia, Canada. Sedimentary Geology, 364, 217-227. Gingold, R. A., and Monaghan, J. J. (1977). Smoothed particle hydrodynamics: theory and application to nonspherical stars. Monthly notices of the royal astronomical society, 181(3), 375-389. Hungr, O., Morgan, G. C., and Kellerhals, R. (1984). Quantitative analysis of debris torrent hazards for design of remedial measures. Canadian Geotechnical Journal, 21(4), 663-677. Locat, J., and Demers, D. (1988). Viscosity, yield stress, remolded strength, and liquidity index relationships for sensitive clays. Canadian Geotechnical Journal, 25(4), 799-806. Lucy, L. B. (1977). A numerical approach to the testing of the fission hypothesis. Astronomical Journal, vol. 82, Dec. 1977, p. 1013-1024., 82, 1013-1024. Llano-Serna, M. A., Farias, M. M., and Pedroso, D. M. (2016). An assessment of the material point method for modelling large scale run-out processes in landslides. Landslides, 13, 1057-1066. McDougall, S., and Hungr, O. (2004). A model for the analysis of rapid landslide motion across threedimensional terrain. Canadian Geotechnical Journal, 41(6), 1084-1097. Nguyen, T. S., Yang, K. H., Ho, C. C., and Huang, F. C. (2021). Postfailure characterization of shallow landslides using the material point method. Geofluids,, 1-25. Peng, C., Li, S., Wu, W., An, H., Chen, X., Ouyang, C., and Tang, H. (2022). On three-dimensional SPH modelling of large-scale landslides. Canadian Geotechnical Journal, 59(1), 24-39. Soga, K., Alonso, E., Yerro, A., Kumar, K., and Bandara, S. (2016). Trends in large-deformation analysis of landslide mass movements with particular emphasis on the material point method. Géotechnique, 66(3), 248273. Squarzoni, G., Bayer, B., Franceschini, S., and Simoni, A. (2020). Pre-and post-failure dynamics of landslides in the Northern Apennines revealed by space-borne synthetic aperture radar interferometry (InSAR). Geomorphology, 369.

Sulsky, D., Chen, Z., and Schreyer, H. L. (1994). A particle method for history-dependent materials. Computer methods in applied mechanics and engineering, 118(12), 179-196. Troncone, A., Conte, E., and Pugliese, L. (2020). BackAnalysis of the Post-Failure Stage of a Landslide Using the Material Point Method. In Geotechnical Research for Land Protection and Development: Proceedings of CNRIG 2019 7 (pp. 265-272). Springer International Publishing. Tan, D. Y., Yin, J. H., Feng, W. Q., Zhu, Z. H., Qin, J. Q., and Chen, W. B. (2019). New simple method for calculating impact force on flexible barrier considering partial muddy debris flow passing through. Journal of Geotechnical and Geoenvironmental Engineering, 145(9), 04019051. Wendeler, C. (2016). Debris-flow protection systems for mountain torrents: basics principles for planning and calculation of flexible barriers. Xu, X., Jin, F., Sun, Q., Soga, K., and Zhou, G. G. (2019). Three-dimensional material point method modeling of runout behavior of the Hongshiyan landslide. Canadian Geotechnical Journal, 56(9), 1318-1337.

Numerical Simulation of Rock Bridge Failure Using Combined Finite-Discrete Element Method Yalin Li, Davide Elmo Norman B. Keevil Institute of Mining Engineering, The University of British Columbia, BC, Canada Omid Mahabadi Geomechanica Inc., Toronto, Ontario, Canada ABSTRACT The Bologna Interpretation of rock bridges by Elmo (2023) compared rock bridges to a spinning coin: you can only tell whether the coin is tail or head when it stops spinning. The spinning coin represents a stable rock mass, and rock bridges inside the rock mass can only be observed or measured when the rock mass has failed. Therefore, the rock bridge percentage approach to relating equivalent rock mass strength should not be used since the value of rock bridge percentage cannot be directly measured before the failure occurs. It has been discussed that rock bridge failure is a timedependent brittle failure process, and the mode of failure is directly related to rock block and joint strengths. This paper presents the results of predictive analysis for investigating the mechanisms of rock bridge failure using Parus rock (скала́ Па́рус) as a case study. A numerical model implementing the combined Finite-Discrete Element Method (FDEM) is used to capture the process of rock bridge failure. The simulation results show that the tensile cracks initiate at the point where the joint terminates. The initiated cracks propagate across rock blocks and coalesce with other induced or natural fractures forming rock bridges. The rock fragments exposed to the free surface slide along well-coalesced fractures leading to potential catastrophic failure. This study concludes that 1) rock bridge failure is a time-dependent process which was not considered in the previously developed mathematical equations for calculating rock bridge strength, and 2) both rock block and joint strength affect the modes of rock bridge failure.

1

INTRODUCTION

Rock bridges are generally defined as the portion of intact rock between discontinuity surfaces that can fail under certain stress conditions. The measurements of rock bridge percentage with respect to the equivalent rock mass strength have been initially studied for slope stability analysis since the early 1960s (e.g., Jennings, 1970, 1972; Jennings and Steffen, 1967; Terzaghi, 1962). The previous studies suggest that the rock bridge percentage can be calculated first based on the length/area of the intact portion relative to the length/area of the rock joint. The equivalent rock mass strength parameters (e.g., equivalent cohesion and friction angle) are then calculated based on the rock bridge percentage and intact rock strength. Those equivalent rock mass strength parameters can be used for limit equilibrium analysis. Recently, Elmo (2022) and Elmo et al. (2023) argued that the rock bridge percentage approach to relating rock mass strength should not be used, as the rock bridge percentage cannot be directly measured. According to the Bologna Interpretation (Elmo 2023), a rock bridge exists only when rock mass fails. Furthermore, Elmo (2023) pointed out that damage-related rock bridge failure is a time-dependent process, and numerical analyses using discrete element models with the incorporation of fracture mechanics principles should be conducted for better insights into potential rock bridge failure. The focus of this research is to study the mechanisms of rock bridge failure and its time-dependent behaviour. Predictive numerical analyses using the combined FiniteDiscrete Element Method (FDEM) are conducted to

explicitly capture the potential fracturing process and rock bridge failure within a jointed rock mass. This research uses the Parus rock (Sail rock) as a case study. This study's central objective is to understand how rock block and joint strength affect rock mass fragmentation and the mechanisms of rock bridge failure. 2

COMBINED FINITE-DISCRETE METHOD (FDEM)

ELEMENT

The combined Finite-Discrete Element Method (FDEM) is an innovative numerical technique that combines the Finite Element Method (FEM) and the Discrete Element Method (DEM) techniques, with the incorporation of fracture mechanics principles, to explicitly simulate progressive damage and failure process in brittle materials (Klerck 2000, Munjiza 2004, Mahabadi 2012, Elmo et al. 2013, Lisjak and Grasselli 2014). The simulation using FDEM starts with FEM analysis for continuum material, followed by DEM when the fractures are generated within the continuum material and newly formed discrete bodies begin to interact with each other. Figure 1 shows the intra-element and inter-element fractures that FDEM can generate within a meshed continuum. The intra-element fracture shown in Figure 1b is caused by splitting the existing meshed elements connected at the same failure point (Figure 1a). The failure plane is orthogonal to the direction of minor principal stress at the failure point (Figure 1b). The mesh coordinate and connectivity matrices are subsequently updated to insert this type of fracture. Note that the intra-element fracture generation scheme usually results in poor mesh quality

leading to smaller model time step size and computational instability (Elmo et al. 2013). The inter-element fracture shown in Figure 1c can be formed along boundaries of meshed elements in the direction most favourably oriented to the failure plane. In this case, the pattern of inter-element fractures is highly dependent on the mesh topology. FDEM analysis using the inter-element fracture generation scheme usually avoids instability when dealing with complex model and mesh geometries.

softening behaviour when the local stress exceeds the local crack element strength, while the meshed triangular/tetrahedral element remains elastic. The softening behaviour of the crack element is controlled by the values of Mode I (GfI) and Mode II (GfII) fracture energies. In the numerical program, a heuristic function by Evans and Marathe (1968) based on laboratory results controls crack element post-peak softening behavior. The residual stress on the yielded crack element gradually reduces with increasing opening/slipping displacement between elements. The broken crack element replaces the yielded crack element to form an open fracture when the relative displacement between two meshed elements reaches critical values, and stress reduces to zero. Using the crack element approach, the fracture mechanisms controlling rock bridge failure can be captured, and failure modes (i.e., Mode I, Mode II and Mode I-II) of generated fractures can be differentiated to study the mechanisms.

Figure 1. Fracture generation scheme in FDEM (after Klerck, 2000; Owen et al., 2005; figure redrawn by Lisjak and Grasselli, 2014): a) failure point and fracture orientation; b) intra-element fracture; and c) inter-element fracture. The commercially available FDEM code Irazu (Geomechanica Inc 2019) was used for this study. In Irazu, a continuum body is discretized by several triangular (2D) or tetrahedral (3D) elements bonded together at their contacts using four-noded (2D) or six-noded (3D) quadrilateral cohesive crack elements (Figure 2; Lisjak and Grasselli, 2014; Mahabadi, 2012). An open fracture is formed when two elements are fully separated under stress and yielded crack elements are replaced with broken elements. Since only inter-element fractures can be generated in Irazu code, the mesh topology is critical for capturing realistic fracture patterns. Figure 3. Constitutive model of a cohesive element in Irazu: a) Mode I; and b) Mode II. c) mixed Mode I-II (Tatone and Grasselli 2015, Geomechanica Inc 2019). 3

Figure 2. Representation of triangular/tetrahedral elements connected by crack elements in Irazu (Lisjak and Grasselli 2014). Figure 3 shows the constitutive behaviours of crack elements used by the Irazu code. Following fracture mechanics principles, Mode I, Mode II and Mode I-II are used to describe yielding/failure modes of the crack element in tension, shear and mixed tension-shear, respectively. The crack element yields and undergoes

NUMERICAL SIMULATIONS OF PARUS ROCK: A CASE STUDY

The Parus rock (also known as Sail rock) is a natural sandstone monolith of the late Cretaceous located on the shore of the Black Sea in Krasnodar Krai, Russian Federation (Elmo et al. 2022). The Parus rock is about 20 m long, 25 m high and 1.5 m wide. A naturally formed circular opening is located several meters above sea level near the NW corner. Figure 4 shows two visible joint sets on the SE face. One joint set is subvertical and persistently dipping 80 degrees toward NE. Rock joints in another joint set are shorter than the prior set and dip about 30 degrees toward SW. These two joint sets are approximately perpendicular to each other, forming a blocky rock mass. Furthermore, the interaction of non-persistent joints perfectly shows the existence of rock bridges. The Parus

rock shown in Figure 4 is used as a case study to investigate the potential rock bridge failure within a jointed rock mass.

Figure 4. Parus rock at SE face (photo from Google Earth shared under a creative commons license CC2.5). Figure 5a shows the deterministic Discrete Fracture Network (DFN) based on trace maps sketched from photos in Figure 4 and studies from Elmo (2023) and Elmo et al. (2022). The model was then meshed by 15,958 triangular elements with an average size of 0.1 m (Figure 5b). The mesh size was selected to ensure optimal mesh quality when discretizing complex DFN geometry. A smaller mesh size can be adopted for a better model resolution. However, this would significantly reduce the model time

step size and increase the model runtime (Tatone and Grasselli 2015). The element size of 0.1 m was found to balance the model runtime and results resolution. The base of the Parus rock model was fixed in both horizontal and vertical directions. In the model, only the gravitational force was applied. Table 1 lists three scenarios considered to investigate how rock block strength affects rock bridge failure. Since not all defects and natural fractures within rock blocks can be explicitly simulated in the numerical model, the conventional Hoek-Brown criterion was used to estimate the strength of the sandstone block at the scale of 1 m2. The Geological Strength Index (GSI) values of 70 and 65 (case B and C) were assumed for estimating the defected sandstone block strength. The equivalent cohesion and friction angle were used for the input parameters of the model (Elmo et al., 2022). Table 2 lists the input parameters for the numerical model based on estimated rock block strength. Note that the input parameters for the FDEM model listed in Table 2 are micro-properties (for meshed triangular and crack elements) and must be adjusted. Geomechanica Inc (2019) suggests that the elastic properties of triangular elements and strength properties of crack elements can be selected to be equal to those of sandstone blocks. The numerical parameter (i.e., penalties) values for crack elements need to be chosen as multipliers of the elastic properties of triangular elements. It is suggested that the normal and fracture penalty values of 10 times the triangular element Young’s modulus can be used. The tangential penalty value as a multiplier of triangular element Young’s modulus is suggested to be a function of mesh size as follows: 𝑛 = 15.29𝑥 −1.6 [1] where 𝑛 is the multiplier of Young’s modulus and 𝑥 is the average mesh size. The unconfined compressive tests on a homogeneous sandstone model were simulated. The results showed that the macroscopic strength and deformation of the sandstone model match the properties assigned to the mesh element and crack element by using the combinations listed in Table 2.

Figure 5. a) Deterministic DFN of Parus rock; and b) FDEM model mesh topology.

Table 1. Rock block properties for three different scenarios in this analysis. Model ID A B C

GSI 100 70 65

Erm (GPa) 20.5 15.1 13

crm (MPa) 10.4 1.6 1.2

σtm (MPa) 4.4 0.8 0.6

φrm (°) 62 62 62

Table 2. Input parameters for FDEM model.

Meshed triangular element

Crack element (cohesive for rock block)

Input parameters Density ρ Young’s modulus (E) Poisson’s ratio (v) Viscous damping factor Friction angle φ Cohesion c Tensile strength σt Mode I fracture energy GfI Mode II fracture energy GfII Normal penalty pn Fracture penalty pf Tangential penalty pt Friction angle φ Normal penalty pn

Value 2600 Erm 0.25

Unit kg/m3 Pa -

1

-

φrm crm σtm

° Pa Pa

(σt/6.88)2/E*

N/m

10GfI*

N/m

10Erm 10Erm 608.7Erm** 30 1E+11

Pa▪m Pa Pa/m ° Pa▪m

Crack element (frictional for rock Tangential penalty pt 1E+10 Pa/m joint) * Selection of the values is based on laboratory test results by Zhang (2002) and suggestions by Geomechanica (2019). ** Selection of the tangential penalty for the cohesive crack element is based on the suggestion by Geomechanica (2019) to ensure the frictional force can be fully mobilized.

4

SIMULATION RESULTS

Figure 6 shows the final failure modes and displacement contours for cases A, B and C. For case A, only the structurally controlled failure is captured. The rock blocks at SW and NE sides slide along the joints while no significant intact failure and rock bridge failure are captured. As the rock block strength decreases (cases B and C), more fractures are captured near SW and NE flanks leading to more unstable rock fragments. The degree of fracturing is related to the rock block strength. In the figure, the failure mode of those fractures is mainly Model I (tensile fracture). Figure 7 shows the failure process, and rock bridge failure for case B. Tensile fractures are initiated within the rock block at the area where the natural rock joints terminate, then propagate with increasing the model run time (effective time). Initiated fractures coalesce with adjacent induced fractures and pre-existing joints, resulting in rock bridge failure. The rock bridge failure was found in the area near SW and NE flanks, where blocks are exposed to the free surfaces. The rock fragments near SW and NE flanks slide along well-coalesced fractures with effective time, leading to potential catastrophic failure. This process was found to be time-dependent. Sensitivity analyses were conducted to investigate how joint tensile and shear strength affect rock bridge failure. It was found that the joint cohesive strength significantly affects the failure mode. As shown in Figure 8, the fractures are formed once the rock blocks near SW and NE flanks start to slide along the joints when the joint cohesions are 0 and 0.2 MPa. No rock bridge failures are captured if joint cohesion is increased to 0.5 MPa. The simulations found that the joint tensile strength has minimal effects on rock bridge failure.

Figure 6. Final failure modes and vertical displacement contours for simulations of cases A, B and C. Note that figures share the same legend.

Figure 7. Captured failure process for case B. Failure mode legend for fractures is referred to in Figure 6.

Figure 8. Final failure modes of case B with different joint cohesion values. Failure mode legend for fractures is referred to in Figure 6. 5

DISCUSSION AND CONCLUSION

This paper used a two-dimensional numerical model implementing FDEM to study the mechanisms and processes of rock bridge failure. It is found that both rock block and joint cohesive strength affect the degree of brittle fracturing and formation of rock bridges. In addition, the simulation results showed that rock bridge failure is a timedependent process. Tensile fractures are initiated when the non-persistent rock joints terminate, then fractures propagate and coalesce with the nearest stress-induced or natural fractures forming rock bridges. This process was not considered in the previous studies using probabilistic step path failure predictions using the DFN model and measurements of rock bridge percentage for slope stability analysis. It is suggested that numerical analysis using discrete element/combined finite-discrete methods should be used to understand the stability of fractured rock masses better. We caution against historical design methods presented in the literature based on measuring the so-called rock bridge percentage since it is impossible to measure entities that do not exist until failure has occurred.

6

ACKNOWLEDGEMENTS

The authors wish to acknowledge the MITACS grant in collaboration with the Centre of Innovation in Mineral Resource Engineering (CIMRE) and Newcrest Mining Limited for the financial support provided to this research. 7

REFERENCES

Elmo, D. 2023. The Bologna Interpretation of Rock Bridges. Geosciences, 13(2): 33. MDPI. Elmo, D., Stead, D., Eberhardt, E., and Vyazmensky, A. 2013. Applications of finite/discrete element modelling to rock engineering problems. International Journal of Geomechanics, 13(5): 565–580. American Society of Civil Engineers. Elmo, D., Tasnim, Z., Borgatti, L., and Marcato, G. 2022. The metaphysical nature of rock bridges and the challenge of measuring their conditional existence. Tucson, AZ, US. Evans, R., and Marathe, M. 1968. Microcracking and stress-strain curves for concrete in tension. Matériaux et Construction, 1(1): 61–64. Springer. Geomechanica Inc. 2019. Irazu Geomechanical Simulation Software. Toronto, Ontario, Canada.

Jennings, J. 1970. A mathematical theory for the calculation of the stability of slopes in open cast mines. In Planning open pit mines, proceedings, Johannesburg. AA Balkema Cape Town. pp. 87– 102. Jennings, J. 1972. An approach to the stability of rock slopes based on the theory of limiting equilibrium with a material exhibiting anisotropic shear strength. In Stability of Rock slopes. ASCE. pp. 269–302. Jennings, J., and Steffen, O. 1967. The Analysis of the stability of slopes in deep opencast mines. Civil Engineering= Siviele Ingenieurswese, 1967(3): 41–54. South African Institution Of Civil Engineering (SAICE). Klerck, P.A. 2000. The finite element modelling of discrete fracture in quasi-brittle materials. PhD Thesis, University of Wales Swansea. Lisjak, A., and Grasselli, G. 2014. A review of discrete modelling techniques for fracturing processes in discontinuous rock masses. Journal of Rock Mechanics and Geotechnical Engineering, 6(4): 301–314. Elsevier. Mahabadi, O.K. 2012. Investigating the influence of microscale heterogeneity and microstructure on geomaterials' failure and mechanical behaviour. PhD Thesis. Munjiza, A.A. 2004. The combined finite-discrete element method. John Wiley & Sons. Owen, D.R.J., Pires, F., de Souza Neto, E., and Feng, Y. 2005. Continuous/discrete strategies for the modelling of fracturing solids. Nato science series sub III Computer and systems sciences, 194: 230–266. Tatone, B.S.A., and Grasselli, G. 2015. A calibration procedure for two-dimensional laboratory-scale hybrid finite–discrete element simulations. International Journal of Rock Mechanics and Mining Sciences, 75: 56–72. doi:https://doi.org/10.1016/j.ijrmms.2015.01.011. Terzaghi, K. 1962. Stability of steep slopes on hard unweathered rock. Geotechnique, 12(4): 251– 270. Thomas Telford Ltd. Zhang, Z. 2002. An empirical relation between mode I fracture toughness and the tensile strength of rock. International journal of rock mechanics and mining sciences, 39(3): 401–406. Elsevier.

Comparison of geosynthetic reinforced soil wall solutions using analytical design methods and numerical modelling A. Moncada & S. Olivella Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya·BarcelonaTech (UPC), and International Centre for Numerical Methods in Engineering (CIMNE), Barcelona, Spain. I.P. Damians Department of Civil and Environmental Engineering, Universitat Politècnica de Catalunya·BarcelonaTech (UPC), International Centre for Numerical Methods in Engineering (CIMNE), and VSL International, Barcelona, Spain. R.J. Bathurst Department of Civil Engineering, Royal Military College of Canada, Kingston, Ontario, Canada. ABSTRACT European design standard (prEN-1997 202x) permits the use of suitably verified numerical models for the design of reinforced soil walls (RSW). The paper first compares three analytical design method outcomes (Coherent Gravity, Simplified, and Stiffness method) available in US (AASHTO 2020) and Canadian (CSA 2019) design codes. Polyester (PET) strap reinforcement arrangements from each method were then simulated using a 2D finite element model (FEM) which considered construction stages and transient compaction conditions. The models for each arrangement were analyzed using different material factors applied to the soil frictional strength. Serviceability limit states (SLS) and ultimate limit states (ULS) were evaluating via horizontal wall deformations, soil shear strains, and maximum reinforcement tensile loads. Numerical results remained within SLS and ULS criteria. The maximum tensile loads from the numerical models were close to, but lower than the predicted loads using the Stiffness Method, which in turn were lower than the loads predicted using the Simplified and Coherent Gravity methods. RÉSUMÉ La norme européenne de conception (prEN-1997 202x) autorise l'utilisation de modèles numériques dûment vérifiés pour la conception des murs en sol renforcé (RSW). L'article compare d'abord trois résultats de méthodes de conception analytiques (gravité cohérente, méthode simplifiée et méthode de rigidité) disponibles dans les codes de conception américains (AASHTO 2020) et canadiens (CSA 2019). Les arrangements de renfort de sangle en polyester (PET) de chaque méthode ont ensuite été simulés à l'aide d'un modèle d'éléments finis (FEM) 2D qui a pris en compte les étapes de construction et les conditions de compactage transitoires. Les modèles pour chaque disposition ont été analysés en utilisant différents facteurs de matériaux appliqués à la résistance au frottement du sol. Les états limites de service (SLS) et les états limites ultimes (ULS) ont été évalués par le biais des déformations de la paroi horizontale, des déformations de cisaillement du sol et des charges de traction maximales des armatures. Les résultats numériques sont restés dans les limites des critères SLS et ULS. Les charges de traction maximales des modèles numériques étaient les plus proches, mais inférieures aux charges prédites en utilisant la méthode de rigidité, qui à leur tour étaient inférieures aux charges prédites en utilisant les méthodes de gravité simplifiée et cohérente. 1

INTRODUCTION

Numerical models have proven to be useful tools to gain insight into the mechanical behaviour and performance of reinforced soil walls (RSWs) constructed with metallic and geosynthetic reinforcement materials (e.g., Damians et al. 2014; Huang et al. 2009; Yu et al. 2015a; among others). All design standards provide analytical methods to quantify and predict the behaviour of RSWs. Typically, numerical models are used to design structures that fall outside the scope of analytical methods (e.g., difficult foundation conditions, unusual wall geometries and very tall walls, and difficult or extreme loading conditions). The latest revision of the European design standard (prEN1997-3 202x) now allows numerical models to be used as a primary design

approach for walls that fall within and beyond the scope of current analytical models. The numerical models must include the wall construction stages and account for serviceability limit states (SLS) and ultimate limit states (ULS). A numerical model of a RSW requires careful selection of a single stiffness value for each reinforcement layer that accounts for continuous sheet reinforcement materials or discontinuous (strip) reinforcement depending on the type of RSW. For steel (inextensible) reinforcement, the selection of a single stiffness value is straightforward. However, the material behaviour of extensible polymeric reinforcement materials, such as polyolefin and polyester (PET) materials, is load-, time-, and temperaturedependent (e.g., Bathurst and Naftchali 2021).

Consequently, great care must be taken when choosing a unique stiffness value. An accepted method for both numerical and analytical approaches is to use the stiffness modulus corresponding to, for example, 2% strain and 1000 hours (AASHTO 2020; Allen and Bathurst 2019). This value can be obtained from isochronous stiffness curves based on creep curves constructed from laboratory product-specific testing, or approximations based on group data for different reinforcement types (Bathurst and Naftchali 2021). The present study provides the details of the numerical models and performance outcomes for three RSW walls with reinforcement layer arrangements resulting from three analytical methods that satisfy the recommendations in prEN-1997. The numerical modelling was carried out using the 2D finite element software package CODE_BRIGHT (Olivella et al. 1996). Staged construction was simulated. Numerical outcomes for SLS and ULS were compared with analytical design method predictions. ULS numerical simulations were carried out using the strength reduction method. 2

Three analytical methods were used to compute the maximum reinforcement load in each reinforcement layer under operational conditions (Stiffness method, Simplified method, and Coherent Gravity method). The reinforced soil material was assigned a friction angle of 38º, representing a triaxial peak friction angle, no cohesion, and unit weight of 18.9 kN/m3, for each design method. 2.1

The Stiffness method (Allen and Bathurst 2018) is empirically based and formulated for design of RSWs under operational conditions. The Stiffness method is applicable for inextensible (steel) and extensible (geosynthetic) type reinforcement types. In the latest edition of AASTHO LRFD Design Specifications (AASHTO 2020), the Stiffness method is restricted to extensible (polymeric) reinforcement products only. The maximum tensile load (Tmax) in each reinforcement layer is computed as: Tmax = Sv [H Dtmax + (Href / H) S] Ka fb g local c [1]

DESIGN CONSIDERATIONS

The reinforced soil wall in this paper is an idealized 10.5 mhigh structure with discrete precast concrete facing panels seated on polymeric bearing pads between the horizontal joints, and attached to PET strap soil reinforcement layers. The bottom wall facing panel is seated on a concrete levelling pad. Reinforcement layers have a uniform vertical spacing of 0.75 m. The wall face is embedded to a depth of 0.525 m (i.e., 5% of wall height). The PET strap reinforcement layers have a length of 7.35 m (i.e., 0.7 of wall height) and a coverage ratio of 0.32 (i.e., 9 cm-wide straps, 2 straps per connection, 4 connections per 2.25 m wide panel). A constant (dead load) uniform surcharge of 25 kPa was placed over the entire width of the backfill soil. The properties that were varied were stiffness and ultimate tensile strength (UTS) of the reinforcement. The UTS (i.e., grade) of the reinforcement straps were 25, 30, and 40 kN/strap. No reinforcement straps above grade 40 were required. Reinforcement stiffness values were estimated at 2% strain and 1000 hours using the power law formulation proposed by Bathurst and Naftchali (2021) for PET straps and group data for different products falling within the PET strap category. Stiffness values for grades 25, 30, and 40 were 197, 231, and 316 kN/strap, respectively. These values are in accordance with laboratory data from product-specific creep data made available to the authors (Hang-Won and Je-Goo 2020). The design of RSWs includes assessment of external and internal stability limit states. External stability limit states include overturning, sliding, foundation bearing capacity, and global and compound stability modes of failure (as detailed in prEN-1997 202x). For internal stability, the maximum tensile load that each reinforcement layer must carry is determined. Next pullout, connection rupture, and reinforcement rupture or over-stressing limit states must be examined. The present study is focused on internal stability limit states only.

Stiffness method

Here Sv is the tributary (vertical) spacing of the reinforcement layer,  is soil unit weight, H is wall height, Dtmax is a load distribution factor with depth below the crest of the wall, Href = 6 m is a reference height value, S is the equivalent height of average soil surcharge acting on the reinforced soil zone, and Ka is the active earth pressure coefficient (Ka = (1−sin )/(1+sin )), where  is the soil friction angle. The remaining parameters, fb, g, local and c are dimensionless influence factors for wall facing batter, global reinforcement stiffness, local stiffness, and soil cohesion, respectively. Connection strength, reinforcement tensile strength and pullout strength are treated as ultimate limit states in North American practice. In order to keep the reinforced soil zone at operational (working stress) conditions the Stiffness method requires that the soil in the reinforced soil mass not develop a contiguous shear failure zone through the height of the wall. This criterion is met by limiting the maximum strains in the reinforcement layers wall to a prescribed value. This so-called “soil failure” limit state is treated as a serviceability limit state. 2.2

Simplified method

The Simplified method appears in Canadian design practice (CSA 2019) and as a permitted legacy method in the USA code (AASHTO 2020). It is applicable for both extensible and inextensible reinforcement types. The maximum tensile load in each reinforcement layer is computed as follows: Tmax = Sv Ka v

[2]

Here, v is the vertical earth pressure at the reinforcement layer elevation including the contribution of any uniform distributed surcharge. 2.3

Coherent gravity method

The third method used to calculate reinforcement maximum tensile load is the Coherent Gravity method (CGM) which is used for RSWs constructed with inextensible steel reinforcement. The maximum tension in each reinforcement layer is calculated as follows: Tmax = K Sv [v L / (L – 2 e)]

[3]

Here, K is a dimensionless earth pressure coefficient that varies linearly with depth, starting at K = K0 = 1−sin  at the top of the wall, to a fixed value of K = Ka at 6 m of depth and beyond, L is the reinforcement length, and e is the eccentricity caused by the retained soil mass acting at the back of the reinforced soil mass. Current design codes, such as CSA (2019) in Canada, AASTHO (2020) and BS8006-1 (2016) limit the CGM to walls with inextensible reinforcement. Miyata et al. (2018) reviewed instrumented and monitored PET strap walls constructed with modern PET strap reinforcement materials and demonstrated that these structures fall within the extensible geosynthetic category based on backcalculated global reinforcement stiffness (Sg), which is calculated as the sum of reinforcement layer stiffnesses divided by the wall height. Nevertheless, their review recorded that some designers have used the Coherent Gravity method for PET strap walls because of their relatively high stiffness compared to polymeric sheet reinforcement products. 2.4

Limit state criteria verifications

In the most recent draft of the prEN-1997 (202x), the use of numerical models is permitted as a primary design methodology, rather than simply a complementary method to verify designs using analytical methods. To use numerical models for RSW design they must be shown to have been used for similar problems (i.e., verified). SLS and ULS conditions must be assessed at each construction stage, as well as at end of construction conditions. The model must use material property values adjusted by codespecified material factors (i.e., non-reduced values for SLS and strength-reduced properties for ULS). Each stage is deemed safe if displacements and reinforcement loads are within prescribed limits. According to Allen and Bathurst (2013) a contiguous internal failure mechanism through the height of the reinforced soil mass in a geosynthetic RSW is expected to occur when strains in the reinforcement exceed 2-3%. Tolerable horizontal displacements will vary between different design guidelines (Bathurst et al. 2010). In this work, a maximum value of 0.5% of the structure height was adopted (BS 8006-1 2016). For ULS design, a material factor (m) must be applied to the soil strength parameters to reduce the soil strength (i.e., a reduction of tan() and c). If equilibrium conditions

are met (i.e., no structural collapse occurs), then the limit state conditions are satisfied. prEN-1997 (202x) defines a material factor of m = 1.25 for ULS conditions. The present study also uses m = 2.00 to examine the influence of weaker soil on numerical model outcomes. The backfill material was assigned a cohesion of 1 kPa to avoid numerical convergence problems in near-surface elements. Consequently, the material factor was applied to the friction angle only. 2.5

Analytical model designs

In the analytical models the friction angle of the soil was not reduced (i.e., m = 1.00). The lowest grade for reinforcement considered in this study was 25. The Stiffness method resulted in a design with all grade 25 reinforcement layers. A better optimized design would require lower grade reinforcement strips which are not available from the product line assumed in this study. Reinforcement arrangements are summarized in Table 1. Table 1. Reinforcement grade distribution (from bottom to top) for each design method. Reinforcement arrangement

Design method 1

2

3

4

5

6-14

A

Stiffness method

25

25

25

25

25

25

B

Simplified method

40

30

30

30

25

25

C

Coherent Gravity method

40

40

40

30

30

25

3

Layer/Grade

NUMERICAL MODEL

Numerical simulations were carried out using the finite element (FE) program CODE_BRIGHT (Olivella et al. 1996). The FE mesh geometry, material zones and structural components are shown in Figure 1. Soil materials (i.e., backfill, front embedment, foundation, and interfaces) were modelled using an elasticplastic constitutive model with dilatancy. Dilatancy angles were assumed as  =  – 30º. The reinforced soil friction angle of 38º representing a triaxial peak friction angle value could be reasonably increased up to 1.1 to 1.2 times due to the plane-strain conditions assumed in the numerical model (Kulhawy and Mayne 1990). However, for simplicity and to keep the friction angle below 40 degrees, as recommended in AASHTO (2020), the friction angle of 38 degrees was used for both numerical and analytical solutions. To simulate the use of lower compaction energy equipment near the facing panels, as recommended in construction practice, a lower elastic modulus value was used for the soil within 1 m of the back of the wall facing (see Table 2). The foundation stiffness was selected to represent a competent and stiff foundation.

the embedded reinforcement elements, are constructed. The placement of the next facing panel and soil layer is repeated until the full wall height is achieved. A transient compaction stress of 5 kPa was applied uniformly over the soil layers. The front toe embedment was applied once the wall reached a height of 4.5 m (i.e., three panel-height). Concrete elements and bearing pads were modelled using a linear elastic model. Facing panels and the levelling pad were assigned Young’s moduli of 32 and 25 GPa, respectively, and Poisson’s ratio of 0.2. Bearing pads comprised of two or four units per panel width were placed at the horizontal joints between panels, resulting in pad layer stiffness of 3.3 and 6.6 kPa, respectively. Two pads were placed between the top four panels (i.e., above 6 m of height), and four pads between the bottom three panels (Damians et al. 2016).

Ri = [(Ri(strip) Astrip) + (Ri(soil) Asoil)] / Alayer

[4]

Here, the soil-soil surface at each reinforcement layer remains unchanged (i.e., reduction factor of Ri(soil) = 1) while the soil-reinforcement contact reduction factor is set to Ri(strip) = 0.67. For the soil-reinforcement interface Ri is applied to the strength parameters. The calculation details for the elastic modulus interface parameters in Table 2 can be found in the papers by Damians et al. (2015, 2022) and Yu et al. (2015b). Reinforcement elements were modelled using a linear elastic model, in which straps are represented by a continuous layer with equivalent stiffness considering the reinforcement coverage ratio and the reinforcement stiffness values used in the analytical designs. Global reinforcement stiffness values (Sg) ranged from 0.93 to 1.08 MPa and fall within the range of 0.79 to 1.96 MPa for typical instrumented PET strap walls reported by Miyata et al. (2018). In the numerical model, the simulated construction of the foundation and levelling pad is carried out first. Second, the first concrete panel is constructed. Third, the first 1.5 m layer of reinforced and retained backfill material, including

36

5

6

100

Backfill soil

38

1

8

10 or 20

0.3

25.1

0.6

0

4.02

0.45

34.8

0.89

8

20

0.45

Soil-facing interface Soil-reinforcement interface

4

Poisson’ s ratio [-]

Dilatancy angle,  [º]

Foundation soil

Material

Young modulus, E [MPa]

Cohesion, c [kPa]

Soil-facing and soil-reinforcement interfaces were modelled using a continuum element approach. This methodology has been successfully used in the past for RSW finite element models when zero-thickness elements are not readily available (Damians et al. 2021, 2022). Interfaces are defined using a reduction factor (R i). The soil-facing interface was assigned Ri = 0.6 applied to the strength (i.e., tan() and c) and stiffness parameters (i.e., E). For the soil-reinforcement interface, the contact area of the reinforcement strip and soil (Astrip), the contact area of the soil-soil interaction (Asoil), and the total contact area at the reinforcement layer elevation (Alayer) were taken into account to obtain equivalent interface parameters for the 2D numerical model, hence:

Friction angle,  [º]

Table 2. Soil and interface property values used in numerical model.

Figure 1. 2D finite element mesh geometry.

0.3

RESULTS

Figure 2 shows the horizontal outward displacements during and after the construction process using the reinforcement arrangement A (Stiffness method) in Table 2). From the beginning of construction, horizontal displacements form an outward curved distribution, centred at about the middle of the structure (Fig. 2a, 2b), and moving to about the bottom third of the wall height as the structure is completed (Fig. 2c). The application of a surcharge results in further horizontal displacements (Fig. 2d). Maximum horizontal displacements were within 0.19% of the structure height (i.e., 2 mm) after the application of the 25 kPa surcharge. As noted earlier, a maximum horizontal displacement of 0.5% of H was assumed to satisfy the SLS criterion. This criterion was met at all construction stages using the three reinforcement layer arrangements. As the material factor is increased, a failure mechanism can be observed based on shear strains within the reinforced soil mass (Fig. 3) starting near the toe of the vertical wall. As the material factor increases, shear strains increase to approximately 1.2% at the bottom of the reinforced soil zone. In this region a linear failure zone can be imagined consistent with expectations using active earth pressure theory applied to extensible reinforced soil masses (dashed line in Fig. 3). Failure is expected to occur

(a)

(b)

(c)

(d)

Figure 2. Horizontal displacements for (a) 3-, (b) 5-, and (c) 7-panel height, and (d) after surcharge is applied using reinforcement arrangement A from the Stiffness method design and SLS condition (m = 1) as per prEN-1997 (202x). Note: scale legend is [m]. when shear strains greater than 2% form a contiguous zone through the reinforced fill. For the ULS limit state condition (m = 1.25), shear strains do not exceed 1.5%. For a material factor of m = 2.00, higher shear strains in the range of 1.25 to 1.5% are present but a contiguous failure mechanism has not developed. While not shown here, the tensile strains in the reinforcement were also well below values of 2-3% that are symptomatic of a contiguous shear zone through the reinforced soil zone as explained earlier. Not unexpected, the largest shear strains occur in the bottom third of the wall where horizontal wall displacements are largest. Figure 4 shows the horizontal displacement profile of the wall facing with increasing material factors applied to tan(). Results are shown using the reinforcement design with arrangement A. The ULS numerical simulation using m = 1.25 results in a slight increase in horizontal displacements with respect to the base case (m = 1.00), nevertheless, displacements are within the SLS displacement criterion. To encourage the development of possible failure mechanisms in the reinforced soil mass, the material factor was increased to m = 2.00. The result was further outward displacement as great as 0.28% of the wall height. As before, there is a clear bulging shape over the bottom third of the structure, with the two bottom panels tilting the most of all panels. Negligible displacements were observed at the foot of the wall (i.e., 3 mm), which can be attributed to the front embedment acting as a horizontal restraint. Figure 5 shows the maximum tensile load in each reinforcement layer using the reinforcement arrangements

for the three analytical design methods. The Stiffness method reinforcement arrangement gives much lower reinforcement tensile loads in all reinforcement layers. The reinforcement arrangements from the Simplified and Coherent Gravity methods result in higher predicted loads, particularly at the base of the structure. Miyata et al. (2018) compared predicted loads using the same three design methods with measured values from full-scale instrumented wall structures. Their results showed that the Simplified method and the CGM lead to safe designs by over-predicting reinforcement loads, particularly for the lower layers, and most noticeable using the CGM. On the other hand, the Stiffness method gave more accurate load predicts when compared to measured values. The numerical model results using the same reinforcement scheme as the Stiffness method can be seen to fall below the values from the analytical Stiffness method. It is interesting to note that the linear trend over approximately the top half of the wall appears in both the analytical and numerical results. However, over the bottom half of the wall the numerical results do not follow the constant load trend predicted by the analytical model. In fact, the K-stiffness method (Allen et al. 2003; Bathurst et al. 2005) which preceded the current Stiffness Method recommended that the load distribution function (Dtmax in Eq. 1) follow a trilinear distribution consistent with observed (measured) loads that were attenuated near the bottom of the walls due to the contribution of the foundation to carry some of the lateral soil load acting on the wall facing. This leg of the Dtmax distribution was discontinued for the current Stiffness method to ensure that there is enough reinforcement load capacity over the bottom of the wall

m = 1.00

m = 1.25

m = 2.00 Figure 3. Numerical model shear strains after surcharge application using different material factor values applied to the soil friction angle together with reinforcement arrangement A.

Figure 4. Horizontal displacement profiles at the wall facing for numerical models with different material factor values applied to the soil friction angle and using the reinforcement arrangement A from the Stiffness method design. Note: the datum for this plot is the toe of the wall at the end of construction. should a compound failure mechanism develop through the reinforced soil zone close to the foundation. Figure 6 shows maximum tensile loads from numerical models considering SLS (m = 1) and ULS (m = 1.25) conditions. The top seven layers for each model case are very similar (i.e., using reinforcement arrangements for

Figure 5. Calculated maximum reinforcement tensile loads using the three analytical design methods and the numerical model results with unfactored soil strength (m = 1.00) and reinforcement arrangement A.

Figure 6. Maximum reinforcement tensile loads from numerical model for (a) SLS (m = 1) and (b) ULS (m = 1.25) cases. Stiffness, Simplified and Coherent Gravity method designs). As expected, larger differences appear in the bottom half of the structure where the reinforcement layer grades vary the most between model arrangements. Higher grade reinforcement layers generate higher loads because they are stiffer. Increasing the material factor to the value required by prEN-1997 results in a detectable but small increase in reinforcement tension, particularly in layers 2 to 7. The differences are not of practical concern for design. The lowest reinforcement UTS is approximately 25 kN/strip. This means that less than 10% of the unfactored rupture capacity of the reinforcement layers is mobilized and thus the ultimate tensile strength limit state is not exceeded. Assuming an overall reduction factor RF = 3 applied to ultimate tensile strength (Tult) to account for creep, durability and installation strength reduction mechanisms as in North American practice, the allowable tensile strength (Tal = Tult/RF) is also not exceeded. Figure 7a shows the magnitude and distribution of maximum loads in each reinforcement layer using reinforcement arrangements A, B and C and material factor m = 2.00 applied to tan  of the soil. Even for this larger material factor, reinforcement loads are less than the computed values using the analytical Stiffness method. The differences between data sets are less than 1.0 kN/strap, which is judged to be negligible for practical design purposes. Figure 7b shows the location of the maximum tensile load in each reinforcement layer. Superimposed on this figure are the assumed failure surfaces using the three analytical methods found in design codes. The Simplified and Stiffness methods are based on active earth pressure theory with the failure surface propagating up from the toe of the wall at 45º +  / 2 from the horizontal. The bilinear curve is used for inextensible (steel) reinforcement and the Coherent Gravity method. The latter can be argued to be a

(a)

(b)

Figure 7. (a) Maximum tensile load in each reinforcement layer, and (b) maximum load location versus normalized distance from the wall face using m = 2 and the three reinforcement arrangements. better fit for all three reinforcement arrangements using the results of the numerical model. The location of these internal failure surfaces is only of interest to analytical methods because they are used to calculate the reinforcement embedment length in the analytical method calculations for the pullout limit state. In compliance with prEN-1997 guidelines for numerical modelling, structure instability was not observed using m = 1.25 for ULS checks for all three cases. Increasing this material factor to m = 2.00 resulted in greater horizontal displacements, shear strains, and reinforcement loads, but model stability was not compromised. It should be noted that global instability and bearing capacity or sliding failure were not observed. This is ascribed to the high rigidity of the foundation (E = 100 MPa). Horizontal displacements at the base of the wall were approximately 3 mm and were similar for all reinforcement arrangements and m cases; thus, the limit state for an external base sliding failure mechanism was not a concern. This can be attributed to the front foot embedment, which includes the levelling pad and part of the first segmental panel, whose passive resistance act as a restraint at the base of the wall. Overturning about the toe was not observed which is, regardless, not unexpected for relatively flexible geosynthetic reinforced soil walls. The European standard requires the verification of all failure modes, while, in AASHTO (2020) and CSA (2019) codes in North America, the external stability overturning limit state is not considered for design. The local horizontal wall displacements were small and thus an internal wall face bulging failure limit state was not a concern. 5

CONCLUSIONS

The most recent revision of prEN-1997 allows the use of a verified numerical model to be used for the design of

geotechnical structures. The present study describes an initial effort to examine whether or not selected limit states that appear in the latest European design guidelines are satisfied using the results of a numerical model. The numerical model uses reinforcement arrangements and PET strap reinforcement materials determined from analytical models using the Stiffness method, Simplified method and the Coherent gravity method (CGM). Staged construction was used in each simulation including the influence of transient compaction equipment loading. Serviceability limit state (SLS) conditions were verified using unfactored soil shear strength (material factor m = 1.00). Ultimate limit state (ULS) conditions were checked with a soil strength reduction material factor of m = 1.25 and 2.00. The main findings are as follows: •

• •

•

•

•

For the reinforcement grade range considered (minimum grade of 25), the Stiffness method provided the most optimized design (lowest maximum tensile load in each layer), with all layers of grade 25. The Simplified method and CGM required higher grade reinforcement for the bottommost layers. Numerical model results were in closest agreement with the Stiffness method predictions for the maximum reinforcement load at end of construction. No excessive deformations were observed at any construction step for the three numerical models, and thus the numerical model satisfied SLS criteria (i.e., verification). Horizontal displacements remained less than 0.25% of the structure height at every construction stage. Maximum reinforcement tensile loads for all three reinforcement arrangements did not exceed the values computed using the analytical Stiffness method. No structural failure or numerical issues were observed using a material factor of 1.25; thus, the numerical model outcomes satisfied code-specified ULS criteria. Reinforcement loads did not exceed 10% of the ultimate tensile strength (UTS), meaning that reinforcement rupture is not expected. Increasing the material to m = 2.00 resulted in incrementally larger horizontal displacements and shear strains, but not enough to generate failure mechanisms. The onset of a potential reinforced soil failure mechanism in proximity to the toe of the wall was observed in the numerical model using the highest material factor m = 2.00. However, the distribution of maximum soil shear strains and the peak strain in the reinforcement layers were not great enough to suggest ULS conditions for the internal stability limits states investigated. No evidence of global, compound or external stability failure mechanisms was observed. This is largely attributed to the large foundation stiffness and the front footing embedment.

Reinforced soil wall structures are large deformation problems, with more than one potential failure mechanism (limit state). Consequently, all potential limit states must be investigated. Numerical models, as permitted by prEN-

1997 for design, if properly used and verified, facilitate design for both serviceability and ultimate limit states. Likewise, where project conditions fall within the scope of analytical methods, such as the Simplified, Stiffness and Coherent Gravity methods, numerical models are a useful complementary tool to gain insight on wall performance features that cannot be easily evaluated using these analytical models (e.g., wall deformations and strains). ACKNOWLEDGEMENTS The authors wish to thank Aaron Kim from GECO Industrial (Korea, Rep) for providing data for polymeric straps (FASTEN products) from reliability assessment testing records. The authors wish to acknowledge the support of the Department of Civil and Environmental Engineering (DECA) of the Universitat Politècnica de Catalunya·BarcelonaTech (UPC) and the International Centre for Numerical Methods in Engineering (CIMNE) and the funding received from the Spanish Ministry of Economy and Competitiveness through the “Severo Ochoa Programme for Centres of Excellence in R&D” (CEX2018000797-S-20-4). REFERENCES AASHTO. 2020. LRFD Bridge Design Specifications, 9th Ed. American Association of State Highway and Transportation Officials (AASHTO), Washington, DC, USA. Allen, T.M. and Bathurst, R.J. 2013. Comparison of working stress and limit equilibrium behavior of reinforced soil walls. In Sound geotechnical research to practice: Honoring Robert D. Holtz II (pp. 499-513). Allen, T. M. and Bathurst, R.J. 2018. Application of the simplified stiffness method to design of reinforced soil walls. Journal of Geotechnical and Geoenvironmental Engineering, 144(5), 04018024. Allen, T.M. and Bathurst, R.J. 2019. Geosynthetic reinforcement stiffness characterization for MSE wall design. Geosynthetics International, 26(6), 592-610. Allen, T.M., Bathurst, R.J., Walters, D.L., Holtz, R.D. and Lee, W.F. 2003. A new working stress method for prediction of reinforcement loads in geosynthetic walls. Canadian Geotechnical Journal, 40(5), 976-994. Bathurst, R.J., Allen. T.M. and Walters, D.L. 2005. Reinforcement loads in geosynthetic walls and the case for a new working stress design method. Geotextiles and Geomembranes, 23(4), 287-322. Bathurst, R.J., Miyata, Y., and Allen, T.M. 2010. Facing displacements in geosynthetic reinforced soil walls. In Earth Retention Conference 3 (pp. 442-459). Bathurst, R.J and Naftchali, F.M. 2021. Geosynthetic reinforcement stiffness for analytical and numerical modelling of reinforced soil structures. Geotextiles and Geomembranes, 49(4), 921-940. BS 8006-1: 2010 + A1. 2016. Code of practice for strengthened/reinforced soils and other fills. BSI, Milton Keynes, UK.

CSA. 2019. Canadian Highway Bridge Design Code. CAN/CSA-S6-19. Canadian Standards Association (CSA), Mississauga, Ontario, Canada. Damians, I.P., Bathurst, R.J., Josa, A., and Lloret, A. 2014. Numerical study of the influence of foundation compressibility and reinforcement stiffness on the behavior of reinforced soil walls. International Journal of Geotechnical Engineering, 8(3), 247-259. Damians, I. P., Bathurst, R. J., Lima, J., Lloret, A., & Josa, A. 2015. Numerical study of the use of activelytensioned polymeric strips for reinforced soil walls. Geotechnical Engineering for Infrastructure and Development. January 2015, 3833-3838. Damians, I. P., Bathurst, R. J., Lloret, A., and Josa, A. 2016. Vertical facing panel-joint gap analysis for steelreinforced soil walls. International Journal of Geomechanics, 16(4), 04015103. Damians, I.P., Bathurst, R.J., Olivella, S., Lloret, A., and Josa, A. 2021. 3D modelling of strip reinforced MSE walls. Acta Geotechnica, 16(3), 711-730. Damians, I.P., Olivella, S., Bathurst, R.J., Lloret, A., and Josa, A. 2022. Modeling soil-facing interface interaction with continuum element methodology. Frontiers in Built Environment, 8, 842495-1. Huang, B., Bathurst, R.J., and Hatami, K., 2009. Numerical study of reinforced soil segmental walls using three different constitutive soil models. Journal of Geotechnical and Geoenvironmental Engineering 135 (10), 1486–1498. Hang-Won, C. and Je-Goo, J. 2020. Reliability assessment of polymeric straps (FASTEN FS E) for soil reinforcement. M213-19-15189. GECO Industrial Co., Ltd. FITI Testing and Research Institute, Chungbuk, Korea. prEN 1997-3. 202x. Eurocode 7: Geotechnical Design — Part 3: Geotechnical Structures. F.E. with agreed CRs (v2022:5). Technical Committee CEN/TC 250 “Structural Eurocodes”. Miyata, Y., Bathurst, R.J., and Allen, T. M. 2018. Evaluation of tensile load model accuracy for PET strap MSE walls. Geosynthetics International, 25(6), 656-671. Olivella, S., Gens, A., Carrera, J., and Alonso, E.E. 1996, 'Numerical Formulation for a Simulator (CODE_BRIGHT) for the Coupled Analysis of Saline Media '' Engineering Computations, 13(7), 87-112. Kulhawy, F.H. and Mayne, P.W. 1990. Manual on estimating soil properties for foundation design. Report EL-6800, Electric Power Research Institute (EPRI). Palo Alto, California, 306p. Yu, Y., Bathurst, R.J., and Miyata, Y. 2015a. Numerical analysis of a mechanically stabilized earth wall reinforced with steel strips. Soils and Foundations, 55(3), 536-547. Yu, Y., Damians, I.P., and Bathurst, R.J. 2015b. Influence of choice of FLAC and PLAXIS interface models on reinforced soil-structure interactions. Computers and Geotechnics 65, 164-174.

Instrumentation and Deformation Measurement of Charlotte County Culvert No. 2. Using Shape Array Technology Mohammad Rezania, Othman Nasir, Arun Valsangkar University of New Brunswick, Fredericton, New Brunswick, Canada Joe MacDonald NBDTI, Fredericton, NB, Canada

ABSTRACT A reliable technique for the measurements of short and long term deformation response of corrugated steel culvert pipes (CSCP) is an essential tool to assess the structural integrity and condition of CSCP. Shape Array method is one of the promising techniques that has the potential for CSCP deformation monitoring. In this work, the load-deformation response of single CSCP is experimentally investigated with Shape Array system during a full-scale load test performed on an existing CSP on Route 127 Culvert No.2, in Charlotte County, southwest New Brunswick. The results showed that Shape Array method was capable of providing accurate and robust monitoring .The deformations were also independently monitored using LiDAR. The Shape Array measurements agreed with deformation data collected using the LiDAR technique. RÉSUMÉ (Change the French version as noted above) Une technique fiable pour mesurer la réponse aux déformations à court et à long terme des tuyaux de ponceau en acier ondulé (CSCP) est un outil essentiel pour évaluer l'intégrité structurelle et l'état des CSCP. La méthode de l'array de formes est l'une des techniques prometteuses qui présente un potentiel pour la surveillance des déformations des CSCP. Dans cette étude, la réponse charge-déformation d'un seul CSCP est examinée expérimentalement à l'aide du système de l'array de formes lors d'un essai de charge à grande échelle réalisé sur un CSP existant sur le ponceau n°2 de la route 127, dans le comté de Charlotte, au sud-ouest du Nouveau-Brunswick. Les résultats ont montré que la méthode de l'array de formes était capable de fournir des capacités de surveillance précises et robustes. Les mesures de l'array de formes étaient en accord avec les données de déformation collectées à l'aide de la technique LiDAR.

1

INTRODUCTION

For more than a century, corrugated steel pipe (CSP) has been the preferred option for engineers and used in many projects. With its wide variety of coatings, corrugation patterns, and wall thickness, CSP offers dependable durability. The flexibility and value of CSP are unmatched by any other pipe material on the market today (Wagener and Leagjeld, 2014). Corrugated steel pipe offers a variety of environmental advantages, such as lower greenhouse gas emissions, lower acid rain, and a reduction in the depletion of resources required in manufacturing, such as water and electricity. CSP is produced in a variety of forms and sizes (National Corrugated Steel Pipe Association, 2018), with diameters ranging from 15 centimeters to more than 15 meters and up to 30 meters of fill height. CSP is a reasonably priced product that can be installed quickly even during extreme weather conditions because it is not much susceptible to climatic variability or moisture. A material's durability refers to its ability to resist against deterioration brought on by corrosion, abrasion, applied loads, and installation techniques. A number of factors, such as soil resistivity, acidity (pH), moisture

content, soluble salts, and oxygen content, affect how long steel pipe will last in soil. The majority of soils have a pH between 6.0 and 8.0, which is recognized as neutral and is an acceptable range for steel pipe durability, but acidic soils, which are more frequently found in areas with considerable rainfall, have lower pH values and tend to be more corrosive. The amount of moisture in the soil can also have an impact on CSP's durability. Moreover, Granular soils that allow the culvert’s backfill to drain quickly increase durability, and low moisture content soils are often not corrosive to CSP. High clay content soils are more corrosive than well-drained soils because they tend to hold water for longer periods of time (Wagener and Leagjeld, 2014). All these factors cause the CSP to degrade and deform, effecting the stability and integrity of CSP structure Wagener and Leagjeld, 2014; García and Moore, 2015). A common technique for determining the structural integrity of CSP is to perform full scale load test of the culverts using a loaded truck, moving or stationary. The stationary load test requires positioning a truck with a known load on top of the culvert at specific spots, and for moving loading tests, it involves moving the truck over the culvert at a predetermined speed. The performance of the culvert in responding to the load test is then measured and evaluated using variety of tools, like displacement

transducers, fibre optics, shape array (SAA) or light detection and ranging laser scanning survey method (LiDAR) cameras. The main objective of this research was to determine the effectiveness of using SAA and LiDAR survey methods to monitor the performance of CSP. In this project, SAA and LiDAR were used to record displacements of Culvert No. 2 on Route 12, southwest New Brunswick.

2

SITE DESCRIPTION

The investigated (CSP) culvert is in Charlotte County, southwest New Brunswick region, approximately 100 km southwest of the City of Fredericton as shown in Figure 1. This culvert was selected for this study by New Brunswick Department of Transportation and Infrastructure (NBDTI) as the culvert was under an ongoing rehabilitation using mortar lining system (NBDTI, 2022). Culvert No. 2 crosses Route 127 with approximately 45˚ degree angle, with a diameter of 1.55 m and an invert length of approximately 34 m and a slope of 4.1% (north to south).

Figure 1. Site Location (Google Earth Image) The streambed to roadbed depth at the road centerline is about 4.4 meters, with a depth of soil above the culvert of up to 2.8 meters as shown in Figure 2 (NBDTI, 2022).

Figure 2. Plan and Section of Culvert No. 2 on Route 127 (NBDTI, 2022)

The culvert corrugate steel plate shown in Figure 3, has thickness of 5 mm, and the corrugations are defined by a pitch of 125 mm (5˝) and a depth of 25 mm (1˝). Figure 3 shows that the culvert faces a significant corrosion at the base of the culvert.

types of SAA deformation sensors, including wire-based sensors, fiber optic sensors, and piezoelectric sensors. Each type has its own advantages and disadvantages, depending on the specific application. One of the main limitations of shape array deformation sensors is their susceptibility to damage or failure under certain conditions, such as high strain rates or extreme temperatures. In addition, the accuracy of the measurements may be affected by the position and orientation of the sensors.

Figure 3. CSP details at the south exit of the culvert.

Figure 4. Shape Array (Measurand, 2023)

3

EXPERIMENTAL PROGRAM

The experimental program methodology included using the shape array system in deformation measurements under load test, and perform a validation measurements using LiDAR scanning. This methodology was achieved by doing the following main tasks: 3.1

In this work, the SAA segments (Measurand, 2023) are installed under the right lane of the road from north to south, 14.75 meters away from northern entrance of the culvert as shown in Figure 5 (a) in circular orientation inside the pipe as shown in Figure 5 (b). Each segment of SAA records coordinate as a signal and the data is transferred to the data acquisition system (DAS) shown in Figure 5 (c).

Installation of the Shape Array System Performing a Load Test Completing a LiDar Scanning Shape Array Instrumentation

Shape Array sensors (SAA®, Measurand, 2023), shown in Figure 4, are a geotechnical sensors can measure deformations for soil and rock such as short- and long-term monitoring of the stability of embankments, tunnels, and foundations monitoring (Measurand, 2023; Dasenbrok et al, 2011; Yan et al, 2021; Lipscombe, 2014; Pitilakis et al, 2013). SAA composed of a series nodes connected by flexible elements to track change in shape of the surrounding material. The use of SAA in geotechnical engineering has increased in the recent due to affordability offered by new manufacturing techniques development in wireless communication technology. There are several

Figure 5. Shape array instrumentation a) location of SAA from the north end of the culvert, b) SAA segments, c) data acquisition system (DAS) box.

All segments and conduit contain extended cable of SAA, tightened to the culvert by pipe strap and wall screws. Conduits contain extended cable of Shape Array, tightened to the crown of the culvert and extended to the data acquisition system (DAS) box. SAA had twenty-two segments, with segment 1 to segment 21 shown in Figure5 (b). 3.2

position 1, NR: North direction, Rear axle, SF: South direction, Front axle, SR: South direction, Rear axle.

LiDAR Instrumentation

In addition to the SAA monitoring, , a 5-meter section of the culvert's length was scanned with Laser Scanning survey method LiDAR by Atlantic Data Acquisition Services Inc. This 5-metre section was located 2.5-metres on either side of the Centre of the West (south bound) lane of Route 127. A 360-degree 2D LiDAR scan was taken along the 5-meter section in increments of 30 centimeters, producing 18 cross-sectional scans that were used to determine cross sections along the culvert's central section as shown in Figure 6 (d and e). The LiDAR scanner was pulled through the 5-metre section along a sliding rail system as seen in Figure6 (d and e). The rail system was bolted to a rectangular aluminum tube and then fastened to two sawhorses. The two sawhorses had plastic containers with approximately 30 kilograms of water on each platform stabilizing them. A tripod was affixed near the inlet to the CSP (Figure 2d) and used as a reference point to ensure each full scan was at the same Z axis location. The fibre glass pull rods were marked and stopped at 30 centre increments (Figure 2d).

Figure 7. Loading Test Setup a) Loading test truck, b, c and d) location of the SAA.

Figure 6. LiDAR instrumentation, including, LiDAR rail setup, 360-degree LiDAR scanner. 3.3

Loading Test

The static load testing was undertaken on Wednesday November 3rd, 2021. The test vehicle was a dump truck shown in Figure 7 (a) weighing 14,340 kg ,supplied by NBDTI. The truck was loaded with 6,420 kg of gravel, bringing its total weight to 20,760 kg. The truck was aligned to the marks on the ground showing the location of the installed SAA as shown in Figure 7 (b, c, and d). Twenty static load tests were completed, ten times in each traffic direction a at the wheel's locations shown in Figure 8 (Alderman, 2012). The truck was positioned over each location for about 20 seconds before moving to the next. In Figure 8, NF1: North direction, Front axle and

Figure 8. Truck locations of the loaded truck positions at both directions (north and south) (Alderman, 2012). 4 4.1

RESULTS AND DISCUSSION SAA Results

Figure 9 shows the vertical displacement, and Figure 10 shows the horizontal displacement data obtained from SAA in the culvert resulted from loading test in the south bound of the road over the culvert. The most critical segments regarding vertical displacement in Figure 9 are

the segments that are mounted on the crown of the culvert (segment 6, 14 and 16, refer to Figure 5b).

Figure 10. Horizontal cumulative displacement in south direction before lining application. 4.2

DISPLACEMENT (mm)

VERTICAL CUMULATIVE DISPLACEMENT 0.035 0.025 0.015 0.005 -0.005 -0.015 -0.025

1

3

5

7

9 11 13 15 17 19 21

LiDAR Results

Figure 11 shows 360 degree 2D LiDAR scan captured along the 5 metre section, showing 18 cross-sectional scans and Figure 12 shows a zoomed in details of the scanned points coordinates change at NR3, NR5, and NR5 loading scenarios compared to the reference initial case. There is a negligible change in coordinates, reflecting a negligible deformation. The results agree with the SAA observation in term of small deformation.

SEGMENT NUMBER

SF1

SF2

SF3

SF4

SF5

SR1

SR2

SR3

SR4

SR5

Figure 9. Vertical cumulative displacement in south direction before lining application. The most important segments for horizontal displacement shown in Figure 10 are the segments which are mounted close to the springline along both sides (segment 11 and 1, refer to Figure 5b). The maximum vertical displacement in south direction static loading test were measured at segments 14, 15 16 and 17, while the maximum horizontal displacements were measured at segments 9 to 13. The maximum displacement's location agrees with the expected location (Sezen, 2008). As noted in Figures 9 and 10, the maximum deflection values in vertical direction were 0.027 mm and .06 mm in horizontal direction. These small, measured values are consistent with the 2.8 m soil cover and the fact that structural integrity of the pipe is not compromised in spite of noticeable corrosion at the bottom of the pipe.

Figure 11. 18 LiDAR scanned cross-sections of a 5-metre section of CSP.

HORIZONTAL CUMULATIVE DISPLACEMENT DISPLACEMENT (mm)

0.07 0.05

0.03 0.01 -0.01

1

3

5

-0.03

7

9 11 13 15 17 19 21

SEGMENT NUMBER

SF1

SF2

SF3

SF4

SF5

SR1

SR2

SR3

SR4

SR5

Figure 12. Cross-sectional LiDAR scan – The Green points are the unloaded baseline scan while the other coloured points represent the three statically loaded positions (NR3, NR5, and NR5). 5

CONCLUSIONS AND RECOMMENDATIONS

The field testing was successfully accomplished in line with the project's criteria, which included measurements of the culvert deformation using SAA. The following conclusions of the field testing are presented:

-

-

-

-

-

The segments positioned near the springline along both sides are the most important in terms of horizontal cumulative displacement (segment 3, 10, 11 and 20). The maximum vertical displacement for north and south lane loading is 0.04 mm at NR5 position and 0.03 mm at SR5 position, respectively. For north direction loading, the maximum horizontal displacement is 0.06 mm at NR5 and NR4 positions, while it is slightly larger than 0.06 mm at SR5 and SR4 positions in south direction. Maximum vertical displacement in the aforementioned positions occurred in both directions at segments 7 and 15. The maximum horizontal cumulative displacement at those positions happened at segments number 10 and 11 in both directions. The key finding of this report was that the SAA technology is capable of measuring a continuous deformation profile of culverts. The static loading tests with a loaded truck did not result in any noticeable deformation at any CSP spots, but ShapeArray technology has demonstrated that even the smallest deformations can be continuously recorded by this device. ShapeArray instrumentation results are very accurate and reliable by comparing them to LiDAR data, and that there is good agreement in deformation measurements between SAA and LiDAR. The SAA was easy to install in the field including the data logger system. The deformations measured by SAA were in close agreement with LiDAR survey undertaken. This field case demonstrates the potential for use of SAA technology in future projects.

Utilizing SHAPEARRAY geotechnical instruments inside culverts offers numerous advantages for measuring strains. SHAPEARRAY's high-precision deformation monitoring capabilities enable detecting and analyzing sensitive changes in the culvert's structural behaviour, such as minor settlements, displacements, or deformations. This level of sensitivity is particularly valuable in assessing the long-term performance and stability of culverts, especially in challenging environments or regions prone to ground movements. By accurately capturing even small strains, engineers and geotechnical experts can gain deeper insights into the culvert's behaviour under various loading conditions, ensuring its design meets structural integrity, safety standards and optimizing maintenance efforts. In future works, SHAPEARRAY's integration in culverts will prove invaluable in enhancing the resilience of infrastructure projects, enabling real-time monitoring and timely intervention to prevent potential failures, and ultimately contributing to safer and more durable geotechnical engineering structures.

6

REFERENCES

Alderman, A. (2012). Field Testing of the Rehabilitated Kelly Creek No. 2 Corrugated Steel Culvert (M. Eng Report). Co-Supervisor Dr. Eldo Hildebrand. Dasenbrock, D., Abdoun, T., & Bennett, V. (2011). Realtime structural health monitoring of landslides and geotechnical assets with ShapeAccelArrays. In J. Han & D. E. Alzamora (Eds.), Proceedings of Geo-Frontiers 2011: Advances in Geotechnical Engineering (pp. 1585-1594). Reston, VA: American Society of Civil Engineers. García, D. B., & Moore, I. D. (2015). Performance of deteriorated corrugated steel culverts rehabilitated with sprayed-on cementitious liners subjected to surface loads. Tunnelling and Underground Space Technology, 47, 222-232. Google earth maps. Lipscombe, R., Carter, C., Perkins, O., Guerrero, S., & Thurlow, P. (2014). The use of Shape Accel Arrays (SAAs) for measuring retaining wall deflection. In Proceedings of the 39th Annual Conference on Deep Foundations (15 p.). Hawthorne, New Jersey: Deep Foundations Institute. Measurand. (2023). SAAV: Shape Array for Structural Monitoring. Retrieved from http://saav.measurand.com/home National Corrugated Steel Pipe Association. (2018). Corrugated Steel Pipe Design Manual (2nd ed.). Dallas, TX: National Corrugated Steel Pipe Association. New Brunswick Department of Transportation and Infrastructure. (2022). Personal communications. Pitilakis, K., Anastasiadis, A., Pitilakis, D., & Rovithis, E. (2013). Full-scale testing of a model structure in Euroseistest to study soil-foundation-structure interaction. Geotechnical Research, 50(1), 12-14. Sezen, H. (2008). In-Situ Load Testing of Corrugated Steel Pipe-Arch Culverts. Wagener, B. D., & Leagjeld, E. E. (2014). Culvert Repair Best Practices, Specifications and Special Provisions − Best Practices Guidelines. Minnesota Department of Transportation. Yan, R., Take, W. A., Hoult, N. A., Meehan, J., & Levesque, C. (2021). Evaluation of shape array sensors to quantify the spatial distribution and seasonal rate of track settlement. Transportation Geotechnics, 27, 100487.

Numerical analysis of temperature development around the storage rooms of a DGR, and the influence of facility geometry. Caleb Coulson, Othman Nasir Department of Civil Engineering – University of New Brunswick, Fredericton, New Brunswick, Canada ABSTRACT The deep geological repository (DGR) is an underground facility, constructed in a stable host rock formation to permanently host nuclear waste, including heat generating spent nuclear fuel. This heat will radiate into the surrounding environment and increase the temperature in the host rock. This temperature rise can lead to safety and serviceability issues. To minimize this concern, geometry of the facility should be optimized such that temperature rise within the host rock from the spent fuel source is minimized, while minimizing the total footprint of the facility. To investigate this, numerical models of a DGR were constructed, and analyzed utilizing FLAC3D. Temperature development in the immediate vicinity around the storage rooms was investigated by the use of a near field model in which the storage room spacing was varied to find the optimum design. RÉSUMÉ Le dépôt géologique profond (DGR) est une installation souterraine construite dans une formation rocheuse stable pour accueillir de manière permanente les déchets nucléaires, y compris les combustibles nucléaires usés générant de la chaleur. Cette chaleur se propage dans l'environnement environnant et augmente la température dans la roche hôte. L'élévation de la température peut entraîner des problèmes de sécurité et de fonctionnalité. Afin de minimiser cette préoccupation, la géométrie de l'installation doit être optimisée de manière à réduire au minimum l'élévation de la température dans la roche hôte due à la source de combustible usé, tout en minimisant l'emprise totale de l'installation. Pour étudier cela, des modèles numériques d'un DGR ont été construits et analysés en utilisant FLAC3D. Le développement de la température dans les environs immédiats des salles de stockage a été étudié à l'aide d'un modèle de champ proche dans lequel l'espacement des salles de stockage a été modifié pour trouver la conception optimale. 1

INTRODUCTION

With an increased social desire for cleaner energy production, nuclear power acts as a viable alternative to coal and petroleum-based energy sources. While Nuclear energy does not produce CO2, it does produce another potentially harmful bi-product; spent fuel bundles. These fuel bundles, while no longer viable for energy production, will remain radioactive, as well as produce heat for hundreds of thousands of years. In Canada, the agency responsible for the management of spent nuclear fuel is the National Waste Management organization (NWMO). Currently the NWMO utilizes temporary storage techniques, Dry Cask Storage, to host Canada’s spent fuel reserve. This is a safe technology, however this technique requires repackaging every 40 to 50 years, and with a total current reserve of 3,200,000 used fuel bundles (approximately 11,800m 3, or 72,000,000kg) of nuclear waste (Reilly, 2022) (NWMO, 2016), the need for a long term, permanent storage regime is prompted. The deep geological repository is proposed as the best long-term solution to the storage of spent nuclear fuel by the NWMO and many equivalent national organizations (NWMO, 2016). A DGR is a deep underground facility, in the magnitude of 500-700 meters below ground surface, comprised of a network of underground tunnels and placement rooms in an acceptable host rock (NWMO,

2022). A hypothetical DGR can be seen illustrated in Figure 1. These storage rooms are built to host steel walled, used fuel containers (UFC’s). UFCs are a key part of the DGR design and play an integral role in containing and isolating the spent fuel, designed such that they can withstand massive amounts of mechanical force in the case of rock failure, to ensure the waste is not released into the surrounding environment (NWMO, 2016). The used fuel containers are encased in bentonite clay in order to further isolate the used fuel from the environment (see Figure 1).

Figure 1: Deep Geological Repository concept (NWMO, 2016)

To ensure the used fuel is properly isolated, the temperature in the host rock must be managed such that the temperatures do not become excessive. Heat output from the spent fuel will propagate into the host rock via conduction, this increase in temperature will lead to thermal expansion, and due to the confined nature of the rock, this will lead to stress field changes which can cause thermal crack generation and/or propagation. As a general rule, the maximum temperature should remain below 100 °C to ensure vapor pressures don’t form that can propagate and worsen cracks within the host rock, as well as increase hydraulic gradients (Guo R. , 2018). Temperature development around the storage rooms of a DGR has been numerically investigated by previous researchers. Guo in 2016, analyzed the thermal performance of a hypothetical DGR in crystalline rock utilizing COSMOL, a finite element analysis software to model and investigate this phenomenon by use of a near and far field model (Guo R. , 2016). Nasir et al. conducted similar works in 2019 utilizing FLAC3D in which a near field model with very similar geometry to Guo’s was used to investigate the generation of thermally induced stress in the rock around the storage room (Nasir, Brennan, & Taek Oh, 2019). Temperature development within the DGR were found to reach a max of approximately 84 °C by Both Guo and Nasir at a time of 45 years after placement (Nasir, Brennan, & Taek Oh, 2019). Previous investigations have investigated the temperature development around the storage rooms of a DGR with fixed geometries with 20 metre centre to centre room spacings. Currently the NWMO recommends a 25metre center to centre spacing for the storage rooms, but states this is not an absolute set value. To investigate the sensitivity of this parameter, a sensitivity analysis was undergone in which the centre-to-centre room spacing was varied from 10 to 25 metres to find the DGR design that optimized facility size and temperature development 2

Figure 2: Numerical modelling methodology

METHODOLOGY

The general methodology for this investigation consisted of generating a near field DGR model geometry, applying material properties, a heat source, as well as boundary and initial conditions, and simulating temperature development for 150 years as shown in Figure 2. The initial model was created with 20 metre centre to centre spacing to match the conditions used by Guo (2016) and Nasir et al. (2019) for validation purposes. With the temperature development in the model validated the geometry was altered to have centre to centre room spacings of 10, 15, and 25 metre room spacings.

2.1

Model Geometry

To Begin investigating the temperature development around the storage rooms of a DGR, a near field model was generated. The near field model was modeled after hypothetical DGR layout in crystalline rock, shown in Figure . The near field model is representative of a thin slice through a storage room of a DGR, to demonstrate how the near field model relates to the entire facility, a simple schematic has been produced, shown in Figure . Figure a shows a simplified rendering of the DGR in Figure . Following the flow through Figure b, c and d, it can be seen that the near field model is representative of a thin slice through a DGR storge room. By utilizing the natural symmetry of the facility, the near field model allows for precises storage room contents to be modeled, while also keeping the relative complexity and computational power necessary for long term analysis relatively low.

spacers are 0.5m in width and are used to space the containers and their buffer box. The remainder of the rooms are back filled with bentonite back fill pellets.

Figure 5: Near field model geometry Figure 3: Hypothetical DGR Layout in Crystalline Rock (Guo R. , 2016)

2.2

Material Properties

To investigate the temperature development within the model, thermal properties were required for each material component. The thermal conductivity, specific heat capacity, and density for each material can be seen in Table 1. Table 1: Material properties Group Granite Rock

Density (kg/m3) 1995

2.0

1060

2276

Spacer Blocks Gap Fill

1.0 0.4

1280 870

1960 1439

60.5

434

7800

2.3

The full geometry of the near field model is 0.75m in thickness, 40m in width, and 10,000m in height with the storage room bottom set 500m below ground surface. There are 5 materials in the model, granite rock (light blue), dense back fill spacer blocks (green), bentonite buffer boxes (magenta), containers (blue), and bentonite back fill pellets (red) as shown in Figure . The rooms of the deep geological repository are 2.2m in height, and 3.2m in width. For validation purposes, the center-to-center room spacing was set to 20m. The modeled mark II UFCs are bisected longitudinally, to a width of 0.25m, representing half the width of a full, ~0.5m diameter container. The containers have also been bisected transversely for both of the two half rooms on either side of the model. The dense back fill

Spec. Heat (kJ/m3K) 845

Buffer Boxes

Container

Figure 4: Relation of near field model to full DGR facility

Th. Conductivity (W/m∙K) 3.0

Initial and Boundary Conditions

For initial conditions, a natural geothermal gradient was applied to the model, with the temperature being set to 5 °C at the ground surface with a 0.012 °C/m gradient downwards through the model, resulting in a temperature of 11 °C at the elevation of the room bottom, and 125 °C at the bottom of the model at a depth of 10,000m BGS. In using the near field model, some assumptions have been made to utilize symmetry in the model. For example, the four outside vertical boundaries have an adiabatic thermal condition. Meaning it is assumed that the storage rooms extend infinitely in the horizontal directions, and therefore, no thermal gradient exists at these surfaces. 2.4

Heat Source

The heat source in a DGR is the used fuel. This spent fuel will produce heat for hundreds of thousands of years. The

volumetric heat output of the spent fuel containers can be seen in Table 2. Table 2: UFC volumetric heat output Time after placement (years)

Volumetric heat output (Q/m3)

Time after placement (years)

Volumetric heat output (Q/m3)

0 5 10 15 20 25 30 40 45 50 60 70 80 105

290.98 267.13 244.87 225.79 209.89 193.99 181.27 157.57 147.88 139.13 124.34 112.42 102.88 86.02

120 130 170 270 470 970 1970 4970 9970 19970 34970 49970 99970 999970

79.34 75.85 66.62 56.45 46.27 32.11 21.94 15.9 11.43 6.61 3.61 2.27 0.65 0.24

2.5

Temperature development validation and sensitivity analysis

The temperature development in the near field model was simulated for 150 years post placement of the UFCs using 20 metre centre to centre spacing. The results will be compared to previous findings by Guo (2016) and Nasir et al. (2019) to validate the temperature development in this research. Once validated, the centre to centre spacing will be varied to values of 10, 15, and 25 metres, to investigate how this affects the temperature development around the storage rooms. 3 3.1

RESULTS AND DISCUSSIONS Temperature development validation

the results of the temperature development in and around the storage rooms was compared to previous works performed by Guo (2016) and Nasir et al. (2019), who conducted similar research on near field models of hypothetical DGRs. The model geometries used by Guo and Nasir are very similar to the geometry analyzed in this research. The temperature development was plotted for 5 different locations shown in Figure . Location A is at the middle of a storage room, location B is at the top of the storage room, location C, D and E are 1, 5, and 10 metres above the top of the room respectively. These are the same locations monitored by Guo (2016) and Nasir et al. (2019), and were selected because the temperature development within the model in this research could be directly compared to the temperature developments of Guo and Nasir et al. for validation.

Figure 6: Points used for temperature development validation: The temperature development at each point of analysis from this research can be seen compared to the temperature developments found by Guo (2016) and Nasir et al. (2019) in Figure . In the near field model utilized for this research, point A, the location at the center of the placement room reached a peak temperature of 81.8 °C at a time of 48.2 years after placement, and point B, the location just outside of the room reached a max temp of 76.2 °C at a time of 66.4 years post placement. From Figure it can be seen that at point A, Nasir et al. found a maximum temperature of 84.7 °C at a time of 45.8 years after placement, and Guo found a max temperature of 83.0°C. The small differences in the temperature development between the model in this research, Guo’s, and Nasir et al.’s could be due to the slight differences in model geometry. The model used in this research also included more storage rooms than previous works by Nasir et al. and Guo. In addition to this, differences in grid discretization between the models could be another explanation for the small differences in temperature developments. However, the peak temperature found in this research, is within a relatively small margin of less than 3°C of the temperatures found previously, and the temperature development at each of the 5 points monitored were very similar to the temperature development found by previous researches. Therefore, the results can be considered accurate for use in further analysis

Figure 7: Temperature development comparison for this research and Guo’s (2016) and Nasir et al.’s (2019) previous findings 3.2

Temperature Development sensitivity analysis

room

spacing

The near field model geometry was adjusted to have center to center spacings of 25, 15, and 10 metres to investigate the sensitivity of the temperature development in and around the DGR storage rooms on the geometry of the facility. To monitor the temperature development in the model, additional observation points were added between the rooms. These can be seen in Figure ; Points A through E remained the same as the temperature development validation however, four additional points were added, point F and H are positioned directly next to the storage rooms at 1.1m above room bottom, point G is positioned at the midpoint between rooms at 1.1m above room bottom, and point I is positioned at the center of the middle storage room. The model was again run for 150 years with the same material properties, boundary and initial conditions, for room spacing of 25, 20, 15, and 10 metres

However, the maximum temperature within the rooms increased by ~33 ºC relative to the 20-metre spacing when room spacing was lowered to 15 metres, and increased 78°C relative to the 20-metre spacing when room spacing was lowered to 10 metres. Table 3: Maximum temperature at each point for various room spacings Room Spacing (m) Max Temp at Point A (°C) Max Temp at Point B (°C) Max Temp at Point C (°C) Max Temp at Point D (°C) Max Temp at Point E (°C) Max Temp at Point F (°C) Max Temp at Point G (°C) Max Temp at Point H (°C) Max Temp at Point I (°C)

Figure 8: Points used in temperature development sensitivity analysis A summary of the temperature development for each room spacing at the points extending vertically away from the rooms can be seen in Figure . The results of the temperature development at the points added for this sensitivity analysis, that extend horizontally between the rooms (Points A, F, G, H and I in Figure ) can be seen in Figure . Table 3 summarizes the maximum temperature at each point of analysis for the 4 various room spacings. From Table 3, it can be seen that the difference in maximum temperature for room spacings of 25 metres and 20 metres is nearly negligible. Within the rooms (points A and I) the difference in max temperature is less than 1 °C.

25

20

15

10

81.2 74.4 70.9 67.6 64 72.7 66.6 72.7 81.2

81.8 76.2 73.5 70.7 67.3 74.9 70.5 74.9 81.8

115 109 107 103 98.6 108 104 108 115

159 155 152 148 141 154 151 154 159

While no substantial difference is noted between the max temperature for 20 and 25m spacings, a considerable rise in temperature is observed when lowering the room spacing to 15 metres and 10 metres at all points. This leads to the conclusion that a 20-metre center to center spacing of the storage rooms may be the optimized design for management of temperature development. Increasing the room spacing to 25 metres does not appreciably lower the temperature development around the storage rooms, and leads to a 25 percent increase in area required to store the same amount of fuel. It also leads to the conclusion that decreasing the room size to 15 or 10 meters is likely not appropriate, as it increases the temperature in the host rock above the allowable threshold of 100 °C. Therefore, the NWMO’s recommended storage room spacing of 25 metres appears to not be the optimized design to maximize storage while limiting temperature rise in the host rock. However, this may not be the only rational for selecting a 25-metre spacing, as temperature development is highly site specific and modeling would have to be undergone with properties directly taken from the selected host rock to confirm this conclusion.

Figure 9: Temperature development at points (A, B, C, D and E) For 10, 15, 20, and 25 storage metre room spacing

Figure 10: Temperature development at points between rooms (A, F, G, H, I) for 10, 15, 20 and 25 metre room spacing 4

CONCLUSIONS

A near field model was created to investigate the temperature development in and around the storage rooms of a DGR due to the thermal output of the spent fuel. The

temperature development in the near field model was simulated with 20 metre centre to centre room spacings for 150 years post placement of the spent fuel. The temperature development within the model was then compared to previous research conducted by Guo and Nasir et al. It was found that the temperature development within the model used in this research matched very closely with previous temperature developments found in similar models (Guo R. , 2016), (Nasir, Brennan, & Taek Oh, 2019), in which maximum temperatures observed were within 3 °C of the temperatures found in the model used in this research. Using the validated model, A sensitivity analysis was conducted to investigate the effects of storage room spacing on the temperature’s development in and around the storage rooms of a DGR. The NWMO currently recommends a 25-metre center to center room spacing. It was found that no substantial difference is noted between the maximum temperature in the host rock for 20 metre and 25 metre spacings, but a considerable rise in temperature is observed when lowering the room spacing to 15 metres and 10 metres at all points, generating temperatures above the maximum allowable temperature of 100 °C. This leads to the conclusion that the 20-metre center to center spacing of the storage rooms may be the optimized design for management of temperature development, while minimizing the footprint of the facility. It also leads to the conclusion that decreasing the room size further to 15 or 10 meters is likely not appropriate, as it considerably increases the temperature in the host rock. 5 REFRENCES Guo, R. (2016). Thermal Response of a Mark II Conceptual Deep Geological Repository in Crystalline Rock. Toronto: NWMO. Guo, R. (2018). Thermal Response of a Conceptual Deep Gological Repository in Sedimentary Rock. Toronto: NWMO. Nasir, O., Brennan, G., & Taek Oh, W. (2019). Near-Field Thermo-Mechanical Coupled Processes in Host Rocks of High-Level Waste Deep Geological Repistories. St. Johns: Canadian Geotechnical Society. NWMO. (2016). Deep Geological Repository. Retrieved from nwmo.ca: https://www.nwmo.ca/en/A-SafeApproach/Facilities/Deep-Geological-Repository NWMO. (2016). What is Used Nuclear Fuel? Toronto: NWMO. NWMO. (2022). Deep Geological Repository. Retrieved from NWMO: https://www.nwmo.ca/en/A-SafeApproach/Facilities/Deep-Geological-Repository NWMO. (2022). What other countries are doing. Retrieved from NWMO: https://www.nwmo.ca/en/CanadasPlan/What-Other-Countries-Are-Doing Reilly, T. (2022). Nuclear Fuel Waste Projections in Canada - 2022 Update. Ottawa: NWMO.

Tuesday, October 3, 2023

ROCK MECHANICS I

EFFECT OF THE DAMAGE ZONE AROUND MINING EXCAVATIONS IN ROCK SALT BASED ON INTERNAL STATE VARIABLE TIME-DEPENDENT MODELLING Jonathan D. Aubertin1, Michel Aubertin2, Abtin Jahanbakhshzadeh2 1École de technologie supérieure (ETS), Université du Québec, Montréal, QC, Canada 2Polytechnique Montréal, Montréal, QC, Canada ABSTRACT Evaluation of the stress and strain distributions around underground openings in mines of evaporites (rock salt, potash) is essential to determine optimal room and pillar dimensions, minimal distance between levels, and crown pillar thickness. The behavior of such low porosity soft rocks exhibits a strong time- and history-dependency when subjected to deviatoric loading. Observations indicate that the time-dependent inelastic (creep) deformations away from the walls (i.e. inside large pillars) can be ductile, while the rock near the openings often show a semi-brittle behavior with the emergence of a damage zone. This article focuses on the response of the damage zone around mining excavations in rock salt. The analysis is based on the effect of damage, which induces a reduction in stiffness and acceleration of the inelastic strain rate. The numerical investigation evaluates the geometry of the damage zone around underground excavations based on experimental data collected on rock salt. A time-dependent model with a strain-hardening (SH) internal state variable (ISV) and a damage component is used to simulate the response of mining excavations induced by the evolving stress state and related displacements. RÉSUMÉ L’évaluation de la distribution des contraintes et des déformations autour des ouvertures souterraines dans les mines d'évaporites (sel gemme, potasse) est essentielle pour déterminer la dimension optimale des ouvertures et leur espacement. Le comportement de ces roches tendres montre une forte dépendance au temps et à l'historique de chargement. Les observations indiquent que les déformations inélastiques différées (fluage) loin des parois sont de nature ductile, alors que la roche près des parois peut présenter un comportement semi-fragile avec l'apparition d’une zone d’endommagement. Le présent article analyse la réponse de cette zone autour des excavations minières dans le sel gemme. L'analyse tient compte de l'effet de l’endommagement qui induit une réduction de la rigidité et une accélération du taux de déformation inélastique. Des simulations numériques évaluent la géométrie de la zone d’endommagement autour des excavations minières souterraines sur la base d’observations expérimentales sur le sel gemme. Un modèle de comportement inélastique avec une variable d'état interne d’écrouissage et un paramètre d’endommagement est utilisé pour simuler le comportement différé (fluage) induit par l'évolution de l'état de contrainte et des déplacements autour des excavations. 1

INTRODUCTION

The geomechanical behavior of low porosity rocks can range from a brittle response, as is the case for fine grained plutonic rocks at relatively low confining pressure, to timedependent ductile (fully plastic) time-dependant straining for evaporites under commonly encountered loading conditions in the rock mass. The response of rocks depends on the properties of the constituent grains and on the characteristics of the various defects that control their non-linear behavior. Brittle mechanisms are essentially related to the creation and propagation of microcracks that produce pressure dependent non-isovolumetric straining. Ductile processes are mainly associated with dislocations generation and motion within grains (crystals), leading to fully plastic, timedependent isovolumetric strains that are (almost) pressure insensitive. Under specific loading conditions, some materials, including various rock types, can also show a semi-brittle response. This transitional inelastic behavior regime usually involves the simultaneous action of

intracrystalline plasticity and micro fracturing (Aubertin et al. 1998a). Studying the semi-brittle regime (SBR) is of interest for many applications in rock mechanics, such as the evaluation of frictional stresses and seismic response in the earth crust, the creation and evolution of fault-zones, and the extraction of geothermal energy at depth (e.g. (Carter and Tsenn 1987; Price 2013; Jacquey and Cacace 2020). The SBR is also relevant for low porosity soft rocks when assessing the effect of the damage zone around underground openings used as waste disposal chambers or for mining operations (Chan et al. 1996; Thorel and Ghoreychi 1996; Cristescu and Hunsche 1997; Aubertin et al. 1998a; Roberts 2015). A semi-brittle behavior is commonly observed in evaporites, such as rock salt and potash, when the loading conditions involve a relatively high deviatoric stress (above 0.3 to 0.5 of the peak strength) at low confining pressure (Aubertin et al. 1993, 1998a; Thorel 1994). These conditions often prevail near the walls of underground mine openings, where a damage zone may develop. In such

instances, the time-dependent inelastic response of the rock tends to be ductile (fully plastic) away from the walls (i.e. inside large pillars), while it depends on the amount of damage induced by the stress state near the excavation boundaries. Such semi-brittle response should be taken into account when analyzing the behavior of mine openings, but it is not commonly the case in practice. The resulting damage zone and superficial conditions are particularly important for safe and reliable operation of large underground excavations in salt mining operations. This article investigates the semi-brittle behavior of rock salt (and other low porosity soft rocks) around underground mining excavations. A time-dependent, strain hardening (SH) constitutive model with an internal state variable (ISV) was implemented in FLAC (Itasca Consulting Group 2019) to simulate the ductile behavior of rock salt (Aubertin et al. 2023). A damage component is added here to the constitutive model, and the new parameters are evaluated explicitly based on laboratory test results. The model formulation, referred to as ISV-SH-D is deemed to capture the geomechanical response in the ductile and semi-brittle regimes. The proposed formulation is first used to simulate constant stress (creep) tests at different confining pressures. The ISV-SH-D model, also implemented in the numerical code FLAC, is then applied to simulate the damage zone and resulting convergence rate near typical mine openings. Results illustrate the influence of semibrittle behavior on deformation near the walls. The geometry of the damage zone around a high bench room configuration is also evaluated with the proposed model and compared with empirical evidence in room and pillar mine workings. 2

SEMI-BRITTLE RESPONSE OF SOFT ROCKS

The semi-brittle behavior has been investigated on a number of engineering materials, including various crystalline rocks submitted to high pressure and temperature (Carter and Kirby 1978; Hirth and Tullis 1989; Fredrich et al. 1989; Pec et al. 2016; Nicolas et al. 2016). In practice however, only a few types of rock show a semibrittle response under commonly encountered conditions in mines. This can be the case with rock salt (and other evaporites), for which intracrystalline plasticity due to dislocations motion is relatively easy to initiate, but which is partially restricted by the limited number of active slip systems that in turn favors concomitant microfracturing. This type of transitional behavior is not easy to investigate and characterize, and its analysis for designing mine openings involves many challenges related to the constitutive equations formulation and their application through numerical modelling. The constitutive and numerical models must then consider that rock salt semibrittle behavior is intrinsically time-dependent, with inelastic straining occurring through distributed stable microcracking (cataclastic damage and flow) combined to mechanisms of intracrystalline (visco) plasticity. Despite the difficulties involved, important observations have been made through specialized experimental testing in the semi-brittle regime (SBR) of rock salt. It has been shown, for instance, that a deviatoric stress in excess of

the damage initiation threshold (DIT) favors brittle processes associated with damage due to microcracking. Decreasing the loading rate (in a constant strain rate, CSR, test) tends to promote ductile processes by reducing the deviatoric stress (at a given strain), which can then remain closer to (and even under) the DIT. Below this threshold, strains are ductile (fully plastic), isovolumetric and (almost) independent of the mean stress. Straining becomes pressure sensitive above the DIT, but volumetric strains are usually much smaller in the SBR than for a brittle behavior. Semi-brittle microcracking in rock salt generally remains diffuse and uniformly distributed until the peak strength is reached (and even beyond). The apparent SBR saturation (peak) stress in a CSR test is lower than the corresponding stress for a fully plastic behavior because microcracking weakens the rock (Aubertin et al. 1998a). Damage weakening also tends to reduce Young’s modulus. In creep tests, the transient (hardening) phase in the SBR is usually followed by a pseudo steady-state and/or by an accelerating (tertiary) creep phase. The minimum creep rate in the SBR for a given deviatoric stress is larger than the stationary creep rate for a comparable ductile condition, due to the weakening effect of microcracking (Sgaoula 1997; Aubertin et al. 1998b). General strain localization (leading to macrofracturing) progresses slowly near and beyond the peak strength, so the rock may be considered as a continuum even in the early part of the tertiary creep phase or along the softening portion of the stress-strain curve. Small scale strain localization may occur but microcracks coalescence is restricted by the plastic deformation processes. Also, while microcracking is generally considered an irreversible process, microcracks closure under pressure may lead to (partial) healing in rock salt, which tends to reduce the effect of past loading on the degradation of the rock strength and elastic parameters (Brodsky and Munson, 1994). 3 3.1

CONSTITUTIVE MODEL FORMULATION Time-dependent Inelastic Modelling

The following introduces an elastic-viscoplastic model with a damage component to represent the ductile and semibrittle regimes of rock salt inelastic behavior under various loading conditions, up to failure. The proposed constitutive model formulation is expressed in terms of partial differential equations for the strain rate 𝜖̇ (s −1 ). Modelled strains include elastic (𝜖 𝑒̇ ) and inelastic (𝜖 𝑖̇ ) components, with the latter arising from ductile (𝜖𝑣𝑖̇ ) and damage induced (𝜖𝑑𝑖̇ ) behavior. The total strain rate can thus be expressed using the following equation (Aubertin et al. 1998b): 𝜖̇ = 𝜖 𝑒̇ + 𝜖𝑣𝑖̇ + 𝜖𝑑𝑖̇ 3.2

[1]

Ductile regime

The following kinetic law is the basis for ductile response of the model (Aubertin et al. 1991, Yahya et al. 2000):

𝜖̇𝑣𝑖 = 𝐴 〈

𝜎 ̅ − 𝜎𝑖 𝑁 〉 𝐾

[2]

where 𝜎̅ (MPa) is the equivalent (von Mises) deviatoric stress expressed from the deviatoric stress tensor (𝜎̅ = √3𝐽2 = √3/2 ∙ 𝑆𝑖𝑗 ∙ 𝑆𝑖𝑗 ); A (s−1 ) and 𝑁 are material parameters; 𝐾 (MPa) represents the drag stress and 𝜎𝑖 includes internal stress components (MPa). The internal state variable strain-hardening (ISV-SH) model was developed from Eq. 2 to represent the inelastic behavior of rock salt under loading conditions associated with typical room and pillar mining sequences (Aubertin and Aubertin 2021, 2022; Aubertin et al. 2023). The main kinetic law for ISV-SH simplifies Eq. 2 by neglecting the internal stress component 𝜎𝑖 ; it can thus be written as: 𝜎 ̅ 𝑁

[3]

𝜖̇v𝑖 = 𝐴 ( ) 𝐾

The drag stress variable K in Eq. 3, which represents the viscoplastic kinetic law without a yield surface, serves to model the transient and steady state inelastic flow, considering the time and loading-history dependent response. The evolution of variable 𝐾 converges toward its saturated value 𝐾′ (MPa) at steady state (i.e. 𝐾 < 𝐾′ implies transient state). The value of variable 𝐾 changes according to the following evolution law: 𝑑𝐾 𝐾̇ = = 𝐴5 (1 − 𝑑𝑡

𝐾 𝐾′

) 𝜖̇𝑣𝑖

[4]

where 𝐴5 is a material parameter. The saturated value 𝐾′ is obtained from Eq. 3 by replacing the actual deviatoric stress component 𝜎̅ by the value 𝜎̅′ associated with the actual inelastic state strain rate 𝜖̇𝑖 (𝑠 −1 ). 𝐾′ =

𝜎 ̅′ 1 𝜖̇ 𝑖 𝑁 ( 𝑣) 𝐴

[5]

≥ 1 MPa

The strain rate is then linked with the steady state (stationary) condition, with the corresponding strain rate represented by the following hyperbolic sine function, which captures the inelastic flow at low and high deviatoric stresses (Aubertin et al. 1998a; Aubertin and Julien 2015): 𝜎 ̅

[6]

𝜖̇ 𝑠𝑠 = 𝜖0̇ ∙ sinh𝑛 ( ) 𝜎0

where 𝜖0̇ (𝑠 −1 ), 𝑛, and 𝜎0 (MPa) are material parameters. The saturated (stationary) value 𝜎̅′ in Eq. 6 is evaluated by isolating 𝜎̅ in Eq. 7 with the evolving inelastic strain rate 𝜖̇𝑣𝑖 (for 𝜖̇ 𝑠𝑠 ): 1

𝜎̅′ = 𝜎0 ∙ 3.3

𝑖

𝜖̇ 𝑛 sinh−1 ( 𝑣̇ ) 𝜖0

[7]

Semi-brittle regime

Following the commonly used approach in Continuum Damage Mechanics, the effect of damage is represented by a state parameter 𝐷, which varies from 0 (no damage) to Dc at the onset of failure (or localisation). The evolution of 𝐷 may depend on the stress conditions (above the DIT),

strain and strain rate, cumulated damage and failure (ultimate state). This relationship can be represented by an evolution law of the form (Kachanov 1986; Askes et al. 2020): 𝐷̇ = 𝐹1 (𝐷) ∙ 𝐹2 (𝜎, 𝑆𝐷 ) ∙ 𝐹3 (𝜖𝑖𝑗 , 𝜖𝑖𝑗̇ , 𝑇)

[8]

where 𝑇 is temperature (considered constant here), 𝑆𝐷 is the damage initiation surface (or DIT), and 𝐹1 , 𝐹2 , 𝐹3 are functions capturing the influence of cumulative damage, stress conditions with respect to the damage surface, and strain rate and cumulative strain. Sgaoula (1997) (see also Aubertin et al. 1998b) adapted Eq. 8, based on experimental evidence, to represent rock salt semi-brittle response; the corresponding SUVIC-D model equation serves below to formulate the damage component included in the proposed ISV-SH-D model. The non-linear yield/failure surface of rock salt is approximated here by the Mises-Schleicher (MS) criterion (Aubertin 2020; Aubertin et al. 2021): √3𝐽2 = √(𝐶0 − 𝑇0 )𝐼1 + 𝐶0 𝑇0

[9]

where 𝐶0 and 𝑇0 are the uniaxial compressive and tensile strength (MPa) of the rock; 𝐼1 (= 𝜎11 + 𝜎22 + 𝜎33 , MPa) is the first invariant of the stress tensor. The MS criterion can also be applied to define the damage initiation threshold (DIT) of rock salt (Aubertin and Simon 1997). 4

NUMERICAL IMPLEMENTATION OF ISV-SH-D

4.1

Numerical tools

The numerical computations and simulations presented here were performed following an explicit implementation of the constitutive equations via Python scripting (Van Rossum and Drake 2009), and then adapted for the 2D finite differences model (FDM) FLAC. Numerical implementation of the ISV-SH constitutive equations, used for the ductile regime, is presented in Aubertin et al. (2023). The next subsections provide additional details for the implementation of the updated model ISV-SH-D applied to simulate the semi-brittle regime or rock salt. 4.2

Net stress, net modulus and MS criterion

With the ISV-SH-D model, the damage variable is evaluated explicitly, based on a specific version of Eq. 8, presented by Aubertin et al. (1998b); the proposed formulation can be expressed as: 𝐷= 〈

√𝐽2 −𝐹𝑖 1.5 〉 𝐹0 −𝐹𝑖

∙ 𝐷𝑐

[10]

where and 𝐹0 and 𝐹𝑖 are the relative positions along the failure (peak) and damage initiation surfaces. The critical damage value 𝐷𝑐 (= 0.5) and that of the exponent (= 1.5) are based on experimental results on rock salt (presented in Sgaoula, 1997). More specifically, the failure (𝐹0 ) and damage initiation (𝐹𝑖 ) surfaces, expressed in the 𝐽2 − 𝐼1 plane, are computed as (based on Eq. 9):

[11]

𝐹𝑖 = √1/3[(𝜎𝑐𝑖 − 𝑇𝑖 )(𝐼1 ) + 𝜎𝑐𝑖 𝑇𝑖 ]

[12]

where 𝐶𝑖 (< 𝐶0 ) and 𝑇𝑖 (< 𝑇0 ) are the uniaxial compressive and tensile initiation strength threshold. The effect of damage modifies the stress and Young’s modulus of the material; this effect is expressed using the net (deviatoric) stress 𝜎̃ (MPa) and net elastic modulus 𝐸̃ (GPa): 𝜎̃ =

𝜎 ̅

[13]

1−𝐷

[14]

𝐸̃ = 𝐸(1 − 𝐷)

Material parameters for the failure and damage initiation surfaces were determined from triaxial compression tests performed on specimens from the Weeks Island and Avery Island mines (Louisiana, USA) (taken from Sgaoula, 1997 and Aubertin 2020). The two high purity salt deposits are characterized by large crystals and high-grade orebodies. Figure 1 shows experimental data for the damage initiation threshold (DIT) and failure (Peak) strength conditions from the Weeks Island (1500 and 1400 levels) and Avery Island (AI) mines (Sgaoula 1997; Aubertin 2020; Aubertin et al. 2021). The curved surfaces (shown in 2D here) for the damage initiation and peak stress are fitted to the experimental data points, as shown in the graph.The corresponding MS criterion (Eqs. 11, 12) parameters used to plot the curves are listed in Table 1.

Table 1: Failure and damage parameters for Eq. 11, 12. 𝑪𝟎 25 MPa 4.3

30

𝝈𝒄𝒊 10 MPa

𝑻𝒊 0.8 MPa

The magnitude of damage induced strains was evaluated by Sgaoula (1997) as an extension of the SUVIC model; the corresponding formulation of SUVIC-D is also presented in Aubertin et al. 1998b. Figure 2 shows experimental results for constant stress (creep) tests conducted on Waste Isolation Pilot Plant (WIPP) salt specimens. Simulations with the SUVIC-D model are also included in the figures. The viscoplastic (𝜖𝑣𝑖̇ ) and damage induced (𝜖𝑑𝑖̇ ) components of cumulative strains are also plotted separately to show their respective contributions for two confining stresses ( 𝜎3 = 2 MPa in Figure 2 (a) and 1 MPa in Figure 2 (b)). (a)

25

WIPP (experimental) SUVIC-D Visco-elastic Brittle

20 15

σ3 = 2 MPa

10 5 0 0.0E+00

35

𝑻𝟎 1 MPa

Semi-Brittle Experimental Response

Axial Strain (%)

𝐹0 = √1/3[(𝐶0 − 𝑇0 )(𝐼1 ) + 𝐶0 𝑇0 ]

5.0E+05

1.0E+06

1.5E+06

2.0E+06

Time (s)

20

0.16

15

0.14

10

0.12

5 0 0

20

40 60 80 100 𝐼1 = 𝜎1 + 𝜎2 + 𝜎3 (MPa)

1500-Peak 1500-DIT AI-DIT DIT surface

120

1400-Peak 1400-DIT Brittle surface

Figure 1: Experimental results from triaxial compression tests on Weeks Island (levels 1400 and 1500) and Avery Island (AI) salt specimens. The two surfaces for damage initiation and peak stress are plotted using Eq. 11, 12 and parameters from Table 1.

Axial Strain (%)

3

𝐽2 =

𝜎1 − 𝜎3

25

0.1

(b) WIPP (Experimental) SUVID-D Visco-elastic Brittle

0.08 0.06 0.04 σ3 = 1 MPa

0.02 0 0.00E+00

1.00E+05

2.00E+05

3.00E+05

Time (s) Figure 2: Constant stress creep test results on rock salt from the WIPP and SUVIC-D simulations, (a) confining pressures 𝜎3 = 2 MPa; (b) confining pressures 𝜎3 = 1 MPa (adapted from Sgaoula, 1997)

For the ISV-SH-D model presented here, the ratio of damage to viscoplastic (creep) strains is evaluated explicitly as a function of 𝜎3, , which tends to suppress microfracturing (damage) when approaching 3 to 5 MPa (Aubertin et al. 1998a, 1999). A step-wise linear approximation is given by the followings:

𝜖𝑣̇

≈ 𝐶(𝜎3 ) {

𝜎3 ≥ 1, 𝐶 = −0.5𝜎3 + 1.5 𝜎3 < 1, 𝐶 = 1 𝜎3 ≥ 3, 𝐶 = 0

ISV-SH

[15]

Experimental points AI salt

0.08 𝜎3 = 15 MPa

The relationship between ratio 𝐶 and 𝜎3 is plotted in Figure 3. It is assumed here (based on experimental data shown in Sgaoula 1997 and others) that rock salt response is ductile (i.e. without damage) for 𝜎3 ≥ 3 MPa, for the deviatoric stress of interest.

𝜎̅ = 15 → 16 MPa

0.06

Strain

𝜖𝐷̇

Figure 4 shows experimental results for three constant stress (creep) tests performed on Avery Island rock salt (with 𝜎3 = 15 MPa), as reported by Senseny et al. (1993). This figure also shows simulations of the creep tests were also performed using the ISV-SH model with parameters from Table 2.

0.04

1.5

𝜎̅ = 10 → 12.5 MPa

0.02

1

𝜎̅ = 5 MPa

𝐶

Ductile regime (𝜎3 ≥ 3 𝑀𝑃𝑎)

0.5

0

Semi-Britle (𝜎3 < 3 𝑀𝑃𝑎) 0

2

4

𝜎3 (MPa) Figure 3: Linear approximation applied to define the strain 𝜖̇ rate ratio 𝐶 = 𝐷̇ as a function of the minimum principal stress 𝜎3 . 5

𝜖𝑣

COMPARISON OF ISV-SH AND ISV-SH-D

The influence of the added damage parameter 𝐷 on simulated material response is first evaluated through explicit comparison for conventional creep tests. The ductile behavior of the material is based on experimental testing (Senseny et al. 1993), carried on Avery Island rock salt at high confinement stress (𝜎3 = 15 𝑀𝑃𝑎). Material parameters for Eq. 3 – 8 are listed in Table 2. Table 2: ISV-SH model parameters calibrated on creep tests performed on Avery Island rock salt samples (Adapted from Aubertin et al. 2023). Parameter A 𝐴5 𝜎0 N n 𝐸0 𝐸 𝜈

Value 1.6 E-14 3.2E+8 14 MPa 7 4 9E-10 17.06 GPa 0.33

0 0.0E+00

2.0E+07

4.0E+07

Time (s)

Figure 4: Experimental results for three constant stress creep tests performed on Avery Island rock salt (data taken from Senseny et al. 1993), and simulated response given by the ISV-SH model (Eq. 3-8) using parameters from Table 2 (adapted from Aubertin et al. 2023) One of the tests in Figure 4 was used to conduct new simulations with ISV-SH-D for 2 different (much lower) confining pressures. Figure 5 (a) shows the simulated curves for this incremental creep test (initial deviatoric stress 𝜎̅ = 10 MPa, increased to 12.5 MPa after 260 days), 𝜖̇ for 𝜎3 = 2 MPa, which yields a ratio 𝐶 = 𝐷̇ = 0.5. The figure 𝜖𝑣

plots the simulated total strain given by ISV-SH-D, and the previously simulated results given by ISV-SH; it also shows the respective strain contributions from the viscoplastic (𝜖𝑣𝑖 ) and damage induced (𝜖𝐷𝑖 ) components of the model formulation (Eq.1). Figure 5 (b) shows the same 𝜖̇ type of results for 𝜎3 = 1 MPa, which yields ratio 𝐶 = 𝐷̇ = 𝜖𝑣

1 ; the curves in this figure for 𝜖𝑣𝑖 and 𝜖𝐷𝑖 are thus the same. It is seen in these figures that the viscoplastic response with some damage (SBR) leads to larger strains then the ISV-SH associated with the ductile regime. This is a consequence, in part, of the deviatoric stress (net stress) amplification due to damage which increases the strains and strain rates (Eq. 13-14). The results shown above highlight the increased strain expected under low confining pressure, when a semi-brittle behavior may occur. Under such conditions, damage increases the total strain rate by adding a damage induced strain component and by increasing the deviatoric (net) stress acting on the viscoplastic component. Such type of loading conditions may prevail near underground excavations, as is investigated next.

(a)

6E-02 𝜎̅ = 10 → 12.5 MPa 𝜎3 = 2 MPa

Strain

ISV-SH-D

ISV-SH

2E-02 𝜖𝐷𝑖

0E+00 0E+00

1E+07

2E+07

3E+07

Time (s) (b)

2E-01 𝜎̅ = 10 → 12.5 MPa 𝜎3 = 1 MPa

ISv-SH-D

Strain

1E-01

8E-02

1.4E+00

𝜖𝑣𝑖 = 𝜖𝐷𝑖

4E-02 ISv-SH

0E+00 0E+00

1E+07

2E+07

3E+07

Time (s) Figure 5: Simulated constant stress (creep) test results obtained with the ISV-SH-D model for (a) 𝜎̅ initially set at 10 MPa, then increased to 12.5 Mpa after 2.25E+07 seconds; minimum principal stress 𝜎3 = 2 Mpa (which yields 𝐶 = 0.5) (b) 𝜎̅ initially set at 10 MPa, then increased to 12.5 MPa after 2.25E+07 seconds; minimum principal stress 𝜎3 = 1 MPa which yields 𝐶 = 1. The different components of the ISV-SH-D model are shown, and compared with the ISV-SH simulated curve.

Vertical displacement (m)

4E-02

𝜖𝑣𝑖

Figure 6 shows a conceptual representation of the panel layout from a vertical section view. The excavation layout is representative of a typical panel in room and pillar salt mining operations. In the simulations, the natural stress state is isotropic (set at 15 MPa). The excavation sequence was carried from left to right with 30-day intervals between each room. Detailed information on model construction, mesh density and other modelling conditions are provided in Aubertin et al. (2023). Figure 7 shows the simulated roof vertical (downward) deformation (at opening center) for the five rooms. The simulations were carried with the ISV-SH-D (semi-brittle response) and ISV-SH (ductile) models. The comparison illustrates the accentuated deformation (and rates) given by the semi-brittle response under low confining stress near the wall excavation. Although the differences between the 2 models are not negligible, the results indicate that the magnitude of the displacements induced by the damage component (developing near the walls) is relatively small compared with the global time-dependent viscoplastic straining that occurs in the entire rock mass surrounding the mine openings. The situation is however different when considering what specifically happens near the walls, as shown below. ISV-SH-D ISV-SH

1.2E+00 1.0E+00

R1

8.0E-01 6.0E-01 4.0E-01 2.0E-01

R3 R2

0.0E+00 0.00E+00

R4

1.00E+08

R5

2.00E+08

Time (s)

SEMI-BRITTLE BEHAVIOR AROUND UNDERGROUND MINING EXCAVATIONS

Figure 7: Roof vertical convergence (downward) over time for rooms 1 to 5 (R1, R2, R3, R4, R5) simulated with ISVSH-D and ISV-SH (see text for details).

The influence of a semi-brittle response of rock salt around underground mining excavation is investigated through numerical simulations using the FDM software FLAC. Both ISV-SH and ISV-SH-D models were implemented in the code using the built-in scripting language fish. A conventional 5-room panel with openings created in sequence is simulated here to investigate the stress redistribution (not shown here) and amplitude of room convergence. The simulations are based on a similar investigation previously conducted with the ISV-SH model (Aubertin et al. 2023).

The damage profile developing around a mining opening is further investigated by simulating the excavation of a tall 30 meters room (15 m in width) associated with vertical benching operation. Figure 6 shows the conceptual representation of the simulated model analysed with FLAC. The natural (horizontal and vertical) stresses were set at 20 MPa, which is large enough to induce a significant semibrittle response of the rock salt near the opening. The excavation model response was simulated over a period of 1 year.

6

15 MPa

15 MPa

15 MPa

Figure 6: Conceptual representation (not to scale, vertical section) of a five-room mine panel with openings created in sequence in the simulations with FLAC. The rooms were excavated from left to right at 30-day intervals. Symmetry axis

(a)

20 MPa

(b)

75 m 180 days excavation

7.5 m

300 m

30 m

20 MPa

0

0.5

Figure 6: Conceptual representation (not to scale, vertical section) of a 30 meters high (half-width of 7.5 m) benched room in conventional room and pillar mine settings. Dimensions taken from Hoentszch et al. (2019) and Aubertin (2020).

𝐷

Figure 7 presents the simulated contours of the deviatoric and minor principal stresses after 180 days from the opening excavation; the damage variable 𝐷 around the benched excavation is also, after 1 day and after 180 days, is also shown. Damage contour plots exhibit a small concentrated damage zone near corners of the excavation shortly after the excavation. The later stage exhibits progressive damage envelopes extending deeper inside the rock mass.

0

𝜎̅ (MPa)

19 (c)

30 𝜎 (MPa) 0 3 (d)

180 days 1 day excavation excavation Figure 7: Contours of the (a) deviatoric (von Mises) stress and (b) minor principal stress after 180 days; and contour of the damage variable D after (c) 1 day and (d) 180 days.

These simulation results exhibit the characteristic damaged zones which are commonly observed in mine workings with tall rooms and near pillars (e.g. Van Sambeek et al. 1993, Van Sambeek 1996). Such damage tends to become negligible deeper inside the rock mass (away from the walls), where the mean (confining) stress is large enough to prevent microcracking. 7

DISCUSSION AND CONDLUDING REMARKS

The term cataclastic flow (or damage) used above to characterize the semi-brittle behavior of rock salt generally refers to permanent straining of rocks due to distributed fracturing. It is also used for relatively brittle rocks exhibiting strains that follow the relative movement of broken fragments induced by micro and macrofractures (without intracrystalline process contribution). In low porosity soft rocks, observations indicate that ductile processes occur at the same time as distributed microcraking develops, leading to a different type of semibrittle behavior. It is also noted here that a brittle-ductile transition is sometimes identified from the experimental stress-strain curves (mostly on hard rocks), with the(so-called) ductile behavior attained at relatively high confining pressure when deviatoric strains occur without softening, despite general rock fracturing (e.g. Paterson and Wong, 2005; Davarpanah et al. 2023). This type of (apparent) ductile behavior is however different than the fully plastic and semi-brilltle regimes considered here to assess and model the inelastic response of rock salt and other low porosity soft rocks. The work presented in this article extends a strainhardening constitutive formulation with an internal state variable, developed for a ductile time-dependent behavior, to account for material response in the semi-brittle regime. The proposed ISV-SH-D model is applied to the semi-brittle behavior of rock salt by adding a damage component, with related strains and damage weakening effect. The damage strain rate is evaluated explicitly as a function of the minimum principal stress and computed as a ratio of the viscoplastic (ductile) strain rate. Damage weakening is implemented by incorporating the net (deviatoric) stress and net (Young’s) elastic modulus in the calculation of the inelastic strain and strain rate. Numerical simulations are conducted to illustrate the influence of damage on room convergence in conventional salt mining conditions. The proposed model captures the characteristic damage zone features observed in mine workings. It is also shown that the damage variable induces non-negligible changes to the convergence rate and overall pattern of the time-dependent inelastic deformation. The stress redistribution is also altered, with the resulting convergence rate becoming more pronounced. The damage variable in the ISV-SH-D model is deemed irreversible over the (relatively small) time period considered here, but some healing could be taken into account for other applications. The ISV-SH-D model formulation is loosely based on the more elaborate SUVIC-D model (Aubertin et al. 1998b), which is also much more complex and challenging to apply for the analysis of mining excavations. The simplifications

adopted here means that some aspects of the semi-brittle response rock salt are not taken into account, including for instance the directional nature of hardening and damage, which would require tensorial variables instead of the scalar (isotropic) formulation applied above. Also, the additional strains due to damage could lead to macroscopic failure (with spalling), as is sometimes observed in the field. The resulting failure, not considered here, would lead to different material (discontinuous) properties (e.g. 𝐸̃ = 0) that would further influence the stress redistribution and convergence pattern; additional work is planned to assess the implications of such conditions. The influence of ground support systems is also of interest (Aubertin and Aubertin 2021). Despite these limitations, the ISV-SH model and newly proposed ISV-SH-D model, which incorporates the semibrittle regime, offer an accessible and practical solution to evaluate the complex time-dependent behavior of low porosity soft rocks such as rock salt and potash in conventional mine settings. The ISV-SH-D model is a useful and relatively simple solution now implemented in FLAC, which is deemed capable of representing key features of the characteristic response of rock salt encountered in conventional salt mining layouts. 8

REFERENCES

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Representation of progressive shear stress of rock joints using the updated complete stress – displacement surface model, CSDS Akram Deiminiat1 and Jonathan D. Aubertin1 1Department of Construction Engineering, École de Technologie Supérieure (ÉTS), Montréal, Canada ABSTRACT Rock masses are composed of discontinuities and intact rocks. Previous studies have shown that rock mass deformations include not only macroscopic behaviour, but also microcracks that initiate, propagate, connect macrocracks and finally induce rock failure. It is thus vital to incorporate geo-mechanical characteristics of rock joints and normal loading into rock joint deformation model. The complete stress-displacement surface model, CSDS, describes the full shear behavior of rock joints subjected to differential loads. In this study, the model is updated by proposing a procedural stepwise method to calibrate CSDS model and effectively represent full shear stress of loaded rock joints by considering geo-mechanical properties. In addition, the proposed method is used with the plane failure analysis to describe progressive shear stress of large-scale rock slides using monitoring deformation data. The updated calibration method is then validated by existing experimental data and a real case study. The results show trustworthy of the proposed model. RÉSUMÉ Les masses rocheuses sont composées de discontinuités et de roches intactes. Des études antérieures ont montré que les déformations des masses rocheuses comprennent non seulement le comportement macroscopique, mais aussi les microfissures qui s'initient, se propagent, connectent les macrofissures et finalement induisent la rupture de la roche. Il est donc essentiel d'intégrer les caractéristiques géomécaniques des joints rocheux et la charge normale dans le modèle de déformation des joints rocheux. Le modèle de surface complète de contrainte-déplacement, CSDS, décrit le comportement de cisaillement complet des joints rocheux soumis à des charges différentielles. Dans cette étude, le modèle est mis à jour en proposant une méthode procédurale par étapes pour calibrer le modèle CSDS et représenter efficacement la contrainte de cisaillement complète des joints rocheux chargés en tenant compte des propriétés géo-mécaniques. En outre, la méthode proposée est utilisée avec l'analyse de défaillance plane pour décrire la contrainte de cisaillement progressive des glissements rocheux à grande échelle en utilisant les données de surveillance de la déformation. La méthode d'étalonnage mise à jour est validée par des données expérimentales existantes et une étude de cas réelle. Les résultats montrent que le modèle proposé est digne de confiance. 1

INTRODUCTION

Fukuzono (1985) probably was the first, who introduced a method to monitor rock slope deformation. The method addressed the problem with the prediction of rock face failures, and it was later used by other researchers (Crosta and Agliardi, 2003; Sharon et al., 2005; Rose and Hungr, 2007). Nevertheless, periodic variation in shear stress has never been studied due to the limitation of the available shear strength criteria. Numerous criteria have been proposed to predict peak and residual shear strength of rock fractures (Fortin et al., 1988; Maksimović, 1992; Saeb and Amadei, 1992; Homand et al., 2001; Grasselli and Egger, 2003). It is emphasized, based on field observations of the continuous and progressive movement of rock instabilities at large scale, that the pre and post peak shear stress profile is necessary for studies in various rock engineering problems (Martin and Chandler, 1994; Fairhurst and Hudson, 1999; Simon, 1999; Simon et al., 2003). A complete shear stress – shear displacement surface model, named CSDS, was proposed by Simon (1999). The model describes post-peak shear stress curve using model parametrization and laboratory shear test data. The original formulation of CSDS required extensive iterative curve

Rock masses consist of structural fractures and joints that separate intact rock components. The mechanical properties of these interfaces are important to determine overall behavior and assess stability around construction and excavations. The present article investigates a numerical formulation to describe the behavior of loaded rock interfaces with practical implications for field monitoring. The potential for failure of rock slopes is primarily due to the shear strength of joint surfaces under loading (Ladanyi and Archambault, 1969; Barton, 1982; Bandis et al., 1983). Understanding the shear behavior of structural interfaces is thus crucial for safe and stable rock slopes.Slope stability evaluation methods can be categorized into four groups: kinematic analysis, limit equilibrium method, numerical modeling, and empirical methods (Stead et al., 2006; Ataei and Bodaghabadi, 2008; Basahel and Mitri, 2017; Amagu et al., 2021). These techniques help to assess the stability of slopes and their potential for failures.

1

fitting which limited its applicability and relatability to physical phenomenon and parameters. Recent work has been undertaken by the authors of this article to update this model and correlate it with parameters of physical meanings, to offer to rock engineering community a complete shear stress-displacement numerical tool. The present work describes an updated version of the CSDS model with laboratory measurement-based calibrations and parameters corresponding to physical components. These changes improve the model parameter determination which otherwise requires extensive curve fitting. The updated model was used to showcase preliminary applicability to large scale rock slope instability problems though a simplified plane failure case study. 2 2.1

2.2

Some modifications are suggested to make the CSDS model calibration easier. The update version incorporates proven formulas into CSDS model for several model parameters that may otherwise it needs extensive curve fittings and trial and error. The formulas are shortly presented in the following section. 2.2.1

𝐽𝐶𝑆 −0.68 𝑉𝑚 = 8.57( ) 𝑎𝑗 𝐽𝐶𝑆 𝐾𝑛𝑖 = 0.02 ( ) + 1.75𝐽𝑅𝐶𝑝 − 7.15 𝑎𝑗

The CSDS Model

𝑎𝑗 =

(1)

(2) (3)

𝑑= exp (−

5𝑢𝑝 )] 𝑢𝑟

5𝑢𝑝 ) − exp (−𝑒𝑢𝑝 ) 𝑢𝑟

𝑏 = 𝑑−𝑎 𝑑𝑒𝑢𝑟 5 − exp [𝑢𝑝 (𝑒 − )] = 0 5(𝑑 − 𝑡𝑟 ) 𝑢𝑟

(4)

2.2.2

(5)

𝐽𝑅𝐶𝑝 𝜎𝐶 (0.2 − 0.1) 5 𝐽𝐶𝑆

(13)

Peak shear strength criterion after Barton and Choubey (1977)

Barton and Choubey (1977) proposed the commonly known peak shear strength criterion by considering the joints roughness at the peak state and joint compressive strength as follow.

(6)

where p and r are peak and residual strengths (MPa), respectively. The up and ur are displacements at the peak and residual shear stress, respectively (mm). These parameters are usually measured by conventional experiments. The CSDS model also describes normal displacement – shear displacement profile based on the content behind the maximum normal closure, Eq. 7 𝑉 = 𝛽1 − 𝛽2 exp(−𝛽3 𝑢)

(12)

where c is uniaxial compressive strength (MPa) and JCS is joint compressive strength (MPa). These equations are incorporated to CSDS model to facilitate the calculation of 1 and 2 in Eqs. 8 and 9, and to ensure the influence of joint deformation is considered in the analyses.

where a, b, c, d and e are model parameters with the condition of a, b, c, d, e > 0 and c < e.

𝜏𝑝 − 𝜏𝑟 [1 − exp (−

( 11)

where aj is initial joint aperture (mm) and it can be obtained by Eq. 13.

The CSDS model proposes an exponential equation to describe the shear stress – shear displacement surface profile (Simon, 1999).

𝑎 ≈ 𝜏𝑟 𝑐 ≈ 5/𝑢𝑟

Normal Closure Model (Bandis et al. 1983)

Bandis et al. (1983) proposed an exponential relationship to describe maximum closure and initial normal stiffness by considering the geophysical properties of joints (i.e., joint aperture (aj) and JRC) as follow.

COMPLETE SHEAR STRESS – DISPLACEMENT SURFACE MODEL (CSDS)

𝐹(𝑢) = 𝜏 = 𝑎 + 𝑏𝑒𝑥𝑝(−𝑐𝑢) − 𝑑𝑒𝑥𝑝(−𝑒𝑢)

Updated CSDS Model

𝐽𝐶𝑆 𝜏𝑝 = 𝜎𝑛 tan [𝐽𝑅𝐶𝑝 𝑙𝑜𝑔 ( ) + 𝜑𝑏 ] 𝜎𝑛

(14)

In the updated CSDS model, the peak shear stress obtained from laboratory tests and/or obtained from Eq. 1 is considered equal to the p obtained by Eq. 14. The Barton model is used in the present study due to easy estimation of JRC and its worldwide application in the engineering field.

(7)

where 𝛽1 , 𝛽2 , and 𝛽3 are model parameters that can be determined by following equations.

2.2.3

𝜎𝑛 𝑘 𝜎𝑛 𝑉𝑚 ) 2 tan𝑖0 + 𝜎𝑐 𝐾𝑛𝑖 𝑉𝑚 − 𝜎𝑛 𝜎𝑛 𝑉𝑚 𝛽2 = 𝛽1 − 𝐾𝑛𝑖 𝑉𝑚 − 𝜎𝑛 1.5 𝛽3 ≅ 𝑢𝑟

Barton (1982) proposed mobilized shear strength model that is originally taken from Barton peak shear strength criterion with the replacement of p with m and JRCp with JRCm. This model considers the progressive degradation of joint roughness during the shear process as follow.

𝛽1 = 𝑢𝑟 (1 −

(8) (9) (10)

2

Mobilized Shear Strength after Barton (1982) and Asadollahi (2009)

𝐽𝐶𝑆 𝜏 = 𝜎𝑛 tan (𝐽𝑅𝐶𝑚 𝑙𝑜𝑔 ( ) + 𝜑𝑟 ) 𝜎𝑛 𝜏𝑚 arctan ( ) − 𝜑𝑟 𝜎𝑛 𝐽𝑅𝐶𝑚 = 𝐽𝐶𝑆 ) 𝑙𝑜𝑔 ( 𝜎𝑛

- For all the data points on the post – peak curve, the u/up (or ε/εp) is calculated and added into Eq. 17 to determine JRCm. - To calculate the CSDS model parameters, 1, 2 and 3 using Eq. 19 from the updated model and Eqs. 9 and 10 from Section 2.1. - The axial strain on the post peak stress – strain curve is a function of several parameters, Eq. 20 (Simon et al. 2003): ∆𝑢𝑐𝑜𝑠𝛽 ∆𝑉𝑠𝑖𝑛𝛽 ∆𝜎1 𝜀 = 𝜀𝑝 + − + (20) 𝐿 𝐿 𝐸

(15) (16)

where  is the mobilized shear stress (MPa) and r is residual friction angle (°). Since Eq. 15 calculates the mobilized shear stress without taking the displacement into account, Barton (1982) proposed a simple relationship between JRCm/JRCp and u/up that is defined through a data series. Using the table and the up, JRCp and u values taken from experimental data, JRCm can be obtained. Asadollahi (2009) also modified Barton’s model as follow to obtain more complete shear stress – displacement curve. The model was later validated by Asadollahi and Tonon (2010). 𝐽𝑅𝐶𝑚 𝑢 = ( )−0.381 𝐽𝑅𝐶𝑝 𝑢𝑝

(17)

𝜎𝑛 0.34 𝐽𝐶𝑆 𝑢𝑝 = 0.0077𝐿0.45 ( ) cos(𝐽𝑅𝐶𝑝 𝑙𝑜𝑔( )) 𝐽𝐶𝑆 𝜎𝑛

(18)

where ε is the axial strain, εp is peak strain, Δu is difference in shear displacement (mm), ΔV is difference in normal displacement (mm), Δ1 is difference in main axial stress (MPa), L is the initial sample length (mm), E is the elastic modulus of rock (MPa) and  is the shear plane angle (°). If one subtracts the peak and elastic strains from Eq. 20 and rewrite the equation in terms of Δu1, Eq. 21 can be obtained for each data on the post peak stress - strain curve. At 1st point, ΔV1 = V1 = 0. Eq. 21 becomes: ∆𝜎1,1 ∆𝑢1 = (𝜀1 − 𝜀𝑝 − ) ∗ (𝐿/𝑐𝑜𝑠𝛽) 𝐸 Δu1 = u1

where L is the specimen length (m).

At 2nd point, ΔV2 = V2 – V1 From Eq. 20, ∆𝜎1,2 ∆𝑉2 𝑠𝑖𝑛𝛽 ∆𝑢2 = (𝜀2 − 𝜀𝑝 − + ) ∗ (𝐿/𝑐𝑜𝑠𝛽) 𝐸 𝐿 u2 = Δu2 + u1

Analysis on the comparison between these models and the shear stress – displacement curve obtained from experimental tests showed that both models can predict well. So, Eqs. 15, 16 and 18 are incorporated into the updated CSDS model, and 1 can be obtained easier and more precise through Eq. 19 𝜎𝑐 𝛽1 = 𝑢𝑟 tan (𝐽𝑅𝐶𝑚 log ( )) 𝜎𝑛 3

(22)

This procedure is repeated for all the data points. - The obtained values for u1 to un (n: number of the data point on the post peak curve) are used with Eq. 1 to obtain the progressive shear stress. - The shear stress versus u profile obtained by these data is comparable to that obtained by direct shear test. In the absence of direct shear test data, an alternative method given in the following steps can be used to determine model properties and CSDS model parameters.

(19)

UPDATED CSDS MODEL CALIBRATION

Since CSDS model can be calibrated via conventional laboratory tests, the updated calibration method can be used to describe full shear stress-displacement profile, full normal displacement – shear displacement curve and full axial stress – strain profile. Below are special cases reflecting typical laboratory testing programs, and the corresponding calibration procedure. 3.1

(21)

- The JRCp obtained by triaxial test data, is used with the back calculation of Eq. 14 to obtain p. - Since the residual friction angle is almost equal to that obtained by direct shear tests (Khosravi 2016), the r is used in Mohr-Coulomb equation with cohesion equal to zero to obtain r. - Since JRCp is known from Step 1 and JCS is known from uniaxial tests, up can be calculated by Eq. 18. - With applying curve fitting and back calculation of Eq. 1, the displacement corresponding r can be considered as residual displacement.

Full Shear Stress – Displacement Curve with Triaxial Compression Test, with/without Direct Shear Test

When direct shear test data are available, CSDS model properties such as p, r, up and ur can be directly extracted from the test data and used to calculate the model parameters (i.e., a, b, c, d and e). Having the model parameters, the following steps can be carried out with the use of triaxial and axial test data.

3.2

Full Shear Stress – Displacement Curve with Direct Shear Test

When direct shear test data is available, model properties are directly extracted from measurements. Consequently, CSDS model parameters can be calculated. Adding the

- To obtain JRCp using the back calculation of Eq. 14. - The aj, Vm and Kni values are determined from Eqs. 11, 12 and 13, respectively.

3

displacement values into Eq. 1, the progressive shear stress can be determined. For the cases when some of the model properties such as r, ur and etc. are not available from experimental data, the proposed equations that are incorporated to the CSDS model, mechanical properties of rock sample such as b, JCS (or c) and E obtained by uniaxial compressive test, and curve fitting technique can be used to obtain model parameters.

The application of new CSDS model calibration method is exemplified in this section for the full shear stressdisplacement profile. Direct shear and triaxial compression test data are taken from Khosravi. (2016) and Wang et al. (2016).

3.3

Triaxial and uniaxial compression test data of a type of rock obtained by Khosravi (2016) and direct shear test results of the same rock obtained by Khosravi and Simon (2018) at normal loads of 5 and 8 MPa are used in this section. Tables 1 and 2 show the rock characteristics and direct shear test data used for the analysis, whereas Table 3 presents the model properties obtained in this study using the proposed model.

4.1

Full Axial Stress – Strain Curve with Triaxial Compression Test

The method for estimation of post peak stress – strain curve proposed by Simon (1999) is used in this section with some modifications. In this method, once CSDS model parameters are obtained by triaxial and uniaxial compressive test data, following steps can be taken:

Table 1 Rock properties taken from khosravi (2016)

- For the data points on the pre and post peak stress – strain curves, shear stress and normal stress are first determined by using Eqs. 23 and 24. 1

1

2

2

𝜎𝑛 = (𝜎1 + 𝜎3 ) − (𝜎1 − 𝜎3 )𝑐𝑜𝑠2𝛽 1

𝜏 = (𝜎1 − 𝜎3 )𝑠𝑖𝑛2𝛽 2

B (°)

E (GPa)

(MPa)

T

So (MPa)

(°)

b

i (°)

24

17

45.4

280

33

53

33

(MPa)

n

up (mm)

ur (mm)

(MPa)

(MPa)

5

0.39

6.0

6.3

3.5

8

0.44

6.0

9.9

6.9

r

r

0 (°)

(°)

48

46

(MPa)

n

up (mm)

ur (mm)

(MPa)

(MPa)

5

0.51

6.7

5.8

3.54

8

0.5

6.0

8.9

6.8

p

r

0

r

(°)

(°)

46.6

43.5

Figure 1 shows a compression between the full shear stress – displacement curve obtained by direct shear test and those obtained by the modified CSDS model for normal loads of 5 and 8 MPa. n = 5 MPa

8

(25)

6 4

(26)

2

where εpre-peak is the axial strain before peak.

Direct shear test Modified CSDS

0 4

p

Table 3 Model properties obtained in this study by CSDS model

Shear stress (MPa)

∆𝑢𝑐𝑜𝑠𝛽 ∆𝑉𝑠𝑖𝑛𝛽 ∆𝜎1 − + 𝐿 𝐿 𝐸

3

Table 2 Direct shear test results taken from Khosravi and Simon (2018) for two normal stress values

(24)

- The Axial strain is then calculated using Eq. 26. 𝜀𝑝𝑟𝑒−𝑝𝑒𝑎𝑘 = 𝜀 +

(MPa)

(23)

where 1 is the principal stress (MPa); 3 is the minor stress (MPa). - With the application of linear solver in Excel and Eq. 1, shear displacement corresponding to the obtained shear stress can be calculated. For each shear stress, there are two values for displacement. Depending on the pre or post peak curve, the displacement corresponding to that part of the curve must be used. - For post peak curve, the displacement larger than up must be used. - Model parameters 1, 2 and 3 are calculated by Eq. 19 of the updated model and Eqs. 9 and 10 from Section 2.1. - Using the predicted u values, 1, 2 and 3 in Eq. 7, the normal displacement (V) is calculated. - The axial strain for post peak is then determined by Eq. 20. - For pre peak curve, the predicted displacement smaller than up must be used. Step 4 is then repeated for the data points on the pre peak curve. Since the volume change in pre peak zone is positive, Eq. 25 must be used to obtain normal displacement. 𝑉 = −( 𝛽1 − 𝛽2 exp(−𝛽3 𝑢))

Full Shear Stress – Displacement Curve with Triaxial Compression Test, with/without Direct Shear Test

0

VERIFICATION OF THE UPDATED CSDS MODEL

4

2

4 6 8 Shear displacement (mm)

10

12

12

10 Shear stress (MPa)

Figure 2 A comparison on the full shear stress – displacement curves obtained by direct shear test (DST) and modified CSDS model without DST; experimental data are taken from Khosravi and Simon (2018) for normal loads of 5 MPa and 8 MPa

n = 8 MPa

8 6

As shown in Figure 2, the full shear stress profiles obtained for two normal loads are precisely predicted by the proposed method using the alternative. Nevertheless, the predicted curve for larger normal load is better than that obtained for normal load of 5 MPa. The results tend to indicate that when direct shear test data is not available, the alternative method can be used to describe the full shear profile. However, further verification with using the experimental data of various materials under different normal loads is required to check if the conclusion given here remains consistent.

4 Direct shear test

2

Modified CSDS

0

0

5 10 Shear displacement (mm) Figure 1 Comparisons between full shear stress – displacement curves obtained by direct shear test (DST) and those obtained by modified CSDS model; experimental data are taken from Khosravi and Simon (2018) for normal loads of 5 MPa and 8 MPa

4.2

Figure 1 reveals that the model can predict the original curve precisely for the two normal loads. A comparison between Tables 2 and 3 lead to the same conclusion. The data given in Tables 1 and 2 are used here again to further validate the model without using direct shear test data. Table 4 shows the model properties obtained by the updated CSDS model and Figure 2 indicates a comparison on the shear stress curve obtained by direct shear test and SCSD model with using the alternative method.

The application of CSDS model is exemplified in this section with the use of direct shear test results obtained by Wang et al. (2016) for a rock joint with JRC of 19.05. Table 5 shows mechanical properties of the rock sample obtained under the normal load of 2 MPa and deformation rate of 6 mm/s. Based on the available data, appropriate method among those described in Section 3 was used here to obtain the full shear stress curve. Table 6 shows the data obtained by the new calibration method of CSDS model and Figure 3 exhibits comparison between the laboratorybased shear profile and that obtained by the proposed calibration method.

Table 4 The model parameters obtained in this study by using the modified CSDS method without direct shear test data n

up (mm)

ur (mm)

5

0.51

8

0.5

Shear stress (MPa)

(MPa)

p

r

(MPa)

(MPa)

5.4

5.8

4.5

5.5

10.7

7.2

r

0 (°)

Table 5 Mechanical characteristics of the rock sample, taken from Wang et al. (2016)

(°) 42 .0

53.0

n = 5 MPa

8

Full Shear Stress – Displacement Curve with Direct Shear Test

6

Young's modulus

Poisson's ratio

UCS (MPa)

10.35

0.166

41.8

Triaxial compression stress C (MPa)

Øpeak (ᵒ)

8.78

44.01

b

(ᵒ) 46

Table 6 The CSDS model properties obtained in this study

4 2

Direct shear test Modified CSDS without DST…

n

(MPa)

0 0

2

4 6 8 10 Shear displacement (mm)

2

12

p (MPa)

up (mm)

(MPa)

ur (mm)

JRCc-

5.0

4.1

4.04

15.04

6.7

r

mob

6

n = 8 MPa

Shear stress (MPa)

Shear stress (MPa)

15 10 5

Direct shear test Modified CSDS without DST data

0

0

2

4 6 8 Shear displacement (mm)

10

5

4 3 2 Experimental shear test CSDS model

1 0

12

0

5

5 10 15 Shear displacement (mm)

20

Figure 3 Full shear stress – displacement profiles obtained by the experimental shear tests taken from Wang et al. (2016) and the new calibration method proposed in this study.

To obtain the shear behavior of rock slide at or close to the point 4 by the CSDS model, mechanical properties of the rock and normal load acting on the rock slide should be obtained if they are not available. In this case study, the mechanical characteristics of the rock were obtained by the triaxial and uniaxial shear tests that are used in this study and presented in Table 7.

The accuracy of the proposed method for the full shear stress – displacement curve can be seen again in Figure 3. The full shear behavior of rock joints obtained in this study exhibit the same trend as that of the original curve. 5

Table 7 Mechanical properties of rock (Kundu et al., 2017)

APPLICATION OF UPDATED CSDS MODEL FOR LARGE SCALE ROCK SLIDES

The verified updated CSDS calibration method is used in this section along with the plane failure analysis to describe progressive shear behavior of large-scale rock slides. The application of suggested method is exemplified here using deformation data that were monitored and reported by Kundu et al. (2017) for one of the joints occurred along the rock slope face. The slope has 70 m height with a slope angle of 60ᵒ, while the joint has a dip angle of 55ᵒ. Figure 4 shows geometry of the rock slide. As seen, there are 4 monitoring points along the rock face. The deformation rate obtained by monitoring point 2 is used for the analysis, which is shown in Figure 5.

Unit weight (kg/m3)

Uniaxial compressive strength (MPa)

2700

90

Triaxial compressive strength (rock face)

Direct shear test (Rock joint)

C (MPa)

Ø (ᵒ)

C (MPa)

Ø (ᵒ)

0.4

42

0.05

34

To determine normal loading, the plane failure analysis proposed by Hoek and Bray (2005) is used in this section. Table 8 shows geometry of the sliding plane that is extracted from Figure 4, and the plane failure analysis for monitoring point 2. Table 8 Geometry of the rock slide at monitoring point 2 and the plane failure analysis

Upper slope H (m)

Z (m)

A (m2)

p (ᵒ)

f (ᵒ)

W (N)

23

22.14

1.05

55

60

84.7

n

(MPa) 0.0008

Using the mechanical properties of rock and normal loading with the updated CSDS model presented in Section 2.2, p, JRCp, up, r and ur are estimated and presented in Table 9. Table 9 CSDS model properties obtained by CSDs model

Figure 4 Geometry of the studied rock face (Kundu et al. 2017), the monitoring point 2 located almost in the upper slope

(MPa)

p

up (cm)

(MPa)

r

ur (cm)

JRCp

2

0.052

0.015

0.032

0.0044

9.3

Since the model properties are known from Table 9, the model parameters (i.e., a, b, c, d, e and d) are calculated and used with the monitoring deformation to determine progressive shear stress for the monitoring period. In this case study, the shear stress curve at points 5 to 8 (see Figure 4) were determined using DEM simulation. The curve obtained at Point 6, which is located close to the monitoring point 2, is used here for the verification of shear stress curve. Figure 6 shows comparisons on the shear stress curve taken from Kundu et al., (2017) and that obtained by the proposed method. As seen, the methodology used in this study results in a shear stress profile that is fit to that taken from Kundu et al. (2017).

0.2 Monitoring point 2

Displacement (cm)

Monitoring point

0.15 0.1 0.05 0

0 0.5 Time (s)1 1.5 2 Figure 5 Displacement versus time monitored at point 2, data taken from Kundu et al., (2017)

6

Shear stress (MPa)

0.06 0.05

7

0.04

The authors acknowledge the financial support from Natural Sciences and Engineering Research Council of Canada through its Discovery Grant program (RGPIN 2022-03893), and École de Technologie Supérieure (ÉTS) construction engineering research funding.

0.03

0.02 Kundua et al. (2017) CSDS model

0.01

ACKNOWLEDGEMENT

0 0

0.5

1 Time (s)

1.5

2

8

Amagu, C.A., Zhang, C., Kodama, J.I., Shioya, K., Yamaguchi, T., Sainoki, A., Sharifzadeh, M. 2021. Displacement measurements and numerical analysis of Long-Term rock slope deformation at Higashi-Shikagoe limestone quarry, Japan. Advances in Civil Engineering, 2021: 1-15. Asadollahi, P. 2009. Stability analysis of a single threedimensional rock block: effect of dilatancy and highvelocity water jet impact. PhD Thesis. University of Texas at Austin, USA. Asadollahi, P., Tonon, F. 2010. Constitutive model for rock fractures: Revisiting Barton's empirical model. Eng. Geol. 113(1-4): 11-32 Ataei, M., Bodaghabadi, S. 2008. Comprehensive analysis of slope stability and determination of stable slopes in the Chador-Malu iron ore mine using numerical and limit equilibrium methods. Journal of China University of Mining and Technology, 18(4): 488-493. Bandis, S.C., Lumsden, A.C., Barton, N.R. 1983. Fundamentals of rock joint deformation. International Journal of Rock Mechanics and Mine Science & Geomechanical Abstract., 20(6): 249-268. Barton, N., Choubey, V. 1977. The shear strength of rock joints in theory and practice. Rock Mechanic. 10(1): 154. Barton, N. 1982. Modelling rock joint behavior from in situ block tests: implications for nuclear waste repository design (Vol. 308). Office of Nuclear Waste Isolation, Battelle Project Management Division, Columbus, Ohio: 96. Basahel, H., and Mitri, H. 2017. Application of rock mass classification systems to rock slope stability assessment: A case study. Journal of rock mechanics and geotechnical engineering, 9(6): 993-1009. Crosta, G.B., and Agliardi, F. 2003. Failure forecast for large rock slides by surface displacement measurements. Canadian Geotechnical Journal, 40(1): 176-191. Fairhurst, C., and Hudson J. 1999. Draft ISRM suggested method for the complete stress-strain curve for intact rock in uniaxial compression. International Journal of Rock Mechanics and Mine Science. 36(3): 279-289. Fortin, M., Archambault, G., Aubertin, M., Gill, D.E. 1988. An algorithm for predicting the effect of a variable normal stiffness on shear strength of discontinuities. In Proceeding of the 15th Canadian Rock Mechanic Symposium, Toronto, Canada, pp. 109-117. Fukuzono, T. 1985. A new method for predicting the failure time of a slope. In Proceedings of 4th International

Figure 6 A comparison between shear stress profile taken from Kundu et al. (2017) and that obtained by CSDS model versus time for monitoring point 2. 6

REFERENCES

DISCUSSION AND CONCLUSIONS

The present work’s aim was to contribute to rock engineering problems with rock slope stability. To this purpose, the complete shear stress – displacement surface model, called CSDS, was first modified to improve the calibration method. Using the proposed method, certain model parameters were obtained by the proven formulas that may otherwise require extensive curve fitting. Then, the updated CSDS model was used along with the plane failure analysis and monitored deformation rate to describe progressive shear stress for large scale rock slides. Validation of the new CSDS model calibration method showed that the proposed method could precisely describe the full shear stress profile of rock joints with the use of triaxial compression tests with/without direct shear test data. In addition, the incorporation of mobilized shear strength criterion and JRC model to the updated CSDS model allowed further development of the model with plane failure analysis, and its application for large scale rock slides. Analyses on a real case study demonstrated that the integrated CSDS model can predict the progressive shear stress for the monitoring deformation of a certain period. Besides the achievements of the current study, there are some limitations. The model was verified by using few experimental data series. Using more laboratory shear test data of different materials obtained under different testing conditions are required to further validate the proposed calibration method and its application for the prediction of shear behavior of rock slides in large scale. Furthermore, the present study only focuses on the plane failure with simple geometry while there are more rock slope failures in the literature such as wedge failure, toppling and son on (Hoek & Bray 2005). Additional work is necessary to develop application of the proposed method for other types of rock slope failures. The normal loading considered in all the analysis is the static load. The dynamic load has not been considered in the proposed method. Since the monitoring displacement rate used in this study was for a short monitoring time, it is necessary to further validate the proposed method using the data of longer monitoring time.

7

Conference and Field Workshop on Landslide., 1985: 145-150. Grasselli, G., and Egger, P. 2003. Constitutive law for the shear strength of rock joints based on threedimensional surface parameters. International Journal of Rock Mechanics and Mine Science. 40(1): 25-40. Hoek, E., and Bray, J.D. 2005, 4th edition. Rock slope engineering. CRC press. Homand, F., Belem, T., Souley, M. 2001. Friction and degradation of rock joint surfaces under shear loads. International Journal of Numerical Analysis Methods Geo-mechanic. 25(10): 973-999. Khosravi, A. 2016. Estimation and validation of post-peak behavior of hard rocks. PhD Thesis. Ecole Polytechnique, Montreal, Canada. Khosravi, A., Simon, R. 2018. Verification of the CSDS model in estimating the postpeak behavior of hard rocks. International Journal of Geomechanic. 18(3), 04017166. Kundu, J., Sarkar, K., Singh, T.N. 2017. Static and dynamic analysis of rock slope–a case study. In ISRM European Rock Mechanics Symposium-EUROCK 2017. OnePetro. Ladanyi, B., and Archambault, G. 1969. Simulation of shear behavior of a jointed rock mass. In The 11th US Symposium on Rock Mechanics (USRMS). OnePetro. Martin, C.D., Chandler, N.A. 1994. The progressive fracture of Lac du Bonnet granite. International Journal of Rock Mechanics and Mine Science & Geomechanical Abstract. 31(6): 643-659. Rose, N. D., and Hungr, O. 2007. Forecasting potential rock slope failure in open pit mines using the inversevelocity method. International Journal of Rock Mechanics and Mining Sciences, 44(2): 308-320. Saeb, S., and Amadei, B. 1992. Modelling rock joints under shear and normal loading International Journal of Rock Mechanics and Mine Science & Geomechanical Abstract., 29: 267–78. Sharon, R., Rose, N., Rantapaa, M. 2005. Design and development of the Northeast layback of the Betze-post open pit. In Proc. of the SME Annual Meeting, Salt Lake City, US. Pre-print: 05-09. Simon, R. 1999. Analysis of fault-slip mechanisms in hard rock mining. PhD Thesis. McGill University, Montreal, Canada. Simon, R., Aubertin, M., Deng, D. 2003. Estimation of postpeak behaviour of brittle rocks using a constitutive model for rock joints. In 56th Canadian Geotechnic Conference. Winnipeg, Canada. Stead, D., Eberhardt, E., Coggan, J.S. 2006. Developments in the characterization of complex rock slope deformation and failure using numerical modelling techniques. Engineering geology, 83(1-3): 217-235. Wang, G., Zhang, X., Jiang, Y., Wu, X., Wang, S. 2016. Rate-dependent mechanical behavior of rough rock joints. International Journal of Rock Mechanics and Mining Sciences, 83: 231-240.

8

Calf Robe Bridge Abutment Stabilization: Case Study Daniel Ferg, Ajay Sharma & Amit Garewal GeoStabilization International Inc, Vancouver, British Columbia, Canada ABSTRACT This technical paper provides a comprehensive account of the abutment stabilization project undertaken by GeoStabilization International (GSI) at the Calf Robe Bridge in Calgary, Alberta. The project involved installation of a discharge conduit through the toe of the bridge abutment to facilitate a downstream discharge from the Bonnybrook Wastewater Treatment Plant (BBWWTP). Before the wastewater infrastructure could be installed, the non-cohesive soils at the abutment needed a bespoke stabilization solution, including the excavation of a 75° slope. Core samples of the underlying bedrock suggested the presence of a possible shear band within the mudstone that interlayered with more competent sandstone. The design-build solution chosen to stabilize the abutment to allow for the installation of conduits is presented in this paper. The solution consisted of the installation of 280 hollow bar soil nails with a 150 mm reinforced shotcrete facing. To mitigate against the potential shear band, the design called for the installation of 118 drilled reinforced concrete shafts. This paper presents the solution and addresses the challenges associated with excavation, installation of soil nails and drilled shafts, load testing methods, and QA/QC testing. Overall, this project is an impressive feat of engineering and an excellent example of GSI's expertise in providing innovative stabilization solutions. RÉSUMÉ Ce document technique fournit un compte rendu complet du projet de stabilisation des culées entrepris par GeoStabilization International Inc. (GSI) au pont Calf Robe à Calgary, en Alberta. Le projet impliquait l'installation d'un conduit de décharge à travers le pied de la culée du pont pour faciliter une décharge en aval de l'usine de traitement des eaux usées de Bonnybrook (BBWWTP). L'excavation d'une pente à 75° dans des sols non cohésifs au niveau de la culée était nécessaire avant l'installation de l'infrastructure d'assainissement et nécessitait une solution de stabilisation sur mesure. Des échantillons de carottes du substrat rocheux sous-jacent ont suggéré une possible bande de cisaillement dans le mudstone qui est intercalé avec du grès plus compétent. La solution de conception-construction choisie pour stabiliser la culée afin de permettre l'installation de conduits est présentée dans cet article. La solution a consisté en la pose de 280 clous de sol en barres creuses avec un parement en béton projeté armé de 150 mm. Pour atténuer la bande de cisaillement potentielle, la conception a nécessité l'installation de 118 puits forés en béton armé . Les défis associés à l'excavation, à l'installation de clous de sol et de puits forés, aux méthodes d'essai de charge, aux tests QA/QC sont également abordés. 1

INTRODUCTION

Located beneath the Calf Robe Bridge in Calgary, Alberta, the project site encompasses the West abutment and Pier 4. The bridge is situated in close proximity to the Bonnybrook Wastewater Treatment Plant (BBWWTP) and serves as a vital access point for Deerfoot Trail, the freeway portion of Alberta Highway 2, and the primary north-south thoroughfare for Calgary. This technical paper provides a detailed account of the slope stabilization undertaken by GSI at the project location. The client requested precise excavation at a 75° slope to accommodate the installation of a 2,400 mm diameter concrete storm drain and two side-by-side box culverts measuring 3,000 mm in height and 3,600 mm in width. The conduits were to be placed in a single trench aligned between the west bridge abutment and pier #4 at the bridge. The scope of work included a detailed design package, supply and installation of soil nails, reinforced shotcrete, and steel-reinforced concrete drilled shafts. This paper discusses the geotechnical conditions and design criteria outlining the limitations and assumptions.

The paper also outlines the operation procedures, including drilling and grouting, verification testing, and construction planning. Throughout the project, engineers closely monitored the site conditions and made modifications to the stabilization plan to ensure a safe and reliable solution. This paper aims to provide a comprehensive understanding of the project's technical aspects, challenges faced, and the solution implemented to address them. 2

ABUTMENT STABILIZATION DESIGN

GSI received a geotechnical evaluation report from the prime contractor, completed by the city’s engineering consultant. which provided a comprehensive background to the project, geotechnical investigation data, sensitivity analysis and geotechnical parameters to be used in detailed design. GSI used the parameters provided by the consultant including a Mohr-Coulomb soil model and a generalized Hoek-Brown bedrock model with an inclusion of a weak layer in the bedrock, susceptible to strain softening. Modelling the bedrock with conservative values and the inclusion of a slip susceptible layer was justified

given the importance of the infrastructure asset being protected.

Table 1. Geotechnical Design Material Properties Reference Parameter

Granular Fill

Native Sand and Gravel

Overall Bedrock Formation

Weak Laminated Zone

Global stability of the site and abutment/pier soil displacements resulting from the proposed construction sequencing were analyzed using the finite element analysis software PLAXIS 2D 2020. The PLAXIS model created for this project was focused on capturing the behavior of the temporary shoring elements and associated construction stages to understand how the proposed excavation may result in settlement or lateral displacement in the west abutment and pier #4, and soil around the abutment and pier. The minimum static global slope stability factor of safety (FS) targeted in the design is 1.25 for short-term, temporary construction loading conditions. Pier 4 loading of 210 kPa (includes weight of concrete) along with the peak bedrock condition; reflective of the characteristic loading condition, and Pier 4 loading of 300 kPa (includes weight of concrete) along with the residual bedrock condition; reflective of the foreseeable worse-case condition were considered for the design analysis.

Unit Weight (kN/m3)

22

22

23

23

Peak Friction Angle, φ’ (degrees)

32

34

N/A

25

Residual Friction Angle, φ’ (degrees)

N/A

N/A

N/A

12

Effective Cohesion, C (kN/m2)

0

0

N/A

0

Reference Elastic Modulus at 50% Strain (kN/m2)

26.3E3

31.1E3

-

1.0E6

Reference Elastic Modulus Constrained (kN/m2)

26.3E3

31.1E3

-

-

Reference Elastic Modulus During Unload/Reload (kN/m2)

79.0E3

93.4E3

-

-

Reference Small Strain Shear Modulus (kN/m2)

79.0E3

85.6E3

-

-

Unconfined Compressive Strength (MPa)

-

-

1.0

-

2.2

Geological Strength Index

-

-

30

-

Material Constant

-

-

5

-

2.1

Design Criteria

Soil and Rock Elements

The subsurface profile used in the modeling and analysis was based on the field and laboratory data provided in the geotechnical report by the client’s consultant, and our experience in similar soil units. The granular fill and native sand and gravel unit identified at the site were modeled using the Hardening Soil small (HSsmall) constitutive model. A key benefit of the HSsmall model is that it provides an adjustable shear modulus degradation curve based on the Hardin-Drnevich relationship (PLAXIS 2014). The HSsmall model includes a stress-dependent stiffness formulation, as well as shear hardening and compaction (cap) hardening in primary loading. The reference stress used to initialize stress conditions was calibrated based on available field data, typical values in the literature, and GSI’s experience in similar materials. The overall sedimentary rock formation identified at the site was modeled using the Hoek-Brown constitutive model. A benefit of the Hoek-Brown model is that it provides a better non-linear failure criterion for the strength of rocks as opposed to the linear Mohr-Coulomb failure criterion. In order to analyze the impact of the stress state within the rock mass on the effectiveness of the existing west abutment and pier #4, as well as the proposed stabilization elements, the conventional MohrCoulomb constitutive model was employed to model the weak laminated mudstone zone found at the site between Elevation 1018.6 and 1018.1 meters (m). The shear strength profiles for the site soils and bedrock formation were defined based on the material properties identified in the consultant’s Geotechnical Report. Table 1 below summarizes the estimated engineering properties for each unit identified at the site.

Nominal (ultimate) bond stress values for subsurface materials were estimated based on the material types and tables in the Federal Highway Administration (FHWA) GEC Circular No.4 (1999), FHWA GEC Circular No.5 (2002), and FHWA GEC Circular No.7 (2015), as well as our experience with similar material types. In PLAXIS, a linear and material dependent function was used to account for the bond reduction in the weak laminated zone within the bedrock formation. Granular Fill – 73 kPa, Native Sand and Gravel – 73 kPa and Bedrock – 200 kPa. 2.3

Structural Elements

The slope stabilization plan consisted of soil nails, drilled shafts, and reinforced shotcrete facing at the west abutment and drilled shafts at the pier #4. The soil nails at the west abutment consisted of injection drilled T40N threaded hollow bar steel declined at 40 degrees off the horizontal and installed with a 100 mm diameter drill bit. Embedment length of soil nails was 5.5 m in the upper three rows and 11.6 m in the bottom two rows. The spacing for soil nails is 1.5 m in the upper three rows and 1 m in the bottom two rows. The hollow bar reinforcement selected for the soil nails was sized according to the structural demands observed in the PLAXIS model. The drilled shafts installed at the abutment and pier #4 had a diameter of 0.3 m and were installed vertically. The shafts were reinforced with No.9 rebar and embedment lengths will be 8.5 m. Spacing of the shafts were set at 0.45 m and 1.5 m for the abutment and pier#4, respectively. The solid bar reinforcement selected for the

drilled shafts was sized according to the structural demands observed in the PLAXIS model. The soil nail and drilled shaft elements were modelled in PLAXIS as embedded beam elements, with axial and bending stiffness values based on the structural capacities. The embedded beam elements were modeled using soil nail pullout resistances based on the available subsurface data, and in similar materials. The stiffness of the embedded beams was defined based on the elastic properties of the steel bars and up to 50% contribution of the grout stiffness surrounding the bars. The reinforced shotcrete facing was modelled in PLAXIS as a 2D plate element with axial and bending stiffness values based on the dimensions of the facing. We note that the existing west abutment and approach slab piles were modelled as embedded beam elements. Based on conversations with the project team, spacing of the piles at the west abutment was selected as approximately 1.4 m and spacing of the piles at the west abutment approach slab was selected as approximately 2.6 m. The modelled embedment depths of the piles at the west abutment and approach slab were Elevation 1022.2 m. The modelled embedment depths of the piles at the trailing west abutment were up to approximately 9 m below ground surface. To model existing loading conditions at the west approach slab and abutment, surcharges of 16 and 100 kilonewtons per square meter (kN/m2) were applied at the approach slab and abutment, respectively. A surcharge of 660 kN/m2 was applied to model existing loading conditions at pier #4. Table 2 below outlines the structural properties of the structural elements used in our PLAXIS 2D model. Properties used for structural elements were based on established values from the manufacturer. Table 2. Properties for Structural Elements Structural Element

Area, A (m2)

Unit Weight (kN/m3)

Moment Elastic of Inertia, Modulus, Ix (m4) E (kN/m2)

T40N Soil Nail

8.1E-3

3.0

5.2E-6

29.1E6

Drilled Shaft

7.3E-2

3.0

4.2E-4

47.3E6

Shotcrete Facing

4.6E-2

0.33

3.0E-4

25.0E6

2.4

Approach to Staged Construction

Global stability was analyzed using the finite element analysis software PLAXIS 2D 2020. In PLAXIS, a factor of safety stability analysis uses a so-called phi/c reduction method in which the strength of materials is reduced with an MSF factor until failure is reached for a stable value of MSF. The MSF factor obtained at failure represents the global minimum factor of safety for the model. GSI completed a short-term (temporary construction) stability assessment, of the temporary slope stabilization system. The PLAXIS model had several sequential construction stages to take into consideration the effects of duration, stress history, and construction sequence on the behavior of the temporary stabilization system and

existing bridge abutment and pier. The full model sequential construction stages in GSI’s PLAXIS model are outlined below. The model deformations were reset to zero following application of the abutment and pier loads. Therefore, the calculated deformation values are relative to the point in time following application of the loads at the west abutment and pier #4. Stage 1 – Generate initial at-rest (k0) stress conditions. Stage 2 – Begin building up fill and abutment/pier layers. This sequence was divided into three stages in the PLAXIS model to account for stress history in the granular fill and native sand and gravel layers. Stage 3 – Apply existing structural loads at the west abutment and pier #4. Stage 4 – The existing rip rap along the slope at the west abutment will be removed and installation of first row of soil nails will be completed. A global stability FS calculation was performed in PLAXIS following installation of the first row of nails. Stage 5 – Excavation will continue up to approximately 0.3m below the second row of soil nails and installation of second row of soil nails at the west abutment will be completed. A global stability FS calculation was performed in PLAXIS following installation of the second row of nails and first lift of shotcrete. Stage 6 – The sequence of excavation and soil nail installation will continue down to approximately 0.3 m below the third row of soil nails at the west abutment. A global stability FS calculation was performed in PLAXIS following installation of the third row of nails and second lift of shotcrete. Stage 7 – The sequence of excavation and soil nail installation will continue down to approximately 0.3 m below the fourth row of soil nails at the west abutment. The drilled shaft array proposed at pier #4 will also be installed in this sequence. A global stability FS calculation was performed in PLAXIS following installation of the fourth row of nails, third lift of shotcrete, and drilled shafts. Stage 8 – The sequence of excavation and soil nail installation will continue down to approximately 0.3 m below the fifth row of soil nails at the west abutment. The drilled shafts will also be installed at the bottom of this excavation lift. A global stability FS calculation was performed in PLAXIS following installation of the fifth row of nails, final lift of shotcrete, and array of drilled shafts. Stage 9 – The sequence of excavation will continue down to the proposed bottom of excavation. 2.5

PLAXIS 2D Finite Element Model Results

Tables 3 and 4 below provide the estimated soil, abutment, and pier displacements (represented as a vector), and factor of safety value, for each construction phase in PLAXIS for peak and residual strength conditions in the laminated zone, respectively. (The first proposed construction sequence represents Stage 4 in the PLAXIS model.) As indicated in Table 3, the minimum required FS value of 1.25 is achieved up to the bedrock elevation. When considering a residual friction angle of 12 degrees in the weak laminated bedrock formation, the FS value

calculated at the bottom of the excavation is 1.17, as indicated in Table 4 (See Stage 9). Under the peak strength condition, the maximum calculated soil displacement at the bottom of excavation is approximately 14.5 millimeters (mm) and occurs in the foreslope of the west abutment. Under the same peak condition, the maximum calculated displacement of the west abutment is approximately 7.5 mm when reaching the bottom of excavation. Further, the maximum displacement calculated at pier #4 when reaching the bottom of excavation is 0.5 mm under the peak strength condition. When considering a residual strength condition within the laminated zone, the maximum calculated soil displacement at the bottom of excavation is 18.0 mm, as shown in Table 4 (See Stage 9). Additionally, the maximum calculated abutment and pier displacement at the bottom of excavation is 11.5 and 10.0 mm, respectively, when considering the residual strength condition. Table 3. PLAXIS Results – Peak Strengths Max. Abutment Displacement (mm)

Max. Pier Displacement (mm)

Global FoS

Stage 4 1.0

1000 years

≥ 1.1

Project Design Life

Consideration was given to requirements to be able to safely pass the IDF of which approximately 400 m3/s would need to be passed by the spillway. The required upgrades to enable the spillway to pass this amount were considered unfeasible in the short term, and it was decided that mitigation of short-term risks to the spillway was a driving priority. A design life of 20 years was selected for the uplift mitigation measures, to afford WSA time to plan for and implement long-term upgrades to enable the dam to pass the IDF. 2.3

and long-term needs, with allocation of funds and implementation timelines. The tailwater level is a necessary parameter for the development of the hydraulic jump profile, and the associated level of 812.86 m was used for this assessment. Other discharge combinations were studied, as discussed in Table 2, but this was used as the reasonably extreme condition for the project objectives.

It is noted that the Canadian Dam Association (CDA) guidelines (CDA 2013) provide FoS for sliding and not specifically for uplift; however, they were considered applicable based on the return period of the event causing the uplift loading combination. 3 3.1

EXISTING STRUCTURE ASSESSMENT Existing Conditions Assessment

A site inspection was conducted on July 19, 2021, to assess conditions of the spillway, identify new damage to the spillway, assess viable repair options, review general site conditions including presence of groundwater and seepage areas, possible access issues and existing public safety measures at the dam. Of particular interest was the seepage that was observed at the chute wall weep drain outlets which indicated the presence of water on the earth side of the spillway walls. Additionally, several other issues such as spillway operation, maintenance and surveillance were documented. Based on the findings of the site inspection, several repair recommendations were provided which included superficial concrete repairs, coating repairs for the gates and hoists shafts and additional public safety measures upgrades. Available construction and post-construction records indicated that high groundwater levels existed at the site and ongoing seepage flows were being collected and discharged through the UDS. In 2009, increased drain flows, suspected to be a result of backflow through the weep drains, were observed, and in 2011, a testing program found increased drain flows during operation of the gates. “Cloudy” flows were also observed during periods of changing drain flows, and during a 2019 camera inspection, a pipe elbow between a transverse drain and a

collector drain was found to be missing (KCB 2020). In addition to these anomalies, the condition of the spillway joints was identified as an area of concern, specifically, the deteriorating condition of the metal waterstops within the longitudinal joints (KCB 2020).

leakage through the joints and weep pipes at the chute sections were estimated and compared to the capacity of the drainage system. It was determined that inflows due to leakage to under the slab cannot be handled by the drainage system.

KCB reviewed the available reports and photographs from the 1959 spillway construction to understand the challenges experienced with high groundwater levels and to identify potential seepage sources. Details of the structure cutoff walls and curtain walls were examined to determine how the original design was meant to control and manage seepage under the spillway.

3.2

Existing condition of the UDS pipes was assessed through a review of video and observations made during drain cleanings and inspections. During an inspection conducted as part of a dam safety review (DSR) completed in 2020 (Wood 2020), the ends of the weep drains were found to be damaged and possibly redirecting high energy flow behind the walls, and thereby creating opportunity to surcharge the UDS, i.e. causing uplift pressures to develop. The elbow intended to connect the left sidewall drainpipe to the second transverse UDS drain line was disconnected or missing and a void developed in the surrounding soils. Although the inspections did not identify any issues with the portion of the UDS comprised of “noco-rode” pipes, the long-term performance of these pipes has been known to be poor. Instances of seepage flows through slab weep drains, sidewall drainpipes, and joints were reviewed. Based on measurements of the depths of the weep drains, it is suspected that the second transverse UDS drain line slopes from right to left (looking downstream), which would result in more flow at the left sidewall drain than the right. Sediment-laden water from the left sidewall drain for the second transverse drain line was observed during a 2019 drain investigation program, which may be related to the disconnected/missing elbow. Seepage flows and slumping of the right slope adjacent to the chute was observed during and after construction. On the left slope adjacent to the spillway, minor seepage spots have been observed. Possible sources of infiltration from the spillway into the underlaying gravel were assessed. Reports from investigations completed in 2011 (Author Unknown) and 2019 (AAFC 2019) were reviewed to determine the effects, if any, of gate operation on water infiltration into the underslab gravel layer and its subsequent drainage. It was found that with Gates 1 or 2 (leftmost gates) operating and weep drains plugged, flows at the left sidewall drain increased. This suggests that infiltration is occurring not only through the weep drains as suspected, but also through the joints and possibly through cracks in the slab. Measurements taken at the reference markers from 1959 to 2016 were reviewed to determine the incremental opening and closing joint gap values and also to determine the vertical displacement of the reference marks over time. There is concern that the metal waterstops in all of the longitudinal joints may be damaged, which would allow the infiltration of water under the spillway slab. The amount of

Uplift Assessment

This section discussed the hydrotechnical work completed to estimate uplift FoS and the uplift forces to achieve the target FoS. In a 2020 report KCB (KCB 2020) assessed the hydraulic performance of the spillway under various flow scenarios. A stability assessment was completed to compute FoS against uplift for the spillway slab monoliths. A 2D hydraulic model was created from LiDAR data and a tailwater rating curve was generated. The water surface profile down the spillway chute and the hydraulic jump geometry was determined for each considered scenario. FoS against uplift were computed assuming varying drainage efficiencies. From this stability analysis, it was determined that the spillway would be unable to safely pass the spillway design discharge in its current state (i.e., before proposed upgrades). Load cases used for analysis are shown in Table 2, and they fall into the Unusual category with a minimum target FoS of 1.3 (LC1 and LC2,) or the Extreme category with a minimum target FoS of 1.1 (LC3). A tailwater level for the combined flows over the spillway and emergency spillway was estimated considering previous studies (KCB 2020) (Hatch 2021) (PFRA n.d.) and was used to estimate hydraulic jump profiles. With the formation of the hydraulic jump the sloping chute monoliths become subjected to hydrostatic uplift forces. Resistance to the uplift forces is afforded by the weight of the 457 mm thick concrete slab and the weight of water flowing on top of the slab. FoS against uplift under current conditions for each of the lower and upper monoliths were determined for the load cases considered and ranged from 0.86 to 1.10, necessitating uplift mitigation measures to safely pass the design discharge. Two different methods were used to determine the FoS. The first method (M1) being the total resisting loads (i.e., the weight of water plus the weight of the slab) divided by the total uplift load; the second method (M2) being the weight of the concrete slab divided by the total of the weight of water on the slab subtracted from the total uplift. The first method typically yields a result closer to one with both methods being equal when the FoS equals one. It should be noted that in some cases M2 results in a significant decrease in the total additional hold-down force required; M1 was used in determining the required holddown force to achieve the required FoS. The additional hold-down force required to provide the minimum target FoS for chute monoliths 2 and 3 are shown in Table 2 considering 0% UDS efficiency (i.e., the drains as plugged.)

Table 2. Additional Resistance Force Required

5.1

Upper Monolith M1 M2 (kN) (kN)

Lower Monolith M1 M2 (kN) (kN)

1.3

459

172

1577

425

LC2

1.3

1,177

860

2,148

662

LC3

1.1

840

705

704

144

Loading Combination

Target FoS

LC1

*The governing load combination (LC2) for both monoliths is shown in boldened text.

4

EVALUATION OF OPTIONS

WSA requested KCB to provide options for ‘passive’ uplift mitigation, which means that they require as low as possible inspection, operation and maintenance. Possible measures for providing additional passive uplift mitigation explored for this site included: ▪ Option 1: Adding soil anchors, considered viable as no evidence of frost heave observed at the site. ▪ Option 2: Adding a concrete overlay on the chute slab, which may be viable but was discarded as an option due to the conflicts with the existing and proposed UDS. Costs for this option would be higher than for Option 1. For the overlay option, it was determined that the addition of approximately a 750 mm thick concrete overlay would be required (governed by LC2, monolith 3). This option was considered impractical and was discarded.

Skin Friction and Bonding Length

A review of the available borehole information was carried out to determine the approximate stratigraphy of the soils underlying the chute monoliths and thereby estimate the grout/soil bond strengths that could be used for the design of the anchors. The grout/soil bond strengths were estimated using the recommendations presented in Recommendations for Prestressed Rock and Soil Anchors by the Post Tensioning Institute (PTI 2014). The review indicated the presence of sand with clay lenses under the slab. An ultimate bond strength of 30 kPa for clay soils and 80 kPa for sandy soil was assumed. Further analysis was completed to define an average blended value based on the expected depths and thicknesses of clay and sand layers in which an anchor would be installed. An average blended grout/soil bond strength of 50 kPa was used for design. An anchor diameter of 150 mm was also used for design, considering constructability and potential number of anchors required. 5.2

Group Failure of Anchors

Group failure mechanism was also considered. Figure 1 describes the general method used for checking group failure of the soil anchors.

▪ Option 3: Structural upgrades to achieve a monolithic U-shaped section. This option would require significantly more work, cost and time to achieve and was discarded. ▪ Option 4: A combination of soil anchors and a concrete slab overlay was considered and deemed impractical for the following reasons: 1) required changes to baffle blocks; 2) higher costs and time for construction; 3) conflicts with other recent upgrades. The most cost-effective solution considered was adding soil anchors. Soil anchors were considered rather than larger concrete piles because soil anchors would be small in size, require less reinforcement, and could be installed with a simpler connection to the existing slab. 5

DESIGN OF UPLIFT MITIGATION MEASURES

Soil anchors were chosen as the preferred uplift mitigation measure. This section describes the geotechnical and structural analyses performed to design the hold-down anchors.

Figure 1. Group Failure of Anchors from (FHA 1999) The key for adequate resistance to group failure is the availability of enough ‘enclosed’ soil mass to resist uplift forces between anchors. The 1.6m free-stress and 7m bond lengths were thus chosen to provide a large enough enclosed soil volume. 5.3

Anchor Head Assembly Design

A design for a recessed anchor head was completed and is shown in Figure 2. A free stressing length of approximately the depth of the bedding gravel was used. It was specified that the anchors’ primary grout be pressure grouted up to the bottom of bedding gravel elevation to provide improved soil/grout bond strength. A load testing

program was developed to determine a final bond length during construction, because the bond length was highly dependent on the assumed skin friction and the load testing program was also used to confirm that anchors were capable of resisting the design loads. The final bond length used was 7000 mm, which was the original estimated required bond length. When determining a bond length a FoS of 2 was used as recommended by Post Tensioning Institute (PTI 2014). For determining the spacing of the anchors, the structural capacity of the existing slab was considered. The construction report (PFRA 1960) indicated that the average concrete compressive strength was 26.49 MPa (3842 psi) with the standard deviation being 5.14 MPa (745 psi). The specified yield strength for the reinforcing steel was 276 MPa (40,000 psi). Reinforcing steel drawings were not available at the time for design purposes; however, hand notes in the Design Data Book (PFRA n.d.) suggested that the chute slab reinforcing included top and bottom mats consisting of #6 bars at 12-inch spacing and #4 bars at 12-inch spacing, respectively, in both directions. Existing rebar spacing was confirmed by rebar scan prior to coring holes in the existing slab. Approximately 19 soil anchors with a design capacity of 80 kN per anchor were determined to be required at each upper monolith and 27 anchors for the lower monolith. The layout of the anchors is shown in Figure 2 and Figure 3. Given that there were 3 rows of monoliths, a total of 138 soil anchors were required. The number and distribution of anchors was determined by dividing each monolith into 6 sections and analyzing the forces acting at each section. Sections that only required a single anchor to achieve the target FoS were provided with 2 anchors to avoid the potential for unbalanced moments in the slab. If a section required a fraction of an 80 kN hold down anchor, the number of anchors for that section was rounded up to the nearest whole number. The final design of anchor locations were also chosen to avoid existing drains, baffle blocks, and other existing features; however, anchor locations were also adjusted slightly in the field to avoid steel reinforcement. Anchors were installed perpendicular to the sloped slab to help avoid slab reinforcing when drilling and to simplify the fabrication of mounting hardware for recessing the anchor heads in the slab. The selected soil anchors consisted of 25 mm diameter steel threadbars with a yield strength of 517 MPa. This bar size was larger than required from a strength perspective; this was decided to provide extra cross section that may corrode over the 20-year design life. Despite the additional cross-section, corrosion protection for the bond length was provided by grout cover, the free-stress length was protected with anti-corrosion compound contained within a PVC sheet shrink wrapped at both ends, and the anchor head assembly was filled with anti-corrosion compound and sealed.

Anchor failure mechanisms were checked including soil/anchor skin friction failure, anchor bar steel yielding, anchor head assembly failure (see paragraph below), local concrete crushing, and concrete punching shear. The anchor head assembly was designed to be recessed within the existing concrete to reduce adverse effects to the spillway hydraulics (Figure 4) and tripping hazards to WSA staff. The steel design incorporated complete joint penetration welds and considered tension, bending, and shear failure modes. 6

RISK ASSESSMENT SUMMARY

The project construction window was selected as September 15 to December 15, 2022, with a buffer to February 15, 2023 by meeting the operational and regulatory requirements. WSA temporarily lowered the reservoir by 2.44m to 813.28m to mitigate the following concerns during construction:

7

▪

High water table under the spillway, which increases difficulties during the installation the soil anchors.

▪

Leakage of water through the gates resulting in wet and icy/slippery working surfaces during winter.

▪

Decreased work efficiency due to wet conditions that could extend the work over two seasons and significantly increase mobilization costs.

CONCLUSION

The design team faced significant technical challenges in key engineering disciplines namely geotechnical, structural, hydrotechnical, risk management and construction planning. This included building an understanding of the site's hydrotechnical and geotechnical context based on original records; reconciling a lower original design capacity against the recently established much larger IDF needs. Before selecting the final design, the project team explored various configurations including structure replacement, a comprehensive rehabilitation, installation of soil anchors, and upgrades to develop a monolithic U-shape section with a concrete overlay. Tailwater reviews and hydraulic jump calculations were completed. Determination of uplift forces, grout-soil bond strength was also completed, and the associated FoS estimated. Soil anchor heads were designed to minimize effects to the chute hydraulics. Distribution of soil anchor locations were chosen based on several considered hydraulic jump scenarios and to avoid existing spillway features. The construction timeline included a mobilization in September 2022 and substantial completion in February 2023.

Figure 2. Plan View of Spillway Showing Anchor Layout (not to scale)

Figure 3. Section View of Spillway Showing Soil Anchor (not to scale)

Figure 4. Anchor Head Assembly Design Detail (not to scale)

8

ACKNOWLEDGEMENTS

We thank the following: WSA and KCB for allowing publication of this information. Peter Roy, P.Eng., for performing the soil/grout bond strength assessment and reviewing this document. Nayeem Uddin, M.Sc., P.Eng., and Kimberly Kusch, P.Eng., for reviewing this document. Joel Hilderman, M.Sc., P.Eng., David Mack, P.Eng., Terry Barkway, P.Eng., Glenn McLaughlin, P.Eng. for providing technical advice during the performance of the work. Daniel Bertrand, P.Eng., for the translating to French. Gabrielle Bristo, M.Eng., Engineer-In-Training, for her support coordinating the preparation of this document. The extended team of engineering, technical and administrative staff at KCB and WSA who helped with the delivery of this project. The team of engineers at KGS Group for providing technical review as WSA’s Owner’s Engineer during the performance of the work.

9

REFERENCES

AAFC. 2019. "West Val Marie Drain Investigation." Author Unknown. 2011. "West Val Marie Spillway Drain Test." CDA. 2013. 2007 Dam Safety Guidelines - Revised 2013. Canadian Dam Association. FHA. 1999. Geotechnical Engineering Circular No.4 Ground Anchors and Anchored Systems. FWHA-IF-99-015, Washington: Office of Bridge Technology Federal Highway Administration Sabatini, P.J.; Pass, D.G.; Bachus, R.C. Hatch. 2021. "Frenchman River System - Dam Breach Inundation Study - H/361920-000-228-2260002." Draft. KCB. 2020. West Val Marie Spillway Drainage Assessment. Report with dated March 31, 2020, Klohn Crippen Berger Ltd. PFRA. 1960. "Construction Report, West Val Marie Spillway 1959." PFRA. n.d. Design Data Book. Compilation of design data. Prairie Farm Rehabilitation Administration. PTI. 2014. Recommendations for Prestressed Rock and Soil Anchors. PTI DC35.1-14, Post-Tensioning Institute. Wood. 2020. "West Val Marie 2019 Dam Safety Review."

Delayed Slope Instabilities in Earth Fill Dams Due to Creep Marvin Renzo B. Malonzo & Marolo Alfaro Department of Civil Engineering – University of Manitoba, Winnipeg, Manitoba, Canada ABSTRACT An earth fill dam exhibited significant deformation in the upstream side after 50 years of satisfactory operation. Studies in this dam revealed that this was brought by creep movement. However, the studies were unable to simulate the strain softening of the soil coupled with time-dependent creep deformation. To address this, the use of Time-dependent Model for Structured soils (TMS) was proposed. It is an incremental plasticity time-dependent constitutive model based on the Modified Cam Clay. It incorporates the principles of critical state soil plasticity which can systematically emulate the primary, secondary and tertiary creep phases. The research would allow the assessment of the long-term slope stability of waterretaining structures and the development of remedial measures for existing earth fill dams that do not satisfy modern longterm slope stability dam safety requirements. RÉSUMÉ Un barrage en terre a montré une déformation importante du côté amont après 50 ans de fonctionnement satisfaisant. Des études dans ce barrage ont révélé que cela était dû au mouvement de fluage. Cependant, les études n'ont pas été en mesure de simuler l'adoucissement du sol couplé à la déformation par fluage en fonction du temps. Pour résoudre ce problème, l'utilisation du modèle dépendant du temps pour les sols structurés (TMS) a été proposée. Il s'agit d'un modèle constitutif de plasticité incrémentale dépendant du temps basé sur la Modified Cam Clay. Il intègre les principes de la plasticité du sol à l'état critique qui permet d'émuler systématiquement les phases de fluage primaire, secondaire et tertiaire. La recherche permettrait l'évaluation de la stabilité des pentes à long terme des structures de retenue d'eau et le développement de mesures correctives pour les barrages en terre existants qui ne satisfont pas aux exigences modernes de sécurité des barrages de stabilité des pentes à long terme. 1

INTRODUCTION

This research work is a continuation of the previous study conducted by Ubay (2020) on seven earth fill waterretaining dams in Canada as part of her dissertation. The 7-earth fill water-retaining dams will be referred to as WD, EF, MFLED, MFRED, CBMD, CBBD2, and CBBD4 as per request by the dam operators and owners to maintain the confidentiality of these facilities. CBBD2 exhibited significant deformations in the upstream side, despite operating satisfactorily for more than 50 years, which prompted immediate repairs, as well as investigation to determine the potential cause of the slope movement. Among the different potential causes of the delayed slope movement discussed in the work of Ubay, creeping of the clay material seemed to be the likely reason. Ubay performed a time-dependent creep deformation analysis using Soft Soil Creep (SSC) model with clay strength parameters between the post-peak and residual shear strengths. The results were able to reproduce the observed deformations, proving the validity of the hypothesis. However, it was noted that SSC lacks the capability of a fully coupled time-rate-dependent analysis. The Time-dependent Model for Structured soils (TMS) was developed by Kalos (2014) as part of his dissertation. The constitutive model incorporates the principles of critical state soil plasticity to simulate the incremental timedependent plasticity behavior of the soil. With this, the TMS is proposed to be used in the analysis to model the soil behavior leading to the delayed slope movement of CBBD2.

2

CONSTITUTIVE MODEL

The shortcoming of the SSC constitutive model in analyzing creep effects over the service life of the structure, as mentioned by Ubay (2020), can be potentially addressed by TMS since it can perform a coupled analysis by systematically emulating the different creep phases under increasing stress levels both in drained and undrained conditions. TMS is based on the principles of Modified Cam Clay (MCC) and the overstress postulate proposed by Perzyna in his work in 1963 and 1966 (Kalos, 2014). Originally, Perzyna’s theory was intended to describe the time-dependent behavior of metals. The mathematical formulation of the theory, as well as the resemblance of metal behavior to soil, allowed its application to be extended to geomaterials. 2.1

Mechanical Behavior of Geomaterials

Geomaterials can be classified as either structured or structureless based on the mechanical behavior (Kalos, 2014). Structured soils are natural soils which has undergone cementation, aging, or preconsolidation (Belokas & Kavvadas, 2011). The structure existing within the soil matrix increases the strength and size of the stress domain in which the soil exhibits stiff behavior (Leroueil & Vaughan, 1990). Stress history and bonding induces the creation of structure within the soil. The stress history gives the “memory” of the past loads that the soil experienced, which in turn governs its mechanical response. Bonding

induces the growth of interparticle resistance which makes the soil stronger (Kalos, 2014). When a structured soil is subjected to sufficient strains, it is reduced to a structureless state (Leroueil & Vaughan, 1990). Therefore, constitutive modelling of structured soils requires the knowledge of the structureless state which is the reference state of the material (Belokas & Kavvadas, 2011). Soils in a structureless state can be described by only using the current specific volume and current effective stress (Belokas & Kavvadas, 2011). The strength of the soil in this state is solely dependent on the interlocking of the soil particles (Kalos, 2014). Burland (1990) described the structureless state of the soil as intrinsic which indicates that the material has no bonding, and all of the stress history memory is removed. The locus of the structureless states represents a limiting state of the structured soils (i.e., minimum point to which the soil strength can decrease) which provides significant insight in the constitutive modelling of structured soils (Belokas & Kavvadas, 2011). 2.2

1.

Intrinsic Strength Envelope (ISE) – contains all the structureless states of the soil after substantial strains have accumulated. It is used in the constitutive model as a reference for the destructuring mechanism of the soil. Within this space, the anisotropy is considered to be nonexistent due to the chaotic distribution and orientation of the soil particles at the start of failure (Kalos, 2014).

2.

Structure Strength Envelope (SSE) – contains the available strength of the soil due to bonding formation. Only the secondary anisotropy will be accounted for in this space which is defined by the position of the plastic yield envelope within SSE (bond anisotropy is not considered).

3.

Plastic Yield Envelope (PYE) - encloses all purely elastic stress states, including viscoelastic stress field, within the SSE. Its size is a fraction of SSE, usually 0.01 to 0.05 of SSE, and its center is controlled by a kinematic hardening law which portrays the recent stress history through the secondary anisotropy tensor (Kalos, 2014).

Perzyna’s Theory

This framework is a combination of the elastoplasticity theory and time-dependent behavior of metals. In this theory (Figure 1), a static yield surface is assumed in the effective stress space, and the stress state is allowed to cross this boundary during a loading increment which results to the accumulation of deformations (Perzyna, 1963 & 1966). The space within the yield surface is the elastic region which is coincidental to the elastic field in elastoplasticity; wherein the deformations are purely elastic and can be defined by the generalized Hooke’s Law. Beyond this boundary is the elastoviscoplastic region wherein the deformation is characterized by the elastic and viscoplastic components. It should be noted that the static yield surface can change its shape and position due to the accumulation of viscoplastic strains (hardening). 

These surfaces are defined in the mean effective stress (m) and deviatoric stress tensor (s) hyperplane to give a generalized framework which eliminates the back-andforth rotation of the stress and strain tensors to the principal stress space (Kalos, 2014). s

PYE ISE

Elastoviscoplastic Region

Equipotential Surface

m

Static Yield Surface

SSE

Overstress

2.4

Elastic Region   Figure 1. Graphical Representation Perzyna’s Theory (from Kalos, 2014) 2.3

Figure 2. Graphical Representation of the Characteristic Surfaces (from Kalos, 2014)

Characteristic Surfaces

Three characteristic surfaces, presented in Figure 2, are incorporated in the formulation of TMS to describe the strength degradation of the material. These are:

Soil Material Behavior

The material behavior during loading is bounded within the characteristic surfaces. The space within the PYE is occupied by the viscoelastic field where the strain increment is described by the elastic and viscous components. The viscoelastoplastic field, wherein plastic deformation is present in addition to the elastic and viscous strains, exists within the SSE that is outside the ISE and PYE and on the boundary of the characteristic surfaces. 2.4.1

Elastic Component

The elastic component is defined by associating the incremental stress tensor (𝑑𝝈) with the incremental elastic

strain tensor (𝑑𝜺𝑒 ) as expressed in Eq. 1. Ce is the elastic stiffness tensor defined by poroelasticity which is based on the stress-strain paths of soil subjected to isotropic and uniaxial loading, unloading, and reloading. The governing equation of poroelasticity is related by the elastic bulk modulus (Ke) to the recompression slope (κ) and specific volume (v) as presented by Eq. 2. The formula tends to predict non-zero values along a full loading cycle which makes it a conservative approach. The elastic shear modulus (Ge) can be related to the elastic bulk modulus by Poisson’s ratio (Eq. 3). 𝑑𝝈 = 𝑪𝑒 ∶ 𝑑𝜺𝑒

[2]

2 ∙ 𝐺𝑒 9∙𝑣 = 3− 𝐾𝑒 1+𝑣

[3]

2.4.2

𝜀̇𝑞𝑣 =

[1]

v ∙𝝈 𝜅

𝐾𝑒 =

stress intensity effect on creep rate, and m defines the speed of strain rate increase with time. Parameter D is the stress ratio which measures the overstress distance as defined in Eq. 8 and Figure 3. The parameter r is a material constant employed to achieve faster creep shear strain rates at high shear stress levels (Kavvadas & Kalos, 2019).

𝜀̇𝑞𝑣 =

𝐷=

𝑚

𝑑𝜺𝑝 = 𝑑𝛬 ∙ 𝑷

𝑞 𝑞𝑓𝑎𝑖𝑙

=

√3 (𝑠: 𝑠) 2 3 3 𝑚𝑎𝑥 {√ (𝑠𝐹 : 𝑠𝐹 ), √ (𝑠𝐶 : 𝑠𝐶 )} 2 2

[7]

[8]

≤1

Initial-CSC cin SSE

clim

Limit-CSC

E

[4]

PYE

sc

sF m

The viscous strain is composed of volumetric and deviatoric deformations which can be observed in standard oedometer and triaxial tests, respectively. Viscous volumetric strains (𝜀̇ 𝑣 ) tend to increase the strength of soil by developing the pre-consolidation condition (Kavvadas & Kalos, 2019). However, this causes structure degradation in highly expansive soils and some weak rocks. The evolution of 𝜀̇ 𝑣 is described by the semi-logarithmic creep formula using the specific volume (v) and secondary compression coefficient (ψ=Cae/ln10). The formula (Eq. 5) is valid for t > t0, where t0 is the reference time for the start of creep. TMS also assumes that 𝜀̇ 𝑣 accumulates at all stress fields by simultaneously laying on the SSE and PYE (Kalos, 2014). 𝜓 v exp (− 𝜀 𝑣 ) v𝑡0 𝜓

[6]

2𝐴 sinh(𝑎̅𝐷𝑟 ) ;𝑚 ≠ 1 𝜀𝑞𝑣 exp [ ] 2𝐴 sinh(𝑎̅𝐷𝑟 ) ∙ 𝑡0

s

Viscous Component

𝜀̇𝑣𝑣 =

;𝑚 = 1

1−𝑚 (1 − 𝑚)𝜀𝑞𝑣 [1 + ] 𝑟 ) 2𝐴 sinh(𝑎̅𝐷 ∙ 𝑡0

Plastic Component

The plastic component of the strain is defined by an associated plastic flow rule and is described through Eq. 4, where P is the plastic potential tensor which controls the size of the plastic strain tensor and dΛ is the scalar quantity expressing the magnitude of the plastic strain tensor.

2.4.3

2𝐴 sinh(𝑎̅𝐷𝑟 )

[5]

The viscous deviatoric component (𝜀̇𝑞𝑣 ) on the other hand, tend to degrade the material structure similar to the deformations caused by plastic strains in inviscid structured soils (Kavvadas & Kalos, 2019). Once high shear stress levels are applied to the soil specimen, deviatoric strains accumulate until creep failure is achieved (Kalos, 2014). The primary and secondary deviatoric creep strains are described by the Singh-Mitchell formula in Eq. 6 and Eq. 7. Singh-Mitchell parameter A controls the measure of the viscous deviatoric strains, 𝑎̅ portrays the

F d Figure 3. Graphical Representation of the Stress States (from Kalos, 2014) 2.5

Hardening Rules

The position and shape of SSE and PYE is controlled by hardening rules (Kalos, 2014). Isotropic hardening law is applied to define the size of SSE, and subsequently, the PYE size through the proportionality ratio ξ. Kinematic hardening rules define the position of these characteristic surfaces, as well as the slope (c) of the Critical State Cone (CSC) in the stress hyperplane (Kalos, 2014). 2.5.1

Isotropic Hardening

The half-size a of the SSE is treated as a hardening parameter which evolves with the accumulation of plastic and viscous strains (Kalos, 2014). The SSE is only allowed to degrade until the ISE. As such, the half-size of SSE can be related to the half-size of the ISE (a*) through the structure ratio (B). In this sense, the parameter B is similar to the over-consolidation ratio (Kavvadas & Kalos, 2019). 𝑎 = 𝐵 ∙ 𝑎∗

[9]

The structure ratio undergoes gradual degradation from an initial value (B0) to a residual value (Bres) when the material is subjected to large strains, resulting to the complete destructuring of the soil (Kalos, 2014). Bres can be set to a value of 1 which indicates that SSE has degraded to the ISE, or it can have a value of slightly above 1 to account for any chemical, biological, or thixotropic bonding. In TMS, parameter B moves between B0 and Bres following a CamClay evolution pattern at large irreversible strains (Kavvadas & Kalos, 2019). This is achieved by using the plastic and viscous strain increments to define the size of SSE as shown in the following equation after Kavvadas & Kalos (2019). 𝑑𝑎 =

2.5.2

𝜕𝑎 𝜕𝑎 : 𝑑𝜀 𝑝 + 𝑣 : 𝑑𝜀 𝑣 𝜕𝜀 𝑝 𝜕𝜀

tensile strength, is represented by parameter d as illustrated in Figure 3 (Kalos, 2014). This parameter degrades exponentially from an initial state (din) to a value of zero at critical state, reducing the SSE to the MCC bounding surface as presented in Eq. 12 (Kavvadas & Kalos, 2019). 𝑑(𝑑) = 3.

[10]

Kinematic Hardening

𝑑𝑐 = 2.

∂c ∂c : 𝑑𝜀 𝑝 + 𝑣 : 𝑑𝜀 𝑣 𝑝 ∂𝜀 ∂𝜀

PYE Location

𝑳𝑴 = 𝜉𝑲𝑴′ → 𝜎𝑀′ = (𝑎 − 𝑑)𝑰 + 𝑑𝝈𝑳 =

[11]

SSE Translation The effect of bonding of soil particles due to isotropic compression, which is the buildup of

𝝈 − 𝝈𝑳 𝜉

[12]

𝑑𝑎 𝝈 + 𝑑𝜇 ∙ 𝜷 𝑎 𝑳

[13]

s

Critical State Cone (CSC) Evolution The slope of the CSC (c), which is related to the MCC parameter M (𝑐 = 𝑀√2/3), is allowed to transition between an initial and final value. The Initial-CSC represents high-speed strain rates, while the Limit-CSC correspond to quasi-static strain rates with an order of 10-7 (Kalos, 2014). Failure of the specimen is achieved when the CSC passes through point E on the PYE wherein the stress is constant (Figure 3). The slope of the CSC at failure does not necessarily coincide with Limit-CSC which allows the material to fail even before reaching its weakest state. The degradation of the CSC inclination simulates the tertiary creep failure under drained conditions (Kalos & Kavvadas, 2018). The evolution of the slope of CSC from an initial value (c in) to a lower value (clim) is described by an exponential decay function (Eq. 11) making use of the deviatoric plastic and viscous strain increments.

[11]

Further loading on a stress state that has reached the surface of the PYE would cause PYE to move along the path of evolution of the stress state (Kavvadas & Kalos, 2019). In TMS, when stress state M lying on PYE is subjected to additional stress, PYE moves towards the conjugate point M’ on the SSE as illustrated in Figure 4. As such, the center point (L) of PYE moves according to the kinematic hardening rule (Eq. 12) proposed by Kavvadas & Amorosi (2015). The hardening rule defines the evolution and movement of PYE by Eq. 13.

The Critical State of the soil is reached when the specimen undergoes large strains leading to the structureless state of the material. Due to its uniqueness, it is an important parameter in the constitutive model. TMS predicts unique critical states by ensuring that the soil state falls onto a unique CSL in the v – ln(m) plot which corresponds to a Critical State Cone (CSC) in the s – m plane (Kavvadas & Kalos, 2019). Kinematic hardening rules are implemented to achieve this which define the translation of SSE along the hydrostatic axis, evolution of the location of the PYE, and the slope of the CSC in the stress hyperplane which subsequently affects the eccentricity of SSE. 1.

𝜕𝑑 𝜕𝑑 : 𝑑𝜀 𝑝 + 𝑣 : 𝑑𝜀 𝑣 𝜕𝜀 𝑝 𝜕𝜀

M

PYE



M’

m

L O

K ISE SSE

Figure 4. Graphical Representation of PYE Movement (from Kalos, 2014) 3

METHODOLOGY

Pertinent laboratory tests shall be conducted to determine the necessary parameters for the analysis. In total, TMS uses 21 parameters in its formulation wherein 9 can be set empirically and the remaining factors can be determined through testing as presented in Table 1 and Table 2, respectively. The Commercial Finite Element Code SIMULIA ABAQUS shall then be used to assess the delayed deformation observed in CBBD2 using the UMAT Subroutine function (TMS was constructed using Fortran).

Table 1. Empirical TMS Parameters (from Kavvadas & Kalos, 2019) Symbol

Parameter

Typical Value

ν

Poisson’s ratio

1/3

t0

Reference time at start of creep

Arbitrary

ξ

Ratio of SSE and PYE ellipsoidal 0.01 – 0.05 shapes

Bres

Ratio of residual structure

ϑqp

Deviatoric destructuring variable for d 1.5 – 2 times due to plastic strain accumulation of θqp

a2v

Deviatoric destructuring variable for d 1.5 – 2 times due to creep of a1v

δ

Constant employed in the interpolation 3 – 7 for the PYE plastic modulus

y

Exponent employed in the interpolation 1 for the PYE plastic modulus

1

Singh-Mitchell parameter; modification 1 exponent

r

Table 2. TMS Parameters to be Determined in the Laboratory (from Kavvadas & Kalos, 2019)

5

ACKNOWLEDGEMENT

We would like to thank Dr. Alexandros Kalos and Dr. Michael J. Kavvadas of the National Technical University of Athens for their support and guidance in using TMS for this research.

Symbol

Parameter

Laboratory Test

c

Slope of CSC in the stress hyperplane

Triaxial Test

ηqp

Deviatoric destructuring variable

Drained Triaxial Test

η vp

Volumetric destructuring variable

Oedometer Test

Niso*

Intrinsic specific volume at p=1kPa

Belokas, G., & Kavvadas, M. (2011). An Intrinsic Compressibility Framework for Clayey Soils. Geotechnical and Geological Engineering, 855 - 871.

κ

Cam clay parameter; recompression line

slope

of

λ

Cam clay parameter; compression line

slope

of

Oedometer Test

Burland, J. (1990). On the Compressibility and Shear Strength of Natural Clays. Geotechnique, 40(3), 329 378.

ψ

Standard logarithm of creep rate

θqp a1v

4

was only induced prior to the calculation of the factor of safety of the state of the structure at the time. As such, a more rigorous solution is needed to address the shortcoming of the SSC model. TMS (Kalos, 2014) is a constitutive model based on the theories of Modified Cam Clay and overstress by Perzyna (1963 & 1966). It uses characteristic surfaces (SSE, PYE, and ISE) defined in the stress hyperplane to simulate the material degradation under loading. Kalos (2014) did note in his work that further study is needed regarding the realworld performance of the model. Therefore, a unique opportunity is available to implement TMS in analyzing the delayed instability observed in CBBD2. The research will assess the longterm slope stability of water-retaining structures and help in developing remedial measures for existing earth fill dams that do not satisfy modern long-term slope stability dam safety requirements.

6

REFERENCES

Creep Oedometer Test

Kalos, A. (2014). Investigation of the Nonlinear Behavior of Soil Materials by Creep Simulation. [Doctoral dissertation, National Technical University of Athens]. Deviatoric destructuring variable for c National Technical University of Athens Institutional due to plastic strain accumulation Drained Creep Repository. Retrieved from Deviatoric destructuring variable for c Triaxial Test http://dx.doi.org/10.26240/heal.ntua.1597 due to creep

A

Singh-Mitchell parameter; strain rate at reference time (t) when deviator stress is zero

𝑎̅

Singh-Mitchell parameter; slope of Undrained linear segment in the logarithmic strain Creep Triaxial rate – shear stress plot Test

m

Singh-Mitchell parameter; slope of linear segment in the logarithmic of strain rate and time plot

CONCLUSION

The delayed instability in CBBD2 was attributed to the creeping of the clay core and blanket as indicated by the analyses of Ubay (2020). The Soft Soil Creep (SSC) constitutive model was used to simulate creep behavior which yielded promising results. However, the analysis was limited due to the sequential assessment wherein the degradation of the shear strength of the soil due to creep

Kalos, A., & Kavvadas, M. (2018). Slope Instabilities Triggered by Creep Induced Strength Degradation. Numerical Methods in Geotechnical Engineering IX, 1097 - 1104. Kavvadas, M., & Amorosi, A. (2015). A Constitutive Model for Structured Soils. Geotechnique, 50(3), 263 - 273. Kavvadas, M., & Kalos, A. (2019). A Time-dependent Plasticity Model for Structured Soils (TMS) Simulating Drained Tertiary Creep. Computers and Geotechnics, 130 - 143. Leroueil, S., & Vaughan, P. (1990). The General and Congruent Effects of Structure in Natural Soils and Weak Rocks. Geotechnique, 40(3), 467 - 488.

Perzyna, P. (1963). The Constitutive Equations for Rate Sensitive Plastic Materials. Quarterly of Applied Mathematics, 20(4), 321 - 332. Perzyna, P. (1966). Fundamental Problems in Viscoplasticity. Advances in Applied Mechanics, 9, 243 - 377. Ubay, I. (2020). Stability Assessment of Aging WaterRetaining Earth Fill Dams. [Doctoral dissertation, University of Manitoba]. University of Manitoba Institutional Repository. Retrieved from http://hdl.handle.net/1993/35140

An experimental study on the influence of soil structure on erosion behavior of sensitive clays Amir-Hossein Daneshi-Sadr, François Duhaime & Yannic Ethier Construction Engineering Department – École de technologie supérieure, Montréal, Québec, Canada

ABSTRACT The present research attempts to evaluate the influence of the aggregated structure of sensitive marine clays on their erosion characteristics. Hole erosion tests were carried on five pairs of remolded and undisturbed specimens, each of which derived from a distinctive parent soil. Hole erosion test results corroborated the high erosion resistance of the intact structured clays. The erodibility of remolded samples, which were subjected to unidimensional compression, was significantly greater due to the destructuration of interparticle bonding between soil fabrics. In addition, a decrease in soil erosion resistance was observed concurrently with an increase in sensitivity. A perceptible interrelation was recognized between the critical shear stress and the plasticity index, wherein soil erodibility decreases with the increase of the plasticity index. RÉSUMÉ Cette recherche a tenté d'évaluer l'influence de la structure agrégée des argiles marines sensibles sur leurs caractéristiques d'érosion. Des essais d'érosion de trou ont été réalisés sur cinq paires d'échantillons remaniés et intactes, chacun provenant d'un sol parent distinctif. Les résultats d’essai d'érosion de trou ont corroboré la résistance élevée à l'érosion des argiles structurées intacts. En raison de la déstructuration de la liaison entre les particules du sol, les échantillons remaniés qui ont été soumis à une compression unidimensionnelle ont montré une érodabilité comparativement plus élevée. De plus, une diminution de la résistance à l’érosion du sol a été observée lorsque la sensibilité est plus élevée. Une interrelation perceptible a été constatée entre la contrainte de cisaillement critique et l'indice de plasticité, dans lequel la résistance à l’érosion du sol s'améliore avec l'augmentation de l'indice de plasticité.

1

INTRODUCTION

Internal erosion due to concentrated leaks is deemed a significant threat to the stability and efficient operation of hydro-technical structures, e.g., levees, dikes, and embankments. Seepage through fissures and defects results in the detachment of soil particles and the transfer of eroded particles downstream, thereby causing the defect to enlarge and the structure to become unstable. The literature has presented a large number of experimental and numerical methods aimed at enhancing comprehension of soil erosion mechanisms and establishing the relationship between soil properties and erosion characteristics, namely critical shear stress, erosion coefficient, and erosion rate index. The hole erosion test (HET), developed by Wan and Fell (2002, 2004a, b), has been extensively employed in studying the erosion behavior of soils owing to its straight-forward methodology and reliable results. Several scholars have made modifications to the testing and interpretation methodology in order to enhance the accuracy and efficacy of soil erosion determination. After placing the sample inside the test cell, a preformed conduit in the center of the sample is subjected to an eroding water flow in order to simulate concentrated leak erosion in earth structures. The exerted tangential seepage force stimulates detachment of soil particles from the conduit walls, which are then carried downstream via the flow. Through monitoring the hydraulic state of the eroding flow and assessment of the

enlargement of the conduit diameter, it is feasible to determine the erosion characteristics. The erosion behavior of cohesive soils is governed by their geotechnical properties, such as mineralogy of clay particles, saturation, and void ratio, and by hydraulic and environmental factors, such as pore fluid chemistry, temperature, and pH. In the case of natural clays, the erodibility is influenced by the distinctive soil structure and the strength of planes of weakness (Lefebvre et al. 1985). Numerous geological incidents of both regional and local significance, such as landslides and erosion, have been documented in the literature (Bjerrum et al. 1969, La Rochelle et al. 1970, Brzezinski 1971, Eden et al. 1971, Tavenas et al. 1971, Eden 1972, Mitchell and Klugman 1979, Williams et al. 1979, Lévy et al. 2012). These occurrences underscore the significance of the thorough comprehension of the behavior of sensitive clays Understanding the particular characteristics of the sensitive clays and the impact of mechanical and environmental parameters on the geotechnical properties is of great importance for the construction and maintenance of hydro-geotechnical structures. The mineralogy, fabric structure, stress-strain behavior, compressibility, and shear strength of the natural clays have been comprehensively examined in the literature (Crawford 1961, Kenney 1968, LaRochelle and Lefebvre 1971, Mitchell 1976, Bentley and Smalley 1978, Baudet and Stallebrass 2004). Notwithstanding, the erosion characteristics of sensitive clays and the influence of interparticle bonds and stratification on erosion parameters

have yet to be comprehensively and experimentally investigated. The existing literature on the erosion properties of sensitive clays is limited by the small number of tested samples and by testing methodologies that differ from current methods (Lefebvre et al. 1985, 1986, Chapuis 1986). The primary aim of the present study is to appraise the erosion behavior of structured clays and to gauge the impact of interparticle bonding on erosion characteristics. 2

DETERMINATION OF CHARACTERISTICS

THE

SOIL

EROSION

The principal equation which describes soil erosion in HET can be written as [1]

𝜀̇𝑡 = Ce (τt - τc)

where, 𝜀̇𝑡 = erosion rate per unit surface area at time t (kg/s/m2), Ce= coefficient of soil erosion (s/m), τt = hydraulic shear stress exerted by eroding flow at time t (N/m 2), and τc = critical shear stress for erosion initiation (N/m 2). Also, the erosion rate index (IHET) is defined as 𝐼𝐻𝐸𝑇 = −log⁡(𝐶𝑒 )

[2]

Wan and Fell (2002, 2004a, b) introduced an interpretation methodology based on Equation 2 in order to quantitatively study the soil erosion mechanisms due to concentrated leakage in embankments. This methodology is based on the principle that for erosion to start, the shear stress applied to the surface of the conduit should be greater than τc. Given that the hydraulic gradient through the conduit is measured by i=Δh/L, the exerted shear stress to the hole periphery can be defined by 𝜏𝑡 = 𝜌𝑤 𝑔𝑖

𝜙𝑡

[3]

4

where ρw is the density of the eroding fluid (kg/m3), g is the gravitational acceleration (9.81 m/s2), and ϕt is the average diameter of the erosion hole at time t (m). Hereupon, the erosion rate per unit surface area of the preformed erosion conduit is determined by 𝜀̇𝑡 =

𝜌𝑑 𝑑𝜙𝑡

[4]

2 𝑑𝑡

Where ρd= soil dry density (kg/m3), and dϕt/dt= change in hole diameter with time (m/s). The initial hole diameter, ϕ i, is 6 mm, and the final diameter of the eroded hole, ϕ f, can be calculated from the volume of the paraffin candle which is casted in the hole after test termination. The diameter ϕt of the hole at time t can be calculated as follows for laminar and turbulent flow conditions, respectively. 𝜙𝑡 = (

16𝑄𝐿𝑓𝐿

𝜋𝜌𝑤 𝑔∆ℎ

𝜙𝑡 = (

1 3

[5]

)

64𝑄2 𝐿𝑓𝑇

𝜋2 𝜌𝑤 𝑔∆ℎ

1

)5

[6]

where 𝑓𝐿 is the friction factor for laminar flow (kg/m2s), 𝑓𝑇 is the friction factor for turbulent flow (kg/m 3), and Q is the flow rate (m3/s). 3

MATERIAL TESTED

A substantial area of eastern Canada is draped by thick accumulations of post-glacial marine clays known for their high sensitivity. These sensitive clays are known as Champlain clays or Leda clays. They consist of fine sediments eroded from the igneous and metamorphic rocks of the Canadian Shield. These clays were initially deposited in saline water but later leached by freshwater during post-glacial rebound (Mitchell and Klugman 1979, Torrance 1979, 1983). Geological history, sedimentation, and post-sedimentation processes all contributed to the distinct structure of sensitive clays. Sedimentation factors such as mineralogical composition and deposition environment promoted flocculation, whilst postsedimentation factors such as slow consolidation and contemporaneous cementation of particles facilitated the formation of the aggregated soil structure. Natural clays in which the formation of cemented interparticle bonds was initiated during the incipient phase of consolidation exhibit greater porosity in comparison to clays with bonds that formed subsequent to overburdening by pursuant deposits (Quigley and Ogunbadejo 1972, Quigley 1980). Champlain clays can present fissures, zones of weakness, and silt or sand lenses (Gaskin et al. 2003). The aggregated structure of the natural clay is formed by cardhouse oriented particles which are connected to each other by rigid cementation bonds (Gillott 1979). The Champlain clay deposits exhibit a spectrum of consolidation states, ranging from normally consolidated to overconsolidated across different geographical regions, with variable mineralogy and particulate composition. Undisturbed nonremolded natural clays can have a high shear strength, nonetheless, destructuration substantially reduces their strength. Few investigations have been conducted to assess the surface or internal erosivity of undisturbed sensitive clays. Research findings indicate that the erosion mechanism of intact structured clays is more intricate compared to conventional cohesive soils. While undisturbed nonweathered structured clays are strong enough to resist shear stresses as high as 400 Pa, the resistance to water erosion is considerably reduced for remolded specimens. Furthermore, for samples presenting erosion, it mostly takes place at the level of either small lumps of clayey material or silt and sand particles along planes of weakness and inherent heterogeneities (Lefebvre et al. 1985, 1986). 4

HET APPARATUS AND TEST PROCEDURE

The HET apparatus at ÉTS is illustrated in Figure 1. The upstream/downstream flow chambers and the main cell are fabricated from transparent PVC, thus enabling qualitative assessment of emerging effluent turbidity as well as the soil specimen condition. The main cell of the apparatus, accommodating the soil during the experiment, is affixed between the upstream and downstream flow chambers.

(a)

(b) Figure 1. (a) Photograph, and (b) schematic illustration of the HET apparatus; notes: 1- inlet pipe, 2- flowmeter, 3- upstream valve for the regulation of the water flow and the initiation of test, 4- upstream port for the connection of the differential pressure sensor, 5- HET cell, 6- downstream port for the connection of the differential pressure sensor, 7- valve for the regulation of the water flow, 8- upstream flow chamber, 9- downstream flow chamber, 10- turbidimeter, 11- outlet pipe, 12- data acquisition card, and 13- computer. HETs are performed on both remolded and intact specimens of sensitive clays with the objective to evaluate their erosion behavior and the impact of destructuration on their stability. The intact specimens were collected using thin-walled samplers in order to preserve the intact clay mechanical properties and structure. After collecting the samples, they were sealed with paraffin wax, and preserved in a humid room to avoid the formation of tension cracks caused by drying. Hole erosion test specimens were introduced in the HET apparatus using the sharp edge of the HET cell. The preparation of remolded samples

entailed the unidimensional compaction of four soil layers on top of a plastic disc, while preserving the initial water content. The purpose of utilizing the disc downstream of the specimen is to prevent specimen slaking and excessive reduction of the length of the conduit (Lim 2006). A 6 mm axial hole was created by pushing a sharp metal rod at the center of the HET specimen. The aim is to induce erosion only in the preformed hole in order to simulate the surface erosion phenomenon in pre-existing defaults. Before test commencement and after affixing the cell, the upstream and downstream flow chambers were simultaneously filled

sensitivity (CFEM; Canadian Geotechnical Society 2006). Samples F1 and F6 exhibited the lowest sensitivity values, while F36 was regarded as the most sensitive sample among those tested. 800 700 600

τc (N/m2)

with water and saturated. Hole erosion tests are performed based on the constant head loss testing method. The adjustable upstream reservoir is capable of providing 3.5 m pressure head. Each test head was maintained for 12 to 15 minutes. If no sign of erosion was detected, the upstream pressure head was increased until progressive erosion and critical conditions were initiated. In order to determine a correlation between erosion characteristics and geotechnical properties of samples, Atterberg limits were measured for the five sensitive clays (BNQ 2501-092). In addition, to demonstrate the influence of the soil structure on the resistance to water erosion, sensitivity of the natural clays was determined using the fall cone penetrometer (BNQ 2501-110).

500 400 300 200 100

5

0

RESULTS AND DISCUSSIONS

Hole erosion tests were carried on five sensitive clay sample. The erodibility of each sample was evaluated in two different conditions, namely intact and remolded. Atterberg limits, sensitivity, remolded and undisturbed strengths, and moisture content of the tested specimens are provided in Table 1. Hole erosion tests specifications are provided in Table 2. On the basis of HET results, the improvement of the soil erodibility is attained by the increment of the critical shear stress and reduction of the erosion rate (Figure 2). The most frequently employed parameter for comparing shear strength of intact and remolded soil is referred to as sensitivity. According to the sensitivity measurements obtained with the fall cone test, soil specimens demonstrated generally low to medium

0

0.5

1

1.5

2

2.5

3

3.5

Ce (×10-5 s/m)

Figure 2. Correlation between the critical shear stress and the erosion coefficient Photographs of some of the paraffin wax candles are presented in Figure 3. It is important to note that due to intense floc erosion and slaking at the downstream end, preparation of the paraffin candle was unfeasible for some samples (Figure 4). The existence of weak planes and silt/fine sand lenses adjacent to the erosion hole facilitates the separation of clay lumps, which accounts for the nonuniform expansion of the conduit diameter in some specimens.

Table 1. Geotechnical parameters of natural clays used in the research program Soil

w (%)

Cu (kPa) 1

Cur (kPa) 1

St 1

LL (%) 2

PL (%)

PI (%)

F1

45.9

22

3.9

6

50

18

32

F2

28.9

120

12.1

10

45

18

27

F6

46.4

52

9.9

5

44

19

25

F6A

29.7

23

2.9

8

41

16

25

F36

41.0

137

6.8

20

44

21

23

Undisturbed strength, Remolded strength, and Sensitivity determined by the cone penetration test (BNQ 2501-110). 2 Liquid limit determined by the cone penetration test (BNQ 2501-092). 1

Table 2. Erosion characteristics of undisturbed and remolded specimens

1

Soil

τc (Pa)

IHET

Ce (× 10-5 s/m)

Qualitative description 1

F1

722.7

5.1

0.79

Very slow

F1-Remolded

227.4

4.5

2.95

Moderately slow

F2

377.8

5.1

0.8

Very slow

F2-Remolded

205.4

4.7

1.93

Moderately slow

F6

334.4

4.9

1.31

Moderately slow

F6-Remolded

225.0

4.7

1.9

Moderately slow

F6A

218.6

4.9

1.4

Moderately slow

F6A-Remolded

189.1

4.7

2.03

Moderately slow

F36

126.1

4.9

1.3

Moderately slow

F36-Remolded 91.7 4.6 2.74 Qualitative description of soil erosivity in accordance with Wan and Fell (2004b).

Moderately slow

800

τc (N/m2)

Noteworthy information pertaining to the natural sedimentary formation and stratified structure of the samples can be gleaned from a meticulous examination of the candles. At the interface between two layers with distinct properties, soil erosion intensifies which causes the eroded section to expand.

700

Undisturbed

600

Remolded

500 400 300 200

Flow Direction

100 0

F1

F2

F6

F6A

F36

Soil Sample

Figure 5. Influence of soil remolding on the critical shear stress of the used structured clays Figure 3. Photographs of the paraffin wax candles from post-test eroded conduits

(a)

Figure 6 shows that the critical shear stress tends to decrease with an increase in sensitivity. The observed tendency is more pronounced in the case of undisturbed samples in comparison to remolded samples. Inconsistencies can be ascribed to variations in the characteristics of intact samples, such as the water content and the depth from which the samples were collected. The results reveal that, despite the fact that brittle interparticle links have been altered in remolded clays, other parameters, including mineralogy and physicochemical characteristics of particles, strongly impact the erosion behavior of sensitive samples.

(b)

800 Undisturbed

700

Figure 4. Pictures of the (a) downstream and (b) upstream sides of the eroded HET specimen. τc (N/m2)

Based on HET results and as depicted in Figure 5, the influence of the structure degradation on the erosion resistance of the tested cemented clays is substantial. The shear resistance of the soil is a function of the coefficient of friction, which is in turn influenced by the structure and stratification of the soil matrix. When structured clays are subjected to one-dimensional compression, the brittle intra-aggregate bonds within the soil structure are disintegrated, resulting in particles to behave like their original pre-cementation essence in which the shear strength is controlled by the interparticle friction (Quigley and Thompson 1966). The compression induces a significant alteration to the porous structure of the clay, without complete obliteration, whereby the randomly oriented particles tend to reorient orthogonally to the applied force direction (Delage and Lefebvre 1984). The increased parallelism of the particles results in a significant decrease in the coefficient of friction (Kenney, 1968). At this stage, soil erosion behavior is significantly influenced by both consolidation state and the chemistry of pore water (Arulanandan et al. 1973, Sherard et al. 1976).

Remolded

600 500 400 300 200 100

0

0

5

10

St

15

20

25

Figure 6. Variation of the critical shear stress for different degrees of the sensitivity of the tested natural clays A correlation can be established between the critical shear stress and Atterberg limits for undisturbed and remolded specimens (Figure 7). The divergence of some samples from the general trend can be explained by the influence of several geotechnical and environmental parameters, such as particle mineralogy, sedimentation conditions, and particle size (Locat et al. 1984). The present study corroborates the findings of earlier studies (e.g., Wan and Fell 2002, Soroush et al. 2019, Shourijeh et al. 2020) indicating that soils with higher plasticity index are more resistant to water erosion.

τc (N/m2)

800

700

Undisturbed

600

Remolded

(a)

500 400 300 200 100

0

0

10

20

30

40

PI (%)

τc (N/m2)

800 700

Undisturbed

600

Remolded

(b)

7

Arulanandan, K., Sargunam, A., Loganathan, P., and Krone, R. 1973. Application of chemical and electrical parameters to prediction of erodibility. Soil Erosion: Causes and Mechanisms, Prevention and Control: 4251.

400 300 100 0

0

20

40

60

80

LL (%) 800

τc (N/m2)

REFERENCES

500

200

700

Undisturbed

600

Remolded

500

Bjerrum, L., Loken, T., Heiberg, S. and Foster, R. 1969. A field study of factors responsible for quick clay slides, Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering, Mexico City, Mexico, 2: 531-540.

400

300 100 0

Baudet, B., and Stallebrass, S. 2004. A constitutive model for structured clays, Géotechnique, 54(4): 269-278. Bentley, S. P., and Smalley, I. J. 1978. Inter‐particle cementation in Canadian post‐glacial clays and the problem of high sensitivity (St > 50), Sedimentology, 25(2): 297-302.

(c)

200

0

5

10

15 PL (%)

20

25

30

Figure 7. Correlation between the critical shear stress and (a) plasticity index, (b) liquid limit, and (c) plastic limit for tested samples 6

results of the hole erosion tests, undisturbed marine clays exhibit a capacity to withstand applied shear stresses of up to 700 Pa. On the contrary, it has been observed that the process of remolding and structural degradation leads to an increase in erodibility. Through applying a unidirectional compression, brittle links between soil fabrics break. As a result, the porous structure of the soil is altered and soil particles that were randomly oriented tend to form parallel layers. In view of the fact that destructured specimens possess lower coefficients of friction compared to undisturbed ones, remolded clays have lower shear resistance, thus are more erosive compared to intact samples. It was discussed that for the majority of the tested clays, the critical shear stress reduces as the sensitivity value increases. In addition, a correlation was observed between the critical shear stress and the plasticity index.

SUMMARY AND CONCLUSIONS

The distinct structure of sensitive eastern Canadian clays is attributed to the gradual accumulation of the sedimentary particles, which occurred concurrently with the formation of cementitious interparticle bonding across the Champlain Sea basin. Remolding and structural degradation break down the interparticle bonds, resulting in a substantial decrease in shear strength. This article aimed to demonstrate the influence of the structure of sensitive Champlain Sea clays on their erosion characteristics. The newly designed HET apparatus at ÉTS along with the interpretation methodology were presented. Both remolded and undisturbed specimens were used in the course of this experimental research. With regard to the

Brzezinski, L. S. 1971. A review of the 1924 Kenogami landslide, Canadian Geotechnical Journal, 8(1): 1-6. Bureau de normalisation du Québec (BNQ). 2014a. Soils – Determination of liquid limit by the fall cone penetrometer and determination of plastic limit, Canadian Standards Association and Bureau de normalisation du Québec, CAN/BNQ 2501-092. Bureau de normalisation du Québec (BNQ). 2014b. Soils – Determination of undrained shear strength and determination of sensitivity of cohesive soils using the fall cone penetrometer, Canadian Standards Association and Bureau de normalisation du Québec, CAN/BNQ 2501-110. Canadian Geotechnical Society. 2006. Canadian foundation engineering manual, 4th ed. BiTech Publishers Ltd. Chapuis, R. P. 1986. Quantitative measurement of the scour resistance of natural solid clays, Canadian Geotechnical Journal, 23(2): 132–141.

Crawford, C. 1961. Engineering studies of Leda clay, In Soils in Canada, University of Toronto Press, Toronto, Canada, 3: 200-217. Crawford, C. B. 1968. Quick clays of eastern Canada. Engineering Geology, 2(4): 239-265. Daneshi-Sadr, A.-H. 2018. An Experimental Parametric Study of the Effects of Physical and Chemical Soil Properties on Erosion of Fine Grained Soils. M.Sc. Thesis, Amirkabir University of Technology, Tehran, Iran. Delage, P., and Lefebvre, G. 1984. Study of the structure of a sensitive Champlain clay and of its evolution during consolidation, Canadian Geotechnical Journal, 21(1): 21-35. Eden, W.J. 1972. Some observations at Le Coteau landslide, Gatineau, Québec, Canadian Geotechnical Journal, 9: 508-514. Eden, W.J., Fletcher, E.B. and Mitchell, R.J. 1971. South Nation River landslide, 16 May 1971, Canadian Geotechnical Journal, 8(3): 446-451. Fell, R., Wan, C. F., Cyganiewicz, J., and Foster, M. 2003. Time for development of internal erosion and piping in embankment dams, Journal of Geotechnical and Geoenvironmental Engineering, 129(4): 307-314. Gaskin, S., Pieterse, J., Shafie, A. A., and Lepage, S. 2003. Erosion of undisturbed clay samples from the banks of the St. Lawrence River, Canadian Journal of Civil Engineering, 30(3): 585-595. Gillott, J. 1979. Fabric, composition and properties of sensitive soils from Canada, Alaska and Norway, Engineering Geology, 14(2-3): 149-172. Kenney, T. C. 1968. A review of recent research on strength and consolidation of soft sensitive clays, Canadian Geotechnical Journal, 5(2): 97-119. Kenney, T. C., and Ali, M. S. 1968. Discussion of “Stability of Natural Slopes in Sensitive Clay”, Journal of the Soil Mechanics and Foundations Division, 94(5): 11851190. La Rochelle, P., Chagnon, J.Y. and Lefebvre, G. 1970. Regional geology and landslides in the marine clay deposits of Eastern Canada, Canadian Geotechnical Journal, 7(2): 145-156.

Lévy, S., Jaboyedoff, M., Locat, J., and Demers, D. 2012. Erosion and channel change as factors of landslides and valley formation in Champlain Sea Clays: The Chacoura River, Quebec, Canada, Geomorphology, 145: 12-18. Lim, S. S. 2006. Experimental Investigation of Erosion in Variably Saturated Clay Soils.Ph.D. thesis, The University of New South Wales, NSW, Australia. Locat, J., Lefebvre, G., and Ballivy, G. 1984. Mineralogy, chemistry, and physical properties interrelationships of some sensitive clays from Eastern Canada, Canadian Geotechnical Journal, 21(3): 530-540. Mitchell, R. J., and Klugman, M. A. 1979. Mass instabilities in sensitive Canadian soils, Engineering Geology, 14(23): 109-134. Quigley, R. M. 1980. Geology, mineralogy, and geochemistry of Canadian soft soils: a geotechnical perspective, Canadian Geotechnical Journal, 17(2): 261-285. Quigley, R. M., and Ogunbadejo, T. A. 1972. Clay layer fabric and oedometer consolidation of a soft varved clay, Canadian Geotechnical Journal, 9(2): 165-175. Quigley, R., and Thompson, C. 1966. The fabric of anisotropically consolidated sensitive marine clay, Canadian Geotechnical Journal, 3(2): 61-73. Sherard, J. L., Dunnigan, L. P., and Decker, R. S. 1976. Identification and nature of dispersive soils. Journal of the Geotechnical Engineering Division, 102(4): 287301. Shourijeh, P. T., Soroush, A., and Daneshi-Sadr, A.-H. 2020. The effects of lime, bentonite and nano-clay on erosion characteristics of clay soils, European Journal of Environmental and Civil Engineering, 26(9): 37623787. Soroush, A., Shourijeh, P. T., and Fouladi, S. R. 2018. The effects of soil erosion characteristics on critical filter design in embankment dams, Geotechnical Testing Journal, 42(3): 789-816. Tavenas, F., Chagnon, J.Y. and La Rochelle, P. 1971. The Saint-Jean-Vianneylandslide: Observations and eyewitness accounts, Canadian Geotechnical Journal, 8(3): 463-478.

La Rochelle, P., and Lefebvre, G. 1971. Sampling disturbance in Champlain clays, ASTM International, STP 483: 143—163.

Torrance, J. K. 1979. Post-depositional changes in the pore-water chemistry of the sensitive marine clays of the Ottawa area, eastern Canada, Engineering Geology, 14(2-3): 135-147.

Lefebvre, G., Rohan, K., and Douville, S. 1985. Erosivity of natural intact structured clay: evaluation, Canadian Geotechnical Journal, 22(4): 508-517.

Torrance, J. K. 1983. Towards a general model of quick clay development, Sedimentology, 30(4): 547-555.

Lefebvre, G., Rohan, K., and Milette, J.-P. 1986. Erosivity of intact clay: Influence of the natural structure, Canadian Geotechnical Journal, 23(4): 427-434.

Wan, C.F., and Fell, R. 2002. Investigation of internal erosion and piping of soils in embankment dams by the slot erosion test and the hole erosion test, UNICIV Report No. R-412, ISBN 85841379 5, School of Civil

and Environmental Engineering, The University of New South Wales, Sydney, Australia. Wan, C. F., and Fell, R. 2004a. Investigation of rate of erosion of soils in embankment dams, Journal of Geotechnical and Geoenvironmental Engineering, 130(4): 373-380. Wan, C. F., and Fell, R. 2004b. Laboratory tests on the rate of piping erosion of soils in embankment dams, Geotechnical testing journal, 27(3): 295-303. Wan, C. F., and Fell, R. 2008. Assessing the potential of internal instability and suffusion in embankment dams and their foundations, Journal of Geotechnical and Geoenvironmental Engineering, 134(3): 401-407. Williams, D. R., Romeril, P. M., and Mitchell, R. J. 1979. Riverbank erosion and recession in the Ottawa area, Canadian Geotechnical Journal, 16(4): 641-650.

Tuesday, October 3, 2023

FOUNDATIONS II

Continuous Flight Augercast (CFA) Piles as Deep Foundation Elements for a High-Rise Building Alexandre Almeida, Naresh Gurpersaud & Hiu Lee Keller, Acton, ON, Canada ABSTRACT The construction of a 34-storey residential building required a deep foundation system competent enough to support the applied loads, and, at the same time, mitigate excessive settlements. Continuous Flight Augercast (CFA) Piles were selected as deep foundation elements for the building. A CFA pile is a cast-in-place reinforced concrete pile, constructed in a single pass process. The main difference between a CFA pile and conventional bored piles is that the concrete is pumped through the hollow core auger during auger withdrawal, maintaining the drill hole stable throughout the whole drilling process. The CFA pile simple and quick installation methodology are key benefits when applied to deep soil profiles. The CFA piles were connected to the structure through a central raft and surrounding pile caps. An overview of the project, its geotechnical conditions, foundation design considerations, results of a full-scale static compression load test, quality control program, and the construction results are highlighted in this paper. RÉSUMÉ La construction d'un immeuble résidentiel de 34 étages nécessitait d’un système de fondation suffisamment compétent pour supporter les charges appliquées et, en même temps, atténuer les tassements excessifs. Les pieux à la tarière continue ont été sélectionnés comme éléments de fondation profonde pour le bâtiment. Un pieux à la tarière continue est un pieu en béton armé coulé sur place, construit en un seul passage. La principale différence entre les pieux à la tarière continue et les pieux forés est que le béton est pompé à travers d’un noyau creux de la tarière pendant le retrait de la tarière, maintenant le trou de forage stable tout au long du processus de forage. La méthodologie d'installation simple et rapide des pieux à la tarière continue est un avantage clé pour les profils de dépôt de sol profond. Les pieux à la tarière continue ont été reliés à la structure par un radier central et les têtes des pieux. L'ensemble du projet, les conditions géotechniques, les considérations relatives à la conception des fondations, les résultats d'un essai de charge statique en grandeur réelle, le contrôle de la qualité et les résultats de la construction sont présenté dans ce document.

1

INTRODUCTION

Continuous Flight Auger (CFA) piles mainly differ from conventional bored piles due to the installation process. CFA piles are constructed by drilling and concreting in a single-pass operation (see Figures 1 and 2). The stability of the drill hole is provided during auger withdraw by maintaining a positive concrete pressure. The central reinforcement cage installation is the last stage of the pile construction. This simple installation makes CFA piles a competitive alternative to traditional large-diameter shafts or driven piles. Commonly, pile diameters of up to 1,200 mm can be achieved with depths of up to 45 m. Quality Control (QC) of CFA piles assists in minimizing any installation defects which can potentially result in pile structural issues. The main component of the QC of CFA piles is the Data Acquisition (DAQ) system. The DAQ system provides real-time information to the driller and inspector regarding the drilling rate, rotation, torque, pouring pressure, pouring flow, and withdrawn speed. The information of each pile is stored and can be easily accessible through cloud-based software. CFA pile design is especially critical for high-rise buildings. CFA piles are inherently slender than a typical large-diameter shaft. Also, the application of more aggressive failure load determination methods means a less stiff foundation element. In these circumstances, a

robust serviceability analysis of the foundation system is extremely important to avoid any excessive total and differential settlements of the structure. The concept of a piled raft approach is often used in CFA pile design for high-rise buildings. Piled rafts consider not only the contribution of the pile stiffness to the settlement mitigation of the structure but also the contribution of the raft direct contact with the bearing layer.

Figure 1. CFA Pile installation

Figure 2. CFA Pile installation process (from Almeida et al. 2021) 2

PROJECT OVERVIEW

A 34-storey building is currently under construction and will house 273 no. residential units and 273 no. vehicular parking spots. The building required a substantially stiff foundation to provide both support for the applied loads and to minimize the settlements. Understanding how the applied loads from the structure transfer to the foundation is a key element for the foundation layout determination. Typically, high-rise buildings will have a central rigid core, where lateral loads applied to the structure (i.e., earthquake or wind) will be resisted. This configuration can be compared to the human body’s spinal cord. Figure 3 illustrates this typical building arrangement. A load redistribution within the foundation system is observed during a lateral load event. The axial compression forces are increased on the side of the building across the lateral load application plane, whereas the axial compression forces will be decreased (or potentially tension will be generated) on the side closest to the lateral force application plane. The central core of the building will as a result attract the greatest portion of this load imbalance due to its stiffer nature. When CFA piles are used as the solution for the foundation, this mechanism usually requires a central piled raft with surrounding smaller pile caps. In addition, many interaction mechanisms within the soil occur due to the applied loads, such as the pile-to-pile, the pile-to-raft, raft-to-pile, and raft-to-raft interactions, as shown in Figure 4. Typically, the central elements within a raft will experience the highest magnitude of induced settlements. Thus, its stiffness is reduced. The raft

structure, on the other hand, has some rigidity which helps compensate for this stiffness difference. Combining the analysis of the 2 mechanisms above, the experienced foundation designer can determine the suitable pile layout. For the job presented in this paper, the final pile layout is shown in Figure 5. The Typical CFA pile profile is shown in Figure 6. A 50 MPa concrete was used in the piles. The CFA pile was also reinforced with a top cage made of 6-20M vertical rebars coupled with 10M ties.

Figure 3. Interaction between applied wind forces and building structure/foundation system

Figure 4. Pile and raft interactions: pile-to-raft and pile-topile (top); and raft-to-raft and raft-to-pile (bottom) (Hain and Lee 1978)

Figure 6. CFA pile structural profile and cross sections as per Keller (2022a) 3

SUBSURFACE CONDITIONS

The Geotechnical Investigation included Standard Penetration Tests (SPT), Pressuremeter Tests (PMT), water content measurements (w), Grain Size Analysis, and Organic Matter. A very competent silt and sand layer is present below 17 m. The geotechnical conditions are shown in Figure 7.

Figure 5. CFA pile layout for the building foundation (from Keller 2022a)

Figure 8. Load distribution onto the central piled raft (from Keller 2022b) Figure 7. Results from the geotechnical investigation as per McClymont & Rak (2021) 4

CENTRAL PILED RAFT

The CFA pile design was developed with special attention given to the piled raft. A 3D Finite Element Method was used to simulate its behaviour. As all forces, elements, and soil conditions are run within the same model, the software automatically considers the effects of the interactions that occur between the foundation elements, as shown in Figure 4. The mechanism of load distribution within the building under a lateral load shown in Figure 3 can also be used to understand the piled raft behaviour under the same lateral load. Therefore, it is important to identify the worst-case conditions of load distribution within the raft and the piles to design for the Ultimate Limit State condition. This case is shown in Figure 8 for the central piled raft. The Serviceability Limit State is usually the governing criterion for the piled raft design. Settlement is evaluated as per the Structural Commentaries of the National Building Code of Canada (2015) recommendations. According to Fellenius (2023), if the settlement condition is satisfactory within a pile group, the ultimate condition is also usually satisfactory. The 3D FEM provides the benefit to also generate settlement plots. The project criterion was to maintain the total expected settlements below 25 mm. The maximum estimated settlement as per the 3D FEM model is equal to 14 mm and minimum settlement of 7 mm. The estimated settlement plot is shown in Figure 9. Figure 10 shows the installed CFA piles during its detailing process for the raft installation.

Figure 9. Settlement distribution across the central piled raft for the serviceability case (from Keller 2022b)

5 5.1

FULL-SCALE STATIC LOAD TEST Load Test Results

A full-scale static compression pre-production load test was conducted on a sacrificial pile to validate the design parameters (see Figure 11). The loading was carried out with general reference to ASTM D1143 (2020) Quick Test (Procedure A) method. The test pile was a 500-mm diameter, 22.3-m long CFA pile. Harsh winter conditions prevented the test pile to achieve its full structural capacity by the test day. The load test was conducted in 2 days in order to allow drilling to start for lightly loaded piles. Both tests load-settlement plots are shown in Figure 12.

0 5 10 15 20 25 30 35

1st Load Test Stage Q (kN) 1000 2000 3000 4000 5000 6000

0

2nd Load Test Stage Q (kN) 0

Settlement (mm)

Figure 10. CFA piles of the central piled raft

Settlement(mm)

failure load than the latter. In Canadian practice, geotechnical consultants have often preferred the former over the later due to its conservative nature. However, a serviceability analysis is not regularly done, and by doing so, there is a higher degree of certainty regarding the building stability and behaviour. It can be argued that with a reliable serviceability analysis, a more aggressive determination method of the failure load of a single pile can be used, such as the Hansen’s 80% method.

0

1000 2000 3000 4000 5000 6000

20 40 60

80 Figure 12. Load test results (from Keller 2022c)

Figure 11. CFA pile static load test (Keller 2022c) 5.2

Single Pile Failure Criterion

The Canadian Foundation Engineering Manual (2004) provides a literature review of 2 determination methods for the failure load of a single pile: Davisson’s Offset Limit method and the Hansen’s 80% Method. According to Almeida et al (2018), the former yields a more conservative

A clear plunging failure was observed during testing at approximately 5,000 kN, where additional load could not be sustained by the pile. Hansen’s 80% method tries to identify the point which 80% of the ultimate load would yield 25% of the ultimate measured settlement. When this point is achieved during testing, the test has failed geotechnically. This is done by plotting √𝑠/𝑄 vs 𝑠, where 𝑄 is the applied load and 𝑠 is the measured settlement of a pile load test. The linear portion of this plot would indicate the ultimate load based on equation 1 below. The ultimate settlement is calculated according to equation 2. The Hansen’s plot for the load test is shown in Figure 13. 𝑄𝑢 = 𝑠𝑢 =

1 2√𝐶1 𝐶2 𝐶2 𝐶1

[1] [2]

Where 𝐶1 and 𝐶2 are, respectively, the slope and yintercept of the line.

√𝑠/𝑄 [√mm/kN]

1st Load Test Stage

0.003 0.002 0.001 0.000

0

40

50

20 30 s (mm)

40 60 80 s (mm) Figure 13. Hansen’s 80% plot (Keller 2022c)

100

2nd Load Test Stage

0.003 √𝑠/𝑄 [√mm/kN]

10

0.002 0.001 0.000

0

20

Thus, the test pile failure load was estimated as approximately 4,900 kN as per the Hansen’s 80% method. 6

QA&QC PROGRAM

The main components of a reliable QA&QC program for CFA piles are good field notes taken by the field personnel and the Data Acquisition (DAQ) System, showing all pertinent installation parameters, such as auger rotation rate, depth of auger, torque and crowd forces, installation date, volume of concrete, concrete pressure, depth of the injection point, and auger withdrawal rate. DAQ records are available in a real-time basis for the contractor’s design engineer and/or operations personnel to review and provide any required guidance. Anomalies can be detected through this installation review process. Concrete strength lab testing should also be part of the QA&QC program to confirm that the required design concrete strength is achieved. Pile Integrity Tests (PIT) were also conducted as part of the QA&QC program. The PIT is performed by positioning one or two accelerometers on the pile head and using a hand-held hammer to impact the pile head. The PIT equipment collects the acceleration data and provides a plot which the aim is to identify the pattern of wave propagation and reflection along the pile depth. The intent is to identify any significant changes in the pile cross section. Two different companies were retained to perform PITs in this job. One critical part of the test is the pile head preparation. If the pile head is not correctly smoothened before the test, the test results may be significantly compromised. Figure 14 shows how the pile heads were prepared by the two companies.

Figure 14. CFA pile head preparation before PIT: company A (top) and company B (bottom) The results submitted by company A condemned 5 out 6 tested piles. The results submitted by company B raised questions regarding 3 out of 16 tested piles (all piles by company A were also tested by company B). Out of the 3 questioned piles by company B, 2 of them were concluded to have bulged at a certain elevation, when comparing the PIT with the DAQ reports. Thus, no remediation was necessary for these piles. The other questionable pile by company B was suspected to have its top section compromised. This pile had its top section exhumed since it was one of the piles with deep cut-off elevations for the central piled raft. No structural issues were found at its top

section. During the time of PIT, this pile, however, had a presence of a bar debonder pipe to facilitate its subsequential detailing process. It is believed that the gap intentionally created inside the debonder was raised as a structural issue of the pile. This difference in test results shows that PIT is extremely dependent on the testing company’s experience. One simple and critical step that was overseen, the pile head preparation, and the overinterpretation of the test results could have cost the owner a substantial loss both in financial and schedule terms. As recommended by the Federal Highway Administration (2007), an automated monitoring for quality control of CFA piles should not be solely relied upon. Field observations and documentation are important aspects for QA&QC. A simple monitoring of pile connections during pile installation can greatly assist on the quality assurance of the installed piles.

Canadian Commission on Building and Fire Codes. 2015. Structural Commentaries (User’s Guide – NBC 2015: Part 4 of Division B). National Research Council of Canada.

7

Keller. 2022c. Continuous Flight Augercast (CFA) Piles Load Test Report, Acton, ON

SUMMARY & CONCLUSIONS

CFA piles were successfully installed to support a 34storey high building. A detailed design process was followed to guarantee the building stability and serviceability conditions. The understanding of the structure-foundation interaction is important when defining the foundation layout of the building. A full-scale static pile load test was conducted to confirm the pile design parameters. A stringent QA&QC program was put in place to validate the CFA production piles. PIT results can complement the QA&QC program with careful review and corroborated with as-built records/field observations. ACKNOLEDGEMENTS The authors would like to acknowledge the work performed by the Field and Operations personnel from Keller and the project team. 8

REFERENCES

Almeida, A., Gurpersaud, N. and Lee, H. 2021. Application of high-capacity Continuous Flight Augercast (CFA) piles in the Greater Toronto Area. Proceedings of GeoNiagara 2021: 74th Canadian Geotechnical Conference, Niagara, ON. Almeida, A., Jesswein, M., Liu, J., Gurpersaud, N. and Bruce, J. 2018. Evaluation of Determination Methods for Ultimate Axial Capacity of Micropiles in Ontario soils. Proceedings of GeoEdmonton 2018: 71st Canadian Geotechnical Conference, Edmonton, AB. ASTM D1143 / D1143M-20. 2020. Standard Test Methods for Deep Foundation Elements Under Static Axial Compressive Load, ASTM International, West Conshohocken, PA, USA

Federal Highway Administration (FHWA). 2007. Geotechnical Engineering Circular No. 8 Design and Construction of Continuous Flight Auger (CFA) Piles, Report no. FHWA-HIF-07-03, Washington, DC, USA Fellenius, B.H., 2023. Basics of foundation design—a textbook. Electronic Edition, www.Fellenius.net, 548 p. Hain, S.J. and Lee, I.K. 1978. The Analysis of Flexible RaftPile Systems, Geotechnique, 28(1), 65-83 Keller. 2022a. CFA Piles Shop Drawings, Acton, ON Keller. 2022b. CFA Piles Design Report – R01, Acton, ON

McClymont & Rak. 2021. Supplementary Soil Investigation Proposed CFA installation, Toronto, ON The Canadian Geotechnical Society. Foundations Committee. 2004. Canadian Foundation Engineering Manual, Canadian Geotechnical Society

Driven Pile Resistance Gain with Time – Evaluation of Pile Load Tests: A Case Study Darcy Hansen, P.Eng., Anastasia Poliacik, P.Eng., Lisa Coyne, P.Eng., WSP Canada Inc. Tony Sangiuliano, Andrew DeSira, Ministry of Transportation Ontario ABSTRACT The Ministry of Transportation of Ontario (MTO) invested in a static pile load test during the detailed design of the Highway 400/Essa Rod overpass replacement in Barrie, Ontario, to determine the axial resistance of the test pile and to evaluate the strength gain with time. The static pile load test supported optimization of the foundation design, which ultimately included 312 HP 310x110 piles of lengths ranging from 30 to 35 metres, as well as development of acceptance criteria to control the installation of production piles. The test pile achieved a relatively low ultimate geotechnical resistance on initial driving, with negligible increase during restrike. However, the static pile load test demonstrated an appreciable improvement over a multi-week period, with the ultimate geotechnical resistance increasing by approximately 150% and 210% after 38 days and 70 days, respectively. Significant cost and schedule savings were realized in the use of higher geotechnical resistances in the pile design based on the load test data. RÉSUMÉ Le Ministère des Transports de l'Ontario (MTO) a investi dans un essai de charge statique sur pieux au cours de la conception détaillée du remplacement du pont de l'autoroute 400/Essa Road à Barrie, en Ontario, afin de déterminer la résistance axiale du pieu d'essai et d'évaluer l'augmentation de la résistance avec le temps. L'essai de charge statique a permis d'optimiser la conception de la fondation, qui comprenait finalement 312 pieux HP 310x110 d'une longueur de 30 à 35 mètres, et de développer des critères d'acceptation pour contrôler l'installation des pieux de production. Le pieu d'essai a atteint une résistance géotechnique ultime relativement faible lors de l'enfoncement initial. Cependant, l'essai de charge statique du pieu a démontré une amélioration appréciable sur une période de plusieurs semaines, la résistance géotechnique ultime augmentant d'environ 150 % et 210 % après 38 jours et 70 jours, respectivement. L'utilisation de résistances géotechniques plus élevées dans la conception des pieux sur la base des données de l'essai de charge a permis de réaliser des économies significatives en termes de coûts et de calendrier. 1

INTRODUCTION

The Highway 400/Essa Road Overpass Replacement in Barrie, Ontario involves the replacement of the existing 50 m wide, six-lane, single-span overpass with an 80 m wide, ten-lane, two-span structure under MTO Contract 2022-2008. The new abutments were designed to be supported on driven steel H-piles and the centre pier on drilled shaft foundations (locally termed caissons). Static pile load testing (SPLT) was completed during the design phase to optimize the pile design, given the significant number and length of piles required for support of the abutments. Approximately 12 km to 14 km of driven piles were estimated to be required to support the new abutments. Typically, the capacity of a driven pile is predicted using static empirical methods during detailed design. Dynamic analysis conducted during driving is a method to verify the pile capacity during the pile installation. The most accurate method of pile capacity determined is the Static Pile Load Test. This paper provides a comparison of the ultimate geotechnical resistances assessed for driven steel H-piles by static methods of analysis, static pile load testing including both the ASTM quick and maintained tests, and high-strain dynamic pile testing on the test pile and

production piles. Discussion is provided on the gain in geotechnical resistance with time, construction specifications, cost and schedule savings, and results obtained to date. Finally, “lessons learned” are provided for consideration in future pile load tests.

2

PROJECT AND SITE CONDITIONS

The existing Essa Road Overpass is located on Highway 400 in Barrie, Ontario. To accommodate the local road widening, the proposed replacement is planned to be a two-span bridge with semi-integral abutments with a total span length of about 94 m. Essa Road will require lowering by as much as 1.5 m in places so that the new overpass meets vertical clearance requirements. The location of the static pile load test was determined based on site access and proximity to the proposed structure. The test was located approximately 50 m west of the north abutment of the existing Essa Road Overpass.

3

SITE INVESTIGATION CONDITIONS

AND

SUBSURFACE

The subsurface conditions encountered in Borehole PLT-1 advanced at the pile load test site are generally consistent with those encountered in Boreholes ERO-1 to

Figure 1: Stratigraphic cross-section along north abutment of Essa Road Overpass Two boreholes and one Dynamic Cone Penetration Test (DCPT), advanced to depths of about 20 m to 30 m, were completed as part of preliminary design. As part of the detail foundation investigation, a total of twelve boreholes (designated as ERO-1 to ERO-12) were advanced to provide coverage for the proposed overpass foundation elements and one borehole (designated as PLT-1) was advanced at the proposed static pile load testing location. In-situ Standard Penetration Testing (SPT) was carried out in all boreholes and five standpipe piezometers were installed to monitor the stabilized groundwater level at the site. In general, the subsurface conditions at the Essa Road Overpass consist of fill and surficial deposits underlain by an extensive non-cohesive deposit of sand to silty sand approximately 30 m to 35 m in thickness, which has a variable state of compactness ranging from loose to very dense; however, the piles were installed predominantly in dense to very dense sand to silty sand with an estimated internal angle of friction of 35°. This deposit is interlayered with various fine-grained deposits and is underlain by very dense or hard deposits of silts, sands and clays, which extend to at least 40 m to 45 m below ground surface. A stratigraphic cross-section through the North Abutment of the Essa Road Overpass is shown in Figure 1; Borehole PLT-1 at the static pile load testing site is projected onto this cross-section. The groundwater level is generally about 3 m below the natural ground surface. Pressurized groundwater conditions (confined by discontinuous clayey sand interlayers) are present within the lower portion of the noncohesive deposit.

ERO-12.

4 4.1

TEST PILE INSTALLATION TESTING PROCEDURES

AND

DYNAMIC

Test Pile and Micropile Installation

The test pile (designated as TP-1) and associated reaction micropiles were installed between November 4 and 22, 2019, approximately 5 m west of Borehole PLT-1. A schematic of the test arrangement is shown in Figure 2. The steel HP 310x110 test pile, equipped with an Ontario Provincial Standard Drawing (OPSD) 3000.100 Type I driving shoe, was driven to a tip elevation of Elevation 216.1 m (about 31.6 m below ground surface) using a Liebherr H40/7 hydraulic hammer with a maximum rated energy of about 55 kJ. The test pile consisted of two sections of HP 310x110 welded together, for a total test pile length of approximately 33 m. 4.2

Hiley and High-Strain Dynamic Testing

A Hiley formula plot was completed in accordance with MTO’s Standard Drawing SS103-11. High Strain Dynamic Testing (more commonly known as Pile Dynamic Analyzer (PDA) testing) was carried out in accordance with ASTM D4945. Both Hiley and PDA testing were carried out at the End of Initial Drive (EOID) on November 4, 2019 and at the Beginning of Restrike (BOR) on November 11, 2019.

Figure 2: Static pile load test arrangement

5.1

STATIC PILE LOAD TEST PROCEDURES Pile Load Test Arrangement

The load test arrangement was constructed between November 19 and 22, 2019. As shown in Figure 2, the load test arrangement consisted of two W920x420 steel reaction beams, two timber cribs, and four reaction micropiles (46 mm diameter rebar grouted within 115 mm diameter holes to 30 m depth) to counteract the jacking load. A hydraulic cylinder jack was used to transfer the load between the top of the test pile and the reaction beam. A load cell was used to monitor the applied loads on the test pile. Four dial gauges were set up radially on a reference frame to measure the vertical movements of the top of the pile as the test progressed. The accuracy of the dial gauges is approximately ±0.13 mm. The dial gauge readings were used as the primary measurement system for pile axial movements and a wire line, comprised of a horizontal medal rod welded to the reference beam and vertical scale welded to the test pile plate, was used as the secondary system for pile axial movements. The pile load test arrangement was in general conformance with ASTM D1143M. 5.2

purpose of this test, the load increments were to be added until either the estimated failure load (i.e., 3,600 kN) was achieved or 30 mm (10% of the pile diameter) of cumulative pile displacement was observed. The pile was not to be loaded to failure, to allow for later testing to failure. As shown in Figure 3, 29 mm of cumulative pile displacement was observed after applying a load of about 2350 kN (or at the 13th load increment) approximately 3 hours after the quick test began. Therefore, upon completion of the 13th load increment, the loads were removed in six decrements of approximately 390 kN each. Applied Load (kN) 4000

3000

2000

1000

0.0 5.0 10.0 15.0

20.0 25.0 30.0

Procedure A – Quick Test

On December 10, 2019, 38 days after the installation and restrike of the test pile, a static pile load test was carried out in general accordance with Procedure A – Quick Test method of ASTM D1143M. Based on an anticipated failure load of 3,600 kN (equal to the estimated ultimate geotechnical resistance from static methods of analysis), test loads were applied in increments of approximately 180 kN (about 5% of the anticipated failure load). Each load increment was held for about 15 minutes. For the

0

35.0

Movement (mm)

5

40.0

Applied Load (kN) vs. Movement Pile Shortening Offset Limit Load

45.0 50.0 55.0

Figure 3. Applied load vs. movement for Procedure A – Quick Test (December 10, 2019 - 3 hours)

5.3

of the test pile and led to the localized deformation/yielding of the flanges at the top of the test pile.

Procedure B – Maintained Test

On January 13 and 14, 2020, 70 days after the installation and restrike of the test pile, a static load test was carried out in general accordance with Procedure B – Maintained Test method of ASTM D1143M. As the pile load test was completed early in the design phase and the design load was unknown at the time of testing, a design load of 1,800 kN was assumed. Based on the assumed design, test loads were applied in increments of about 450 kN (or 25% of the assumed design load). Each load increment was held for a minimum of 20 minutes and maximum of 2 hours, until the rate of axial movement did not exceed 0.25 mm per hour. For the purpose of this test, the load increments were to be added until either 200% of the design load (i.e., 3,600 kN) was achieved or progressive movement greater than 45 mm (i.e., 15% of the pile diameter) was observed. As shown in Figure 4, about 40 mm of cumulative pile displacement was observed prior to applying the last load increment approximately 8 hours after the test began. Upon applying the eighth and final load increment, the flanges on the test pile yielded and the jack shifted at a load of about 3,500 kN, and no further loading or unloading could be carried out. A photo of the yielded upper portion of the test pile is shown in Figure 5.

As evident from the measured increased rate of displacement, it appears the test pile was approaching axial geotechnical failure. Based on this information and considering the penultimate load of 3150 kN was approaching the estimated ultimate bearing resistance for the HP 310x110 pile, the design team concurred that it is unlikely that a higher bearing resistance could be justified for use in the design. As such, in consultation with the owners (MTO), it was determined that there would not be a significant benefit to repair the test pile and reaction micropiles to complete additional testing for an anticipated nominal gain in ultimate axial geotechnical resistance.

Applied Load (kN) 4000

3000

2000

1000

0 0.0 5.0

Figure 5. Test pile after localized yielding of top of pile during Procedure B – Maintained Test.

10.0

Lessons from this pile yielding failure are briefly summarized in the “Lessons Learned” section of this paper.

20.0 25.0 30.0 35.0

Movement (mm)

15.0

40.0 Applied Load (kN) vs. Movement Pile Shortening

45.0 50.0

Offset Limit Load 55.0

Figure 4. Applied load vs. movement for Procedure B – Maintained Test (January 13, 2020 - 8.75 hours) 5.4

Failure of Test Pile and Reaction System

On the basis of site observations and monitoring results, the cause of the failure was attributed to uplifting (up to about 20 mm) of two of the four reaction micropiles which caused an eccentric / inclined loading condition to develop; this resulted in stress concentrations in a portion of the top

6 6.1

GEOTECHNICAL RESISTANCE ASSESSMENT Static Analysis During Detail Design (Pre-SPLT)

The ultimate (unfactored) axial geotechnical resistances were assessed using various methods for steel HP 310x110 piles installed to depths ranging from about 25 m to 35 m below ground surface (i.e., pile tip elevations ranging from Elevation 226.5 m to 210.8 m) at the static pile load testing site based on Borehole PLT-1 and at the proposed north and south abutments for the overpass replacement based on Boreholes ERO-1 to ERO-12. The methods included both β and Ks effective stress methods outlined in CFEM, Poulos and Davis (1980), Meyerhof (1976) and Decourt (1995), using an estimated initial effective friction angle of 35° in the dense to very dense silty sand to sand. The ultimate geotechnical resistances were estimated to range from 3,000 kN to 3,800 kN including a combination of shaft resistance and endbearing. 6.2

Hiley Formula on Test Pile

The Hiley formula calculations are shown in Table 1 and were completed in accordance with the Standard Drawing SS103-11. It should be noted that Standard Drawing SS103-11 does not provide a value for the efficiency factor for a hydraulic hammer; however, the ratio between the measured energy and the energy input by the pile driving operator (i.e., a measure of the efficiency of the pile driving hammer) during PDA testing carried out at EOID and BOR is slightly above 1.0. Therefore, for the purpose of the Hiley formula calculation, the efficiency factor has been taken as equal to 1.0. Table 1. Ultimate Geotechnical Resistance of Test Pile Based on Hiley Formula Ultimate Pile Resistance (kN) 2 31.6 to 31.8 EOID 1,675 31.8 BOR 1,625 1 EOID denotes End of Initial Drive, and BOR denotes Beginning of Restrike. 2 The ultimate geotechnical resistances presented are unfactored values. Pile Depth (m)

6.3

Test Condition 1

High-Strain Dynamic Testing (PDA Testing) on Test Pile

The results of the PDA testing on the test pile at end of initial driving (EOID) and beginning of restrike (BOR) are provided in Table 2. Table 2. Ultimate Geotechnical Resistance of Test Pile from PDA Ultimate Pile Resistance (kN) 2 31.6 to 31.8 EOID 1,500 31.8 BOR 1,550 1 EOID denotes End of Initial Drive, and BOR denotes Beginning of Restrike. 2 The ultimate geotechnical resistances presented are unfactored values. Pile Depth (m)

6.4

Test Condition 1

Static Pile Load Test Procedure A – Quick Test

The pile movement shown on Figure 3 is based on the average of the four dial gauge readings, and the applied load was measured from the load cell output. Based on the results of Procedure A, the ultimate (unfactored) geotechnical resistance of the test pile after 38 days was assessed, and the results are summarized below.

6.5

Static Pile Load Test Procedure B – Maintained Test

The pile movement measurement shown on Figure 4 is based on the average of the four dial gauge readings. The applied load on the test pile was measured from the load cell output. Based on the results of Procedure B without extrapolation, the ultimate (unfactored) geotechnical resistance of the pile after 70 days is assessed to be at least 3,150 kN, which is equal to the last maintained load increment prior to the termination of the test. Based on the results of Procedure B with linear extrapolation, the ultimate (unfactored) geotechnical resistance of the test pile was further assessed, and the results are summarized in Table 4. Table 4. Geotechnical Resistance from Maintained Test Assessment Method

Ultimate Pile Resistance (kN)

Davisson Offset Method1

3,300

10 per cent of Pile Diameter

2,750

Davisson Offset Method based on linear extrapolation of the test data. 1

Although Procedure B was not completed in its entirety, the test reached the penultimate load increment and the data obtained up to that point are considered valid. The test was approaching the failure load as defined by ASTM D1143M, which is the load at which the total axial movement exceeds 15% of the pile diameter/width (or 45 mm for an HP 310x110 pile). Based on the trend of the data, it is anticipated that the test would have reached 45 mm of pile movement prior to reaching and maintaining 200% of design load at 3,600 kN, at which time the unloading portion of Procedure B would have commenced. 7

DISCUSSION

7.1

Geotechnical Resistance Recommendation

A comparison of the ultimate (unfactored) geotechnical resistances and the factored ultimate geotechnical resistances from the Hiley testing, PDA testing, and static pile load testing (SPLT) Procedure A and Procedure B is presented in Table 5. Table 5. Comparison of Geotechnical Resistance

Table 3. Ultimate Geotechnical Resistance from Quick Test Assessment Method

Ultimate Pile Resistance (kN)

Davisson Offset Method1

2,600

10 per cent of Pile Diameter

2,300

Davisson Offset Method based on linear extrapolation of the test data. 1

Factored Ultimate Geotechnical Resistance (kN)1

Assessment Method

Ultimate Geotechnical Resistance (kN)

Pile Driving EOID

PDA

1,500

750

Hiley

1,675

840

Pile Driving BOR

PDA

1,550

775

Hiley

1,625

810

Test

SPLT Proc. A

Davisson

2,600

1,560

10% of Pile

2,300

1,380

SPLT Proc. B

Davisson

3,150

1,890

10% of Pile

2,750

1,650

8 8.1

Geotechnical resistance factor is based on Table 6.2 of CHBDC CSA-S6:19 for a typical degree of understanding. A resistance factor of 0.5 is used for dynamic / pile driving testing and 0.6 for SPLT. 1

It is noted that the Davisson Method is commonly used to estimate the geotechnical resistance of HP 310x110 piles, based on pile load test data (CFEM, 2004), whereas the 10% of Pile Diameter method is better suited to estimate the geotechnical resistance of larger piles. As such, based on the SPLT Procedure B test, it was recommended that a factored ultimate geotechnical resistance of 1,890 kN be used for detail design of HP 310x110 piles that are driven to or below Elevation 216 m and into the very dense, native silty sand to sand deposit. Based on the applied load versus movement data from the Procedure B static pile load test, as plotted on Figure 4, it is estimated that the unfactored serviceability geotechnical resistance for 25 mm of movement is approximately 2,500 kN. The factored serviceability geotechnical resistance for 25 mm of movement is therefore 2,250 kN based on a geotechnical resistance factor, ϕgu, of 0.9 for a static test and a consequence factor, ψ, of 1.0 for a typical degree of site understanding (CHBDC CSA-S6:19). 7.2

Estimated Strength Gain with Time

Pile Acceptance Criteria

It was anticipated that the geotechnical resistance of the production piles would experience a similar gain in capacity over time, provided the piles were constructed similarly and driven to or below Elevation 216 m and into very dense, native silty sand to sand deposit. As there was not an appreciable increase in geotechnical resistance measured at 7 days after EOID via Hiley and PDA, the design team decided there was little value in prolonging the restrike timing beyond the minimum 24-hour period specified in OPSS.PROV 903. For a design factored ultimate axial geotechnical resistance of 1,890 kN, the acceptance criteria for production piles shown in Table 7 were included in the contract. PDA testing was specified to be completed on at least 10% of piles. Table 7. Acceptance Criteria for Production Piles Test Method High-Strain Dynamic (PDA) Testing Hiley Testing

8.2

The results of the PDA and Hiley tests performed at EOID were compared with the results of the PDA and Hiley tests performed at BOR, and the results of Static Pile Load Testing ASTM D1143M Procedure A at 38 days and Procedure B at 70 days, as summarized in Table 6. An increase of up to 210% was observed in the estimated ultimate geotechnical resistance over a period of 70 days.

Test Condition

Minimum Ultimate Geotechnical Resistance (kN)

EOID

1,500

BOR

1,550

EOID

1,625

BOR

1,625

Contract Specifications

0

1,500 to 1,6751

-

7

1,550 to 1,625

Approximately 0 (±3)

SPLT Proc. A

38

2,6002

155 to 175

The target criteria rely on an observed gain in geotechnical resistance with time based on the SPLT Procedure B test results measured approximately 70 days after test pile installation. The target criteria are appropriate for design provided that superstructure construction will not occur until after the piles have experienced the majority of the anticipated strength gain. An Operational Constraint was included in the contract that specified the superstructure construction could not occur for at least two months following production piling. Commencement of substructure construction could be permitted prior to this two-month period. Given the potential variability of the subsurface conditions at this site, it was recommended that any test results below the acceptance criteria be assessed by the Foundation Engineering Specialist (FES) in conjunction with the Design Team, and consideration should be given to the measured results from PDA and Hiley testing for nearby piles.

SPLT Proc. B

70

3,150

190 to 210

8.3

Table 6. Summary of Increase in Geotechnical Resistance with Time

Test Pile Driving EOID Pile Driving BOR

Set-up Period (Days)

Ultimate Geotechnical Resistance (kN)

2

Average Geotechnical Resistance Gain3 (%)

1

The range of ultimate geotechnical resistance is obtained from the Hiley formula and PDA testing. 2 The ultimate geotechnical resistance is obtained using Davisson method, as this method considered to be better suited for HP 310x110 piles. 3 The approximate average gain in geotechnical resistance has been compared to the PDA and Hiley test results at EOID. 1

IMPACT ON DESIGN AND CONSTRUCTION

Production Piling

At the time of writing this paper, pile installation for the portions of the new structure which are to accommodate the outside widening of Highway 400 (adjacent to the existing highway) has been completed. A total of 54 PDA tests were carried out, at EOID and BOR for various soil set periods, on 54 of the production piles. The results of

the PDA testing showed that about 80% of the piles achieved estimated ultimate (unfactored) resistances greater than 1500 kN at BOR, as specified in the Contract Documents. In general, the piles saw an increase in geotechnical resistance between the EOID and subsequent BOR testing. The PDA test results from the production piles are compared to the target BOR value and the results of the static pile test in Figure 5.

Figure 5. Comparison of production pile PDA results with target BOR value and static pile load test results. In cases where the estimated BOR values were lower than 1,500 kN, the results were evaluated relative to the depth of the pile, Hiley results (corrected for hammer efficiencies measured during PDA testing), the blows per last 0.2 m of pile penetration, field observations and the results of other PDA tests within the group of piles. The strength gain observed in the production piles through PDA testing was generally similar to or greater than that observed in the test pile. Based on review by the Foundation Engineering Specialist, the designers and MTO, it was decided based on the results of the PDA and the observed resistance gain that sufficient production piles in each quadrant had achieved the target BOR resistance and that the pile group as a whole was anticipated to achieve the required design loads given the two-month period of setup prior to superstructure construction. 8.4

Design Efficiency

The results of the pile load test demonstrating significant resistance gain with time allowed for optimization of the abutment pile design, decreasing the total number of piles required for the contract. The results of PDA testing during production piling provided the contract administration and design team with appropriate information to make informed decisions with regards to pile acceptance, in consideration of the resistance gain with time. This allowed for minimal impact to the construction schedule, which may have had to be extended if further pile driving was required to obtain the target geotechnical resistances. This was particularly

impactful, as the fine grained non-cohesive and cohesive deposits found at this site proved challenging ground conditions for pile driving in some locations.

9

LESSONS LEARNED

The following “lessons learned” are provided for awareness and consideration for static pile load testing. Where applicable, these recommendations should be addressed in the terms of reference or specifications for static pile load tests. 1. The installation of the reaction micropiles and the setup of the reaction frame should be supervised and signed off by the structural engineer who designs the reaction frame, prior to commencement of the static pile load test(s). An on-site inspection of the reaction frame should be carried out by the reaction frame designer prior to each pile load test if and where multiple load test procedures are completed. As part of this inspection, the reaction frame designer/contractor should confirm and document that the center of the reaction frame is plumb with the center of the test pile, and that the hemispherical bearing, jack, and load cell are centered prior to commencing each pile load test. Where relatively high ultimate test loads are applied, the upper portions of the test pile flanges should be reinforced to minimize the potential for localized deformation or buckling that may be associated with non-concentric loading on the top of the pile. 2. The grout mixture (i.e., the cement, bentonite, and water ratio) used for reaction micropile installation should be recorded and grout samples should be obtained at the time of installation and submitted for laboratory testing to confirm adequate grout strength. Confirmation of grout strength should be provided prior to commencement of any pile load tests. 3. Each reaction micropile should be proof-tested to confirm it can withstand the planned maximum test load. 4. It is recommended that the contract documents specify surveying of the reaction frame be carried out using optical level shooting of fixed points on each reaction pile at a specified frequency (e.g., every hour), to provide more accurate observations of movement. It is further recommended that review and/or alert levels for differential movement of dial readings be established as a safety precaution related to the potential failure of the reaction system and/or the test pile. 5. It is recommended that data acquisition technology be used in place of manual dial gauges to collect consistent, real-time data throughout the static pile load tests. Implementation of data acquisition systems allows for field staff to be at a safe distance from the hydraulic jack and reaction frame should

any sudden shifts/movements (failure) of the reaction system or test pile occur. 6. Consideration should be given to installing vibrating wire piezometers to measure porewater pressures prior to, during, and after test pile installation, which will allow for an improved understanding of initial porewater pressure development and dissipation over time, and in turn could be used to correlate strength gain or relaxation over time. 7. If a hydraulic hammer is used for installation of piles, OPSS.PROV 903 (Deep Foundations) requires the contractor to submit information on the hammer energy, rated energy and operating efficiency; if this cannot be demonstrated in advance for a hydraulic hammer, the efficiency of the pile driving hammer should be verified during production pile installation using PDA testing. It is recommended that more research / understanding be carried out to determine whether the Hiley test method (which remains MTO’s standard test on production piles) is still applicable and can be appropriately modified for use with a hydraulic hammer (as opposed to conventional diesel hammer). This could include PDA testing to confirm the efficiency of the hydraulic hammer, as well as continued comparison of Hiley and PDA testing on production piles. It is noted that some in the deep foundation industry are migrating toward the use of PDA testing and elimination of Hiley testing, which has safety implications related to placing personnel in or near to the “line of force”. Based on the preliminary findings from the production piles installed at this site to date, the hammer efficiencies were found to vary between piles and depths. In general, the Hiley tests were not consistent with the results of the PDA, even when corrected for the energy efficiency range observed during PDA testing. However, these observations may be due to the specific conditions at this site; hence, site-specific comparisons between Hiley and PDA are recommended if and where any reliance is to be placed on Hiley testing results. 10

CONCLUSIONS

The static pile load test carried out for the detail design at the Highway 400-Essa Road Overpass site provided the design team with information that allowed them to develop an efficient design, which led to significant savings in the number and length of piles, and to savings in time during construction associated with production piling. Specifically, a higher factored ultimate geotechnical resistance was used in design than was suggested by static analysis or by the results of Hiley and PDA testing on initial driving. The results also allowed for the construction team and foundation engineering specialist to assess pile acceptance during construction where there were deviations from the target estimated ultimate resistance, to make informed assessments of the acceptance of the pile groups during construction. In this regard, the static pile

load test results also contributed to significant time and cost savings during construction. The static pile load testing also provided lessons learned that should be considered for future pile load tests in similar conditions. Further assessment based on the results of longer-term static pile load tests at multiple sites is recommended to permit further calibration of static analyses with such results. 11

REFERENCES

ASTM International D4945 Standard Test Method for HighStrain Dynamic Testing of Deep Foundations ASTM International D1143M Standard Test Methods for Deep Foundations Under Static Axial Compressive Load Canadian Geotechnical Society. 2006. Canadian Foundation Engineering Manual, 4th Edition. BiTech Publisher Ltd., British Columbia, Canada. Canadian Standard Association (CSA) Group. Canadian Highway Bridge Design Code (CHBDC 2019) and Commentary on CAD.CSA S6:19. Chapman, L.J. and Putnam, D.F. 1984. The Physiography of Southern Ontario. Ontario Geological Society, Special Volume 2, 3rd Edition. Accompanied by Map P2715. Golder Associates Ltd., 2021. Foundation Investigation and Design Report, Essa Road Overpass, Highway 400/Essa Road Interchange Reconstruction, Barrie, Ontario, G.W.P. 2337-16-00, Assignment No. (MTO GEOCRES No. 31D-767) MTO’s Standard Drawing SS103-11 Pile Driving Control Ontario Provincial Standard Specifications, OPSS.PROV 903 Construction Specification for Deep Foundations Ontario Provincial Standard Drawings OPSD 3000.100 Foundation Piles, Steel H-Pile Driving Shoe

Centrifuge Modeling for Cyclic Axial Performance of Helix Piles in Sand Naveel Islam, Lijun Deng, Rick Chalaturnyk Department of Civil & Environmental Engineering-University of Alberta, Edmonton, Alberta, Canada & Luke Penner Reaction Piling Inc., Nisku, Alberta, Canada ABSTRACT The article shows the axial load responses of five instrumented model aluminum piles subjected to incremental cycles of pseudo-static loads through centrifuge model testing in dry sand. Average axial cyclic displacements of 1%, 2%, 5%, and 10% of the shaft or helix diameter were adopted in four packages. Later, a post-cyclic monotonic compressive displacement was implemented. All load tests were conducted inflight at 20 g scale units. It was observed that, with increased cycle intensity, the load-bearing capacity of the piles increased. However, a loss of strength occurs due to a possible gap formed at the base and around helices. Moreover, the theoretical equations over predicted the ultimate axial capacity of the piles. The piles with double helices and larger inter-helix spacing generally provided a higher load-bearing capacity. Last, the distribution of the ultimate axial loads, the maximum loads from cyclic sequences, and the ultimate unit post-cyclic shaft resistances are shown. RÉSUMÉ L'article montre les réponses aux charges axiales de cinq pieux en aluminium modèles instrumentés soumis à des cycles incrémentiels de charges pseudo-statiques par le biais d'essais sur modèle de centrifugeuse dans du sable sec. Des déplacements cycliques axiaux moyens de 1 %, 2 %, 5 % et 10 % du diamètre de l'arbre ou de l'hélice ont été adoptés dans quatre ensembles. Plus tard, un déplacement compressif monotone post-cyclique a été implémenté. Tous les tests de charge ont été effectués en vol à des unités d'échelle de 20 g. Il a été observé qu'avec l'augmentation de l'intensité du cycle, la capacité portante des pieux augmentait. Cependant, une perte de résistance se produit en raison d'un éventuel espace formé à la base et autour des hélices. De plus, les équations théoriques sur prédisent la capacité axiale ultime des pieux. Les pieux à double hélices et à espacement inter-hélices plus important ont généralement fourni une capacité portante plus élevée. Enfin, la distribution des charges axiales ultimes, les charges maximales des séquences cycliques et les résistances post-cycliques unitaires ultimes de l'arbre sont présentées. 1

INTRODUCTION

Helical piles, an innovative deep foundation type, are popular in the construction industries, predominantly in Western Canada. These piles are used to support transmission towers, solar panels, wind turbines, and building systems that might exhibit cyclic motions during their operational stage or earthquakes (Urabe et al., 2015; Al-Baghdadi et al., 2016; Schiavon et al., 2019; Brown et al. 2019). Such cyclic motion in the axial direction of the piles might lead to an excessive settlement or axial pile failure. The design of helical piles subjected to vertical cyclic load or seismic motions is usually overlooked in practice (Tsuha et al., 2012). Moreover, the current guideline includes estimations for the axial load-bearing capacity of the piles for static conditions only (e.g., Mitsch and Clemence 1985; Ghaly and Hanna 1991, CFEM 2006, Perko 2009). A cyclic nature exhibits complex loading that demands appropriate experimental facilities. Some researchers conducted load-controlled, axial cyclic field and model tests on the sand to study the axial load response behavior of piles (Jardine and Standing, 2012; Tsuha et al., 2012). Through a continuous axial load in 15 cycles, El Naggar and Abdelghany (2007) showed a reduction of about 10% was observed in the helix piles. Petereit (1987), through a cyclic load test on 1 g model helix anchors in the sand,

observed a continuous accumulative displacement even at the low cycles. The higher the load amplitude cycle, the larger the displacement rate. Khidri and Deng (2022) performed field axial cyclic load-controlled tests on instrumented screw piles in the sand to measure the load transfer within the pile segment. However, field tests are affected by the enhanced cost of testing at the site. Centrifuge modeling of helix pile-soil interaction was proved effective for monotonic testing (Levesque et al., 2003; Tsuha et al., 2012). Additional strain gauges as instrumentations could provide useful insights into the axial load redistribution between the shaft and helices (Li et al., 2022). However, studies on the cyclic nature of helix or similar pile types in sands using centrifuge are very limited. Most research was focused only on the effect of pile geometries and the number of cycles over the load-bearing capacity of the piles. Centrifuge testing by Urabe et al. (2015) showed the degradation of shaft friction by about 20-50% when subjected to cycles on both smooth and wing piles in sand. Schiavon et al. (2019) performed similar tests on helix anchors to ascertain the post-cyclic monotonic performance in the sand. Post-cyclic tests on such piles showed a reduction in the ultimate uplift capacity of 4-9 %. In contrast, there is a gap in appropriate knowledge on the axial cyclic load response mechanism and capacity of helix piles in sand.

171.4 P0CyC

77.3 113

B

183.1

171.2 P2CyC

112.7

A 379 303

A

P4CyC

B

.7 Ø12 Ø3 171.5 P3CyC 8.1 268.8

9 4. 11

268.8 P1CyC

x NI02 (a) z (b)

A-A

300

SG1 Silica Sand

SG4

1D SG4 SG3 2D 3.5D 1D SG3 SG1 SG2 0.5D SG2 SG1 0.3D 1.5D D d 2D

228.6

SG2 3.5D

P4CyC

P2CyC

71.4

100

P0CyC

x

NI02 (b) z (c)

B-B

0.5D SG2 SG1 0.3D

1D SG4 1D SG3 SG2 1.5D 2.5D SG1

228.6

SG3 3D

P3CyC

71.4

100

P1CyC

300

The centrifuge model testing was conducted under the designation of test series NI02. Islam et al. (2022) detail the results of the test series NI01. In NI02, five model piles were tested in a model silica sandbox. Fig 1a shows the test layout plan of the model piles. Fig. 1b-c shows the cross-sectional profiles of the model piles arranged in two separate carriageway sections on the model box. The piles are designated as PX L, where P stands for pile, X indicates the pile number, and L stands for loading type. The smooth pile is denoted as P0, and the helix piles, as P1 to P4. In the following sections, the L is designated either as M or C to separate the non-cyclic monotonic (M) test (see Islam et al. 2022) from the post-cyclic monotonic (C) tests (current test). All the model piles were installed at 1g but later tested in flight at the 20 g scale units for each displacement sequence. Table 1 summarizes the scale factors of the analyzed parameters from the prototype to the model scale of the pile. After completing each test pile (one at a time), the model piles were left in place until the end of the test of the last pile. The minimum center-to-center pile spacing in rows was kept four times the helix diameter to minimize test zone influence (Li et al., 2022). Table 2 summarizes the dimensions of the model piles in model and prototype scale units. All tested five aluminum piles, i.e., one smooth (P0), one single-helix (P1), and three double-helix piles with helix spacing ratio (Sr) of 1.5 (P2), 2.5 (P3), and 3.5 (P4) had a prototype shaft diameter, (d) 254 mm, and for the helix piles, the helix diameter, (D) 762 mm. Common to practice, the pitch of the helices (P), defined as the opening size of the helix, was set to 254 mm in prototype scale, equal to the shaft diameter of the pile. Axial strain gauges (SGs) are installed at designated locations along the pile shaft inside a grooved trench (see Fig 1b-c for the SG locations). Marked as SG1 from the tip to the SG4 at the top end of the pile, these SGs are placed at a minimum distance of 10 mm from the helix blades to avoid any possible helix bending stresses. The setup and calibration of the strain gauges are detailed in Islam et al. (2022). Table 3 summarizes the properties of the model silica sand. The silica sand (Sil3) was procured from Sil Industrial Minerals in Edmonton, Alberta. Sil 3 was selected as the effective model sand considering a reduced scale effect.

(a)

811 709

51

38

CENTRIFUGE MODELING

y

30

2

Schiavon et al. (2016) showed through centrifuge tests that, for the model sand to impose no scale effect on the piles, the sand should consist of uniformly distributed grain sizes, should not contain a lot of fine sand dust, and requires an effective helical radius of the model helix pile (i.e., rh) to the average grain size (d50) greater than 58. From the particle size distribution of Sil 3 sand, the estimated d50 = 0.29 mm and rh =19.05 mm; thus, rh/ d50 = 65.69. Moreover, the estimated Cu = 1.5 mm and Cc = 1.08 mm for the model sand indicates a uniform distribution of grain sizes.

Silica Sand

30

The study is intended to present the axial load responses of smooth, single, and double helix piles in sand subject to incremental pseudo-static cycles through centrifuge tests. The developed centrifuge modeling technique (see Islam et al. 2022) is adapted to accustom the cyclic nature of displacements at stages. First, the axial load-displacement relationship of a smooth and selected helix pile is shown at definite displacement stages and pile depths. Second, the axial responses of all five model piles at the post-cyclic monotonic compressive displacement stage are shown for each strain gauge location. Moreover, the measured and the estimated ultimate capacity from the post-cyclic and nocyclic stages are compared. Last, the internal axial ultimate load transfer and the ultimate shaft resistances are shown over the pile depth.

x

NI02 (c) Vertical Smooth pile D=38.1mm d=12.7mm

Axial Strain Gauges 0

12

24

Vertical Helix pile Unit: mm 36 inch

Figure 1. NI02;.7(b) Cross-sectional 0 .1 (a).2Layout .3 plan .4 of.5test .6 .8 .9 1 meter profile at A-A section; and (b) Cross-sectional profile at BB section. All dimensions are in model scale. Table 1. Selected scale factors (prototype/model) of centrifuge modeling tests Term

Force

Dimension

Stress

Scale Factor

400

20

1

Table 2. Summary of Pile Dimensions in Model and Prototype Scale Unit

Prototype Scale Unit

No. of helices

Shaft dia. d (mm)

Helix Dia. D (mm)

Pile Length L (mm)

Helix Spacing S (mm)

Lower Helix Embedment E (mm)

Sr = (S⁄D)

P0

0

12.7

38.1

271.8

NA.

150

NA.

P1

1

12.7

38.1

271.8

NA.

150

NA.

P2

2

12.7

38.1

271.8

57.2

150

1.5

P3

2

12.7

38.1

271.8

95.2

150

2.5

P4

2

12.7

38.1

271.8

133.4

150

3.5

P0

0

254

762

5436

N.A.

3000

N.A.

P1

1

254

762

5436

N.A.

3000

N.A.

P2

2

254

762

5436

1144

3000

1.5

P3

2

254

762

5436

1905

3000

2.5

P4

2

254

762

5436

2668

3000

3.5

Table 3. Geotechnical properties of model silica sand Term Specific Gravity (Gs) (Correlated) Average Model density (), Mg/m3 Constant Volume Friction angle (ϕcv), °.* Relative density (%) (As-built) Void Ratio (As-built) Note: *Measured from the direct shear test confinement stress level 50-400 kPa.

Value 2.65 1.61 31.7 49.7 0.646 at the

As shown in Fig. 2, the centrifuge model assembly consists of three major parts: the dual-axis electric actuator affixed at the top of the container, the soil model box, and the

constant rpm gear motor mounted to the actuator and the head of the pile. The electric actuator assists in the axial movement of the piles. It is controlled through computer 1, located in the control room. The soil model box, constructed as an aluminum container, measures internally 709.2 mm (length) x 300 mm (width) x 400 mm (height) with wall thickness ≥ 30 mm. Appropriate sand Pluviation technique was adopted to control the height and rate of deposition of sand to ensure an optimum relative density throughout the model box. The as-built relative density and void ratio of sand for centrifuge testing are shown in Table 3. Details of the adopted Pluviation technique and calibrations have been mentioned in Islam et al. (2022). Last, the gear motor exerted a driving torque at a constant

180 120 60

Axial Load (kN)

0 -60

Normalized Displacement (%)

Model Scale Unit

Type

22.5 20.0 17.5 P0 PL1 L1 SG2 P0 PL1 L3 SG2 15.0 12.5 10.0 7.5 5.0 2.5 0.0 -2.5 0.00 0.25 0.50

300

0.0 40

0.5 P0 PL1 L1 SG1 P0 PL1 L3 SG1

P2 PL2 L1 SG2 P0 PL2 L3 SG2

P0 PL1 L2 SG2 P0 PL1 L4 SG2

P0 PL3 L1 SG2 P0 PL3 L3 SG2

1.5

1.75

2.00

2.25

P0 PL4 L1 SG2 P0 PL4 L3 SG2

150 0

0

-150

-100 0

1

2

3

4

80

0 160

P0 PL2 L1 SG1 P0 PL2 L3 SG1

2

4

P0 PL3 L1 SG1 P0 PL3 L3 SG1

P2 PL2 L2 SG1 P0 PL2 L4 SG1

6

8

0

10

120

240

40

80

160

20

40

0

-60 0.5

1.0

1.5

2.0

1

2

3

4

16

20

0

-40 0

12

P0 PL4 L2 SG1 P0 PL4 L4 SG1

80

0

-20 0.0

8

P0 PL4 L1 SG1 P0 PL4 L3 SG1

60

0

-40

4

320

P0 PL3 L2 SG1 P0 PL3 L4 SG1

20

-20

P0 PL4 L2 SG2 P0 PL4 L4 SG2

300

100

0 2.50

-75 2.0

450

200

75

1.25 1.50 Time (Hrs)

P0 PL1 L2 SG1 P0 PL1 L4 SG1

P0 PL3 L2 SG2 P0 PL3 L4 SG2

300

150

1.00

1.0

600

400

P0 PL2 L2 SG2 P0 PL2 L4 SG2

225

0.75

-120

-80 0

2

4

6

8

10

0

4

8

12

16

20

Normalized Displacement (%)

Figure 2. Centrifuge Model Assembly. rotation rate of 23 revolutions per min (rpm)—a 12-volt DC power supply assisted in maintaining the required torque.

Simultaneously, an axial displacement rate of 292.2 mm/ min (= 23 × P/min) was maintained using the electric

Normalized Displacement (%)

actuator. Such dual action of the actuator and the motor was to maintain the standard practice of installing the piles at a penetration rate of one pitch of the helix per revolution; to keep the soil disturbance minimal (Perko, 2009; Tsuha et al., 2012). Axial SGs are connected to the data logger channel at the side and later to the data logging computer in the control room. Figure 3 presents the sequences of axial loading incorporated to implement the cyclic and post-cyclic monotonic compression on the piles. First, four sequences of one-way compressive-tensile displacements are implemented in four loops at a constant 1 mm/min displacement rate. A similar sequence of one-way load-

22.5 20.0 17.5 15.0 12.5 10.0 7.5 PL2 5.0 PL1 2.5 0.0 -2.5 0.00 0.25

controlled cycles has been implemented in the literature (e.g., Schivon et al., 2019; Khidri and Deng, 2022). The cyclic load sequences denoted by PL1 to PL4 are at a mean normalized displacement of 1%, 2%, 5%, and 10% of the shaft (for P0) and helix diameter (for P1-P4). Later, a monotonic compression test (CL) was performed at the post-cyclic stage by pushing the actuator down at a constant 0.333 mm/min rate up to a normalized displacement of 20% of the shaft (for P0) and helix diameter (for P1-P4). It is to note that the constant rate test has also been used in past studies on centrifuge tests on helical piles (Wang et al. 2013; Li et al. 2022).

PL4

CL

PL3

Compressive Displacement

Pseudostatic Cyclic Displacements

0.50

0.75

1.00

1.25 1.50 Time (Hrs)

1.75

2.00

2.25

2.50

Figure 3. Stages of subsequent pseudo-static cyclic and compressive normalized axial displacements (w/d for P0 and w/D for P1-P4) on the pile against time. 3

AXIAL BEHAVIOR OF PILES

The axial load vs. normalized displacements of selected model piles are shown in Fig. 4 and 5. The relationships are established from the strain gauges located along the pile depths. For each sequence of cycles (PL1 to PL4), the curves L1 to L4 indicates each of the four loops. From Fig. 4, the smooth pile (P0), it was observed that the axial loadbearing capacity increased with higher cycle intensity. A similar loading-unloading trend was observed at each loop at a particular sequence. At a definite stage, the first loop cycle pushes a relatively undistributed sand layer, depicts the maximum load resistance, and later, the cycles degrade in an area with repetitions. Moreover, a rapid strength degradation was observed at the end stage of displacement (nearly about 0% at PL1 to about 5% at PL4). Such nature illustrates the general gap effect at the pile base, mostly observed during frequent loading-unloading and reloading sequences of motion. From Fig. 5, the helix pile (P3), it was noted that the cyclic load vs. normalized displacement relationship for a particular load sequence (along the columns) is similar at the definite SG location. The load-bearing responses at the SG4 (at the pile top) were considerably higher compared

to SG1 (at the bottom). Along the rows, the load capacity at the higher cycle intensity was considerably larger for a particular SG. The area under the curve degrades gradually with increased cycles and intensity. Such as, at PL1, the first loop, L1, depicts the general trend of the load response for a typical helix pile in sand. However, with additional loops, the strength degrades gradually. Later, at PL2, the trend is different that PL1; there is a gradual strength decrease even during the loading stage, at about 3% normalized displacement. Such nature might be because of the gap of sand surrounding the helix location caused by the previous loading sequence. Like the P0 pile, a gradual horizontal shift of possible strength increase was observed from L1 to L4 at each PLs. Figure 6 shows the maximum axial cyclic capacity distribution along the pile depth for P0 to P4 (left to right) for each cyclic load stage. It can be seen that, with increased cycle intensity, the capacity of the piles increased linearly with depth. The piles P2 to P4 had a double helix. It can be observed that the maximum capacity was higher in pile P3 with larger spacing in comparison to pile P2.

150

150

PL1

100

100

-50 -100

Axial Load (kN)

Axial Load (kN)

0

0.0 30

0.5 30

1.0

1.5

1.0

0

2.0

601

1

2

2

3

3

-804

4

40 20

-30

-45

0 0.5

1.0

-45

1.5

-20

2.0

0

1

2

-20 0.0

0.5

1.0

1.5

2.0

8

0280 4

10

1

2

140

30

0

70

0

0

-30

0

-70

4

0

2

4

6

8

12

16

16

20

20

70

SG1

10

0

4

8

12

16

20

-70

Normalized 4 0 Displacement 2 4 6 8 (%) 10

3

8

12

140

30

3

4

8

210

210

-30

0

0

8 -120 10

60

60

0

0.0

6

6

280

20 -30

4

4

90

90

-15

-15

2

120 40

0

0

0

0 1202

60

15

15

-120

-80 0

1.5 -60 2.0

L1 L2 L3 L4

0

0 -60

0.5

L1 L2 L3 L4

0

0

0 0.0

SG2

120

120

80 0

-50

240

240

80

60

-100

360

360

160

160

60

PL4

480

480

240

240

120

120

0

PL3

320

320

180

180

50

50

PL2

240

240

0

4

8

12

16

20

Normalized Displacement (%)

Figure 4. Axial Pseudostatic Cyclic Load vs. Normalized Displacement for Smooth Pile (P0).

150

600 450

-150

0.0 600

1.0 2.0

0

1 60

0

2

1

3

2

4

3

4

0.0

2400 600

160

3600

240

80

2400

120

0

1200

0

-80

0

-120

2.5 120

0

2 5.0

4 7.5

6 8 10.0

-45 0.5

0.0 1.0

0.5 1.5

1.0 2.0

300

-600 1.5 2.0 0

-20 1

02

13

600

2 4

70

-30 2.5 0 5.02

-70 4 7.5

6 8 10.0

10 0

5 0

104

15 8

20 16 12

20

2400 1800

1050 700

1200

350

600

0 0

SG3

0

4 0.0

200 100

20

0

Normalized Displacement (%) 1400

400

200

3

12 16 20

1200

0 0

0.0

8 15

140

600

-300

0

4 10

2400

30 0

0

210

1200

0

-30

5

280

60 20

SG4

3600

1800

150

10 0

4800

90

40

300

-15

Axial Load (kN)

0

-60

900

0

300

1.5-8002.0

4800

900

0

-400

0.5 1.5

L1 L2 L3 L4

360

1800

60

0

0.0 1.0

PL4

480

6000

2700

120

-100

PL3

240

400

15

450

320 3600

800

-50

0.5 30

PL2

180

0

Axial Load (kN)

0

240 1200

50

300 150

PL1

100

SG2

-200 0

0 -100

-400 0.0

0.5

1.0

1.5

2.0

100

0

1

2

3

0.0

4

2.5

5.0

7.5

0

10.0

300

1000

1800

200

750

1350

100

500

900

0

250

450

-100

0

0

5

10

15

20

50

0

SG1

-50 0.0

0.5

1.0

1.5

2.0

0

1

2

3

4

0.0

2.5

5.0

7.5

10.0

0

5

10

15

20

Normalized Displacement (%)

Figure 5. Axial Pseudostatic Cyclic Load vs. Normalized Displacement for Helix Pile (P3).

Qmax (kN) 0

100 200 300 400 500

0

1000

2000

3000

4000 0

1000 2000 3000 4000 0

1000 2000 3000 4000 5000 0

100

200

300

400

0.0

Depth in pile (m)

0.5 1.0 1.5 2.0 2.5 3.0

P1

P0

PL1 PL2 PL3 PL4

P2

P3

P4

Figure 6. Axial Maximum Cyclic Load Distribution along the pile length.

SG2

(a) 400 300

0 0

5

10

15

20

3000

Axial Load (kN)

5000

(b) 4000

SG3

3000 SG2

2000 SG1

1000 0 0 4000

5

10

15

(c)

20

SG4

3000 SG3

2000

(d)

SG2

SG1

(c)

15

20

SG1

0 200

10

15

0

5

10

15

20

(e)

150 SG4

100

SG4 SG3

SG3 SG2

50

0

0

SG1

5

10

15

20

Normalized Displacement (%) SG4

0 200

0

5

(e)

Figure 7. Axial Post Cyclic Compression vs. Normalized 10 15 20 SG3 Displacement: (a) P0; (b) P1; (c) P2; (d) P3 & (e) P4.

150

for smooth piles: 𝑄𝑢 = 𝜋𝑑𝐸𝜎𝑣 𝐾𝑠 𝑡𝑎𝑛𝛿 + 𝑁𝑡 𝜎𝑣 𝐴𝑠

0 5

SG1

1000

ForSG2the smooth pile, according to the pile load testing SG4 guideline (ASTM D 1143), the failure load is stated by the SG1 100 SG3 stage where the axial displacement reaches 15% of the 0 shaft diameter. For helix piles, however, there are no SG2 0 5 50 10 15 20 standard guidelines; however, in practice, it is common to Normalized Displacement (%)ultimate capacity at 5% of the normalized consider 0 0 5 displacement 10 15 20 for sand (Sakr, 2011; Elsherbiny et al., 2013). theoretical(%) estimated ultimate capacity was also NormalizedThe Displacement calculated for the smooth and helix piles following the Canadian Foundation Engineering Manual (CFEM 2006):

SG2

0

SG2

2000

SG2

5 10 1000

SG3

1000

2000

1000

Axial Load (kN)

0 4000

20

2000

0 100

15

3000

1000

SG1

200

SG1

4000

2000

SG4

3000

SG3

3000

(d)

4000

4000 5000

5000

SG2

Axial Load (kN)

500

Axial Load (kN)

500compression Figure 7 shows the post-cyclic monotonic load vs. normalized displacement relationship (a) for all five 400 model piles from the SGs over the pile depth. All piles were displaced up to a normalized displacement300 of 20% of the shaft (for P0 only) or helix diameter. There is a nonlinear increase in load resistances on all the piles200 with increased displacement with maximum load observed for SG4 100 compared to SG1. The helix piles' bearing capacity was significantly higher than the smooth pile. However, pile P4 0 0 5 10 did not represent the true load-bearing 5000 resistances, (b) possibly due to SG data logging errors.

20

Normalized Displacement (%)

and for helix piles:

[1]

1

1500

𝑄𝑢 = 𝛾𝐻𝑏 𝐴𝑏 𝑁𝑞 + 𝛾𝐷𝐴𝑏 𝑁𝛾 + 𝜋𝑑𝐿𝜎𝑣 𝐾𝑠 𝑡𝑎𝑛𝛿 + 𝛾𝐻𝑡 𝐴𝑡 𝑁𝑞 + 2

2

[2]

𝛾𝐷𝐴𝑡 𝑁𝛾

where Hb, Ht, E, and L stand for the depth to bottom helix, depth to top helix, lower helix embedment, and total length of pile, respectively; σv is the overburden stress; Ks states the coefficient of lateral earth pressure in compression loading; δ refers to the interface friction angle between soil and pile material; As, Ab, and At indicates the surface area of the smooth, bottom helix, and top helix of the piles, respectively and Nq, Nγ are the dimensionless bearing capacity factors for the local shear condition and N t refers to toe bearing capacity factor of the smooth pile (refer to CFEM 2006). Figure 8 shows the plot for the estimated ultimate capacity from CFEM (2006) to the measured post-cyclic ultimate capacity from centrifuge test results for the model piles (as in Figure 7). Further, measured values from non-cyclic load tests (presented by Islam et al. 2022) were shown. It can be seen that; the post-cyclic capacity is reasonably lower compared to the monotonic loading sequences only. In contrast, the methods in the current guideline might lead to overestimating the piles' capacity subjected to cyclic loading. The coefficient of determination (R2) changes from 0.896 to 0.834 from a monotonic only to a post-cyclic monotonic loading stage. Figure 9a shows the axial ultimate compressive load distribution along the depth in a pile from the SGs. The capacity of the piles with helices was comparatively higher compared to smooth piles. With the increase in depth, the ultimate load decreased.

Post-Cyclic

Depth in pile (m)

50

100

150

R2= 0.896

E P W In S R P R A

750 500 250 0

P0 M P2 M P4 M

0

250

500

P0 C P2 C P4 C

750

P1 M P3 M

P1 C P3 C

1000 1250 1500

Measured Qu (kN)

Figure 8. Comparison of Measured and Estimated Ultimate Axial Capacities from Monotonic and Post-Cyclic Compression test. The ultimate unit shaft resistance (qsU) on the smooth portions of the pile segment between two adjacent SGs was plotted along the pile depth in Fig. 9b. to study the nature of shaft resistance after loading. The unit shaft resistance (qs) was calculated as follows:

𝑞𝑠 =

𝑄𝑡𝑜𝑝 −𝑄𝑏𝑜𝑡

[3]

𝐴𝑠

where Qtop and Qbot are the measured axial loads at the top and bottom of a definite shaft segment, and As is the outer surface area of the segment. With the addition of helices, the ultimate unit shaft resistance increased over the length of the pile. qsU (kPa)

200

250

0

0.0

0.0

0.5

0.5

1.0

1.0

1.5

1.5

2.0

2.0

2.5

2.5

3.0

Monotonic

1000

Qult (kN) 0

R2= 0.834

1250

Estimated Qu (kN)

1

Eq Pl W In S R Pe R A

(a)

3.0

500

2000

2500 P0 C P1 C P2 C P3 C P4 C

(b)

Figure 9. (a) Axial Ultimate Load and (b) Ultimate Unit Shaft Resistance distribution along the pile length. 4

CONCLUSIONS

The article overviews the axial cyclic behavior of instrumented smooth and helix piles through centrifuge model tests at a 20 g scale to simulate the axial cyclic operational/ seismic loading nature. The study also aims to provide the internal load transfer within the pile shaft with

increased depths. The following major conclusions can be drawn: 1. The implementation of the sequence of cyclic load causes a reduction of load bearing capacity of the piles. With increased intensity, a possible gap at the base or around the helices occurs due to the loading sequence at the previous step.

2. Along the pile depth, the maximum axial cyclic load changes; the highest load occurs at the first loop of the cycle and later degrades with subsequent cycles at a particular load intensity. 3. Comparing a pseudo-static monotonic and a post-cyclic monotonic stage with the estimated theoretical capacity shows a significant difference. Even though the monotonic loading at the non-cyclic stage was comparable; however, the theoretical equations over predicts the capacity measured at the post-cyclic loading stages. 5

ACKNOWLEDGEMENTS

This project is funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and Reaction Piling Inc. The authors appreciate the experimental assistance of Yazhao Wang and Dmytro Pantov from the University of Alberta. The Canadian Foundation for Innovation supported the development of the Centrifuge (GeoCERF) facility. REFERENCES Al-Baghdadi, T.A., Brown, M.J. and Knappett, J.A. 2016. Development of an inflight centrifuge screw pile installation and loading system. In 3rd European Conf. on Physical Modelling in Geotechnics. 239–244. ASTM D 1143/D 1143M-20. Standard Test Methods for Deep Foundation Element Under Static Axial Compressive Load. West Conshohocken, VA. Brown, M., Davidson, C., Brennan, A., Knappett, J., Cerfontaine, B., and Sharif, Y. 2019. Physical modeling of screw piles for offshore wind energy foundations. In 1st International Symposium on Screw Piles for Energy Applications. CFEM. 2006. Canadian Foundation Engineering Manual. 4th ed. Canadian Geotechnical Society, BiTech Publisher Ltd., Canada. El Naggar, M.H., and Abdelghany, Y. 2007.Helical screw piles (HSP) capacity for axial cyclic loadings in cohesive soils. Proceedings of the 4th international conference on earthquake Geotechnical engineering, ThessalonikiGreece, June, 25–8. Elsherbiny, Z.H., and El Naggar, M.H. 2013. Axial compressive capacity of helical piles from field tests and numerical study. Canadian Geotechnical. Journal, 50(12):1191-1203. Ghaly, A. and Hanna, A. 1991. Experimental and theoretical studies on installation torque of screw anchors. Canadian Geotech. Journal, 28 (3): 353–364 Islam, N., Deng, L., Chalaturnyk, R., Penner, L. 2022. Centrifuge modeling technique for axial compressive loading on helical piles in sand. The 75th Canadian Geotechnical Conference-GeoCalgary2022, Calgary, Alberta, Canada, Oct 2-5 Jardine, R. J., and Standing, J. R. 2012. Field axial cyclic loading experiments on piles driven in sand. Soils and Foundations, 52(4):723–36.

Khidri, M., and Deng, L. 2022. Field axial loading tests of screw micropiles in sand. Canadian Geotechnical Journal, 59(3): 458-472. Levesque, C.L., Wheaton, D.E. and Valsangkar, A.J. 2003. Centrifuge Modeling of Helical Anchors in Sand, Proceedings of the 12th Panamerican Conf. on Soil Mechanics and Foundation Engineering, 2: 1859-1863. Li, W., Deng, L. and Chalaturnyk, R. 2022. Centrifuge modeling of the behavior of helical piles in cohesive soils from installation and axial loading. Soils and Foundations, 62(3):101141. Mitsch, M.P., Clemence, S.P., 1985. The uplift capacity of helix anchors in sand: Uplift behavior of anchor foundations in soil. In Proc. ASCE, New York, pp. 26–47 Perko, H.A. 2009. Helical piles: a practical guide to design and installation. John Wiley & Sons. Petereit, R. 1987. The static and cyclic pullout behavior of plate anchors in fine saturated sand. MSc Thesis, Oregon State University, Corvallis, U S A. Sakr, M. 2011. Installation and performance characteristics of high capacity helical piles in cohesionless soils. DFI Journal-The Journal of the Deep Foundations Institute, 5(1): 39–57. Schiavon, J. A., Tsuha, C. D. H. C., and Thorel, L. 2016. Scale effect in centrifuge tests of helical anchors in sand, International Journal of Physical Modeling in Geotechnics, 16(4):185-196. Schiavon, J. A., Tsuha, C. D. H. C., Neel, A., and Thorel, L. 2019. Centrifuge modeling of a helical anchor under different cyclic loading conditions in sand. International Journal of Physical Modelling in Geotechnics, 19(2):7288. Tsuha, C. D. H. C., Aoki, N., Rault, G., Thorel, L., and Garnier, J. 2012. Evaluation of the efficiencies of helical anchor plates in sand by centrifuge model tests, Canadian Geotechnical Journal, 49(9):1102-1114. Urabe, K., Tokimatsu, K., Suzuki, H., and Asaka, Y. 2015. Bearing capacity and pullout resistance of wing piles during cyclic vertical loading. In Proceedings of the 6th International Conference on Earthquake Geotechnical Engineering, 358-367. Wang, D., Merifield, R.S., and Gaudin, C. 2013. Uplift behavior of helical anchors in clay. Canadian Geotechnical Journal, 50(6): 575–584.

Bi-Directional Static Load Test for Caissons in Georgian Bay Shale: A Case Study in Greater Toronto Area, Ontario Madison Kennedy, Joe Carvalho & Lisa Coyne WSP Canada Inc., Mississauga, Ontario, Canada Tony J. Sangiuliano & Minkyung Kwak Ministry of Transportation Ontario, Toronto, Ontario, Canada ABSTRACT Rock-socketed drilled shaft foundations for Ministry of Transportation Ontario (MTO) projects have traditionally involved relatively long sockets relying on a significant proportion of sidewall resistance, which may not take full advantage of base resistance. A shorter, more efficient rock socket was designed for bridge piers in weak to medium strong Georgian Bay shale as part of the twinning of QEW/Credit River bridge in Mississauga, Ontario. An axial bi-directional static load test, also called an Osterberg cell or “O-cell” test, was completed to measure the ultimate sidewall and base geotechnical resistances of the rock socket on the test shaft, for comparison to and verification of the factored geotechnical resistance used in the design of the production drilled shafts. These results expand the knowledge base for caisson foundation design in the Georgian Bay rock formation, and demonstrate the effectiveness of SQUID, CSL and TIP in interpreting O-cell test results and in quality control during construction. RÉSUMÉ Les fondations de puits forés dans la roche pour les projets du Ministère des Transports de l'Ontario (MTO) ont traditionnellement impliqué des socles relativement longs reposant sur une proportion significative de la résistance des parois latérales, ce qui ne prendre pas compte de la résistance de la base. Un caisson plus court et plus efficace a été conçu pour les piliers de pont dans les schistes argileux de la Baie Georgienne de résistance faible à moyenne dans le cadre de l'élargissement du pont QEW/Credit River à Mississauga, en Ontario. Un essai de charge statique bidirectionnelle axiale a été réalisé pour mesurer les résistances géotechniques ultimes de la paroi latérale et de la base du fondation, à des fins de comparaison et de vérification de la résistance géotechnique utilisée dans la conception. Ces résultats élargissent la base de connaissances pour la conception des fondations de caissons dans cette formation rocheuse, et démontrent l'efficacité du SQUID, du CSL et du TIP dans l'interprétation des résultats des essais et dans le contrôle de la qualité pendant la construction. 1

INTRODUCTION

A twin bridge has been designed and is under construction to carry the Queen Elizabeth Way (QEW) over the Credit River valley in Mississauga, Ontario, as part of a publicprivate partnership project administered by Infrastructure Ontario (IO) and the Ministry of Transportation (MTO). The original Reference Concept Design in bid stage incorporated twelve 1.8 m diameter drilled shafts (locally termed caissons) at each pier, with rock sockets extending approximately 8 m to 10.5 m into the Georgian Bay shale bedrock. The pier foundation design developed in the bid and execution stages incorporated ten 1.5 m diameter caissons, with 2 m to 2.5 m long rock sockets. The approach to geotechnical design and the results of bidirectional static load testing are presented in the following sections. 2

SITE DESCRIPTION – EXISTING AND NEW BRIDGE

The QEW/Credit River crossing connects Toronto, Mississauga and the Niagara Peninsula. The Credit River valley is approximately 20 m deep at the site and incised into shale bedrock, with valley slopes oriented at

approximately 1.7H:1V and 3.5H:1V on the west and east sides of the river respectively. The existing bridge is an approximately 256 m long and 29 m wide, seven-span spandrel arch structure, with concrete arches at the piers. It was constructed as a fourlane bridge in 1934 and was widened to six lanes in 1960. The twin bridge is being constructed immediately to the north of the existing QEW Credit River bridge with a separation distance of ±2.0 m between the old and new structures. The new bridge consists of a three-span structure with caisson-supported piers located in the floodplain on each side of the Credit River, and abutment footings founded on bedrock near the crests of the valley. 3

SITE INVESTIGATION AND SUBSURFACE CONDITIONS

A series of geotechnical investigations were carried out at this site to determine the subsurface conditions and obtain the data required for the reference concept design included in the Design Build package. The geotechnical investigation consisted of geotechnical boreholes including in situ tests such as optical and acoustic televiewer to confirm the bedrock condition and packer testing to

determine the hydraulic conductivity of the rock. Figure 1 shows the borehole locations and generalized stratigraphy.

this structure (CHBDC, 2019). For the design based on analysis, geotechnical resistance factors of 0.4 and 0.8 were applied for the ultimate limit state (ULS) and serviceability state (SLS), respectively. 4.1

Calculation Methods

The geotechnical resistance of the caissons was estimated based on a combination of resistance from the base of the drilled shaft, and from sidewall resistance along the potion of the shaft imbedded in competent bedrock. 4.1.1

Ultimate Geotechnical Resistance – Drilled Shaft Base

The ultimate geotechnical resistance for the drilled shaft based was evaluated using the Hoek Brown strength model. The rock mass strength parameters were based on the interpretation of the laboratory test results and an estimation of the rock mass rating (RMR) based on the expected mode of failure of the rock mass. The relationships between the intact strength and the field strength as a function of Geological Strength Index (GSI, or RMR′76) are given in Equation 1 to 3 (Hoek et al. 2002).

𝑚𝑏 = 𝑚𝑖 × 𝑒 Figure 1. Borehole location and soil strata at the QEW/ Credit River Bridge pier locations In general, the Credit River valley has been incised into the shale bedrock with varying thicknesses and composition/consistency of soil at each of the proposed foundation elements. At the west pier, the subsurface conditions within the floodplain consist of fill and clayey silt to silty clay with organics underlain by sand and gravel and clayey silt (residual soil). At the east pier, the subsurface conditions consist of fill underlain by clayey silt or silty sand, organic soils, and silty sand containing organics and clayey silt pockets. Shale bedrock underlies these soils at depths of approximately 6 m and 7 m below floodplain level at the west and east piers, respectively. The bedrock consists of thinly laminated to mediumbedded grey shale of the Georgian Bay Formation, the upper portion of which is moderately to slightly weathered, becoming slightly weathered to fresh with depth. The shale is very weak to weak with an average unconfined compressive strength of 15 MPa, and it contains stronger limestone layers. 4

ANALYSIS BASED FOUNDATION DESIGN

The foundation design was initially carried out based on analysis and was subsequently confirmed with in situ testing. The QEW/Credit River bridge is considered a lifeline structure, and as such a consequence factor of Ψ = 0.9 was used based on a high consequence level for

𝑠=𝑒

𝑎=

(

(

GSI−100 ) 28−14𝐷

GSI−100 ) 9−3𝐷

1 1 −GSI⁄15 + (𝑒 − 𝑒 −20⁄3 ) 2 6

[1]

[2]

[3]

where 𝑚𝑖 is the intact rock Hoek-Brown m parameter; GSI is the geological strength index; and D is the disturbance factor. The selection of the Hoek Brown parameters is further discussed below. The geotechnical resistance calculation was based on a two-wedge failure, below and adjacent to the foundation, as shown in Figure 2. In the case of a caisson the rock below and adjacent to the caisson base is confined by the rock and overburden surrounding it and, therefore, can be considered to be in a triaxial stress state. The ultimate geotechnical resistance of the caisson base was calculated using Equation 4 (Hoek et al. 2002). 𝑎 𝑚 ∙ 𝜎3𝐴 𝑞𝑢𝑙𝑡 = 𝐶𝑓1 [𝜎3𝐴 + 𝜎𝑐𝑖 ( + 𝑠) ] 𝜎𝑐𝑖

[4]

where, 𝜎3𝐴 is given by Equation 5, 𝑎 𝑚 ∙ 𝑞𝑠 𝜎3𝐴 = 𝑞𝑠 + 𝜎𝑐𝑖 ( + 𝑠) 𝜎𝑐𝑖

[5]

carried out at each pier location based on the different loading conditions provided by the structural engineer to determine the settlement of the foundation. 4.2

Parameter Selection

The parameters required to complete the analysis of the geotechnical resistance were obtained from information and laboratory testing carried out during the previous site investigations which included core logging, unconfined compressive strength tests, point load tests and downhole optical televiewer logging. 4.2.1

Figure 2. Illustration of the model for the calculation of the caisson base ultimate geotechnical resistance (modified from Wyllie, 1999). where 𝑞𝑠 , is the vertical confining stress of the wedge adjacent to the caisson base and 𝐶𝑓1 is a shape factor which is 1.2 for a circular foundation shape (Sowers, 1970). 4.1.2

Ultimate Geotechnical Resistance – Drilled Shaft Side Wall

In practice, socket side resistance capacity is calculated by assuming a single average value of unit side resistance acting along the concrete-rock interface, for each rock layer. This value is multiplied by the surface area of the shaft to obtain a total side resistance, given by Equation 6 (CGS, 2006). 𝑄𝑠 = 𝑓𝑠𝑢 × 𝜋𝐷𝐿

[6]

The parameter 𝑓𝑠𝑢 was estimated using the relationship proposed by Horvath and Kenney (1979), Equation 7.

𝑓𝑠𝑢 = 𝑏√𝑞𝑢

[7]

which correlates the uniaxial compressive strength (𝑞𝑢 ) of the weaker material (rock or concrete) and the shaft roughness. For the caissons, a shaft roughness of R1 for straight, smooth-sided socket, grooves or indentations less than 1 mm deep (after Pells et al. 1980) was selected and a roughness value (b) of 0.45 was selected based on data summarized by Rowe and Armitage (1987). 4.1.3

Serviceability Geotechnical Resistance

The settlement of the pier foundations was estimated using the rock mass properties and the commercially available program RSPile by Rocscience Inc. A group analysis was

Rock Mass Quality (RMR76 / GSI)

To quantify the rock mass quality the Rock Mass Rating (RMR) estimates using Bieniawski’s 1976 ratings (typically 5 points less than the 1989 ratings) were used for estimates of strength. The 1976 rather than 1989 ratings have been used because the original relationships between rock mass Hoek-Brown parameters and intact Hoek-Brown parameters were based on the 1976 ratings. In the estimation of RMR, the spacing rating was based on the most adverse joint set, or the joint set of most significance to the failure mode. Due to the nature (orientation of bedding, typical frequency of non-subhorizontal joints) of the Georgian Bay Shale the joint spacing rating was evaluated in two different ways depending on the analysis being carried out. For the ultimate geotechnical resistance of the Georgian Bay Shale, the vertical and sub vertical crossjointing is more significant than bedding, as failure of the foundation across the bedding would require rupture across the intact rock. Vertical cross joints typically have spacing in the range of meters, and as such the sub vertical joint spacing was used to calculate the RMR 76 for geotechnical resistance. When considering serviceability geotechnical resistance, or settlement of the foundation, the bedding in the Georgian Bay Shale plays a major role in the stiffness of the rock mass because the load acts perpendicularly to it. In the vertical direction, the stiffness of the rock mass is mostly controlled by the compression of the bedding, especially if clay seams are logged in the rock core. Therefore, the rating for spacing in the calculation of RMR 76 for settlement purposes should be based on the bedding spacing, or its fracture frequency; also, the joint condition rating should consider the continuity and the roughness/infilling of the bedding. The RMR76 values obtained for the strength and modulus parameters at this stie are presented relative to elevation in Figure 3. 4.2.2

Unconfined Compressive Strength and Intact Modulus

The unconfined compressive strength and the elastic modulus of the intact rock was obtained from uniaxial compression tests instrumented with strain gauges, as well as correlations with Point Load Tests (PLT). The UCS from the laboratory testing and point load testing, as well as the

intact modulus results for the pier foundations are summarized in Figure 4.

bedding is consistently greater (RQD = 100) or smaller (RQD = 0) than 100 mm. Therefore, the spacing should be used directly in the estimation of the vertical modulus according to Equation 8 (Goodman, 1989). 1 1 1 = + 𝐸𝑛 𝐸𝑖 𝑘𝑛 𝑠

[8]

Where En is the normal modulus, where stress is applied normal to the joints, Ei is the intact modulus of the rock, kn is the stiffness of the contact, and s is the joint spacing. The spacing is readily available from drillhole records; however, the stiffness of the bedding will depend on how tight the bedding is, or whether there are softer clay seams and how thick they are. The best way to assess the tightness of the bedding is using optical televiewer images, where available. In a horizontally layered rock mass, the lateral (horizontal) modulus is mostly controlled by the intact rock strength and not affected by the bedding. If vertical or subvertical jointing is widely spaced, the lateral modulus of the rock mass should approach intact values. Figure 3. Rock Mass Rating vs. Elevation

4.2.4

Hoek-Brown Parameter mi

The estimation of the intact Hoek-Brown parameter 𝑚𝑖 is typically obtained from curve fitting laboratory results of tensile, uniaxial and triaxial testing. In the absence of triaxial testing, suggested values by Hoek and Brown (reported in Hoek and Marinos, 2000) can be used, or simple relationships between tensile strength and uniaxial compressive strengths can be used as reasonable estimates. The suggested values for 𝑚𝑖 for shales by Hoek and Brown (Hoek and Marinos, 2000), when no other data is available, are 6±2. While no direct tensile or Brazilian tests were undertaken, a large number of axial and diametral Point Load Tests (PLT) were done on the recovered rock core samples. The ISRM suggested method for determining point load strength suggests that the 𝐼𝑠(50) value is approximately 0.8 times the Brazilian tensile strength (ISRM 1985). Given an average UCS of 15 MPa and an average tensile strength of 0.72 MPa, the 𝑚𝑖 is estimated to be 17.4 using the more conservative Hoek and Martin approach. Experience in the Georgian Bay shale suggests that values of 𝑚𝑖 may be lower. Therefore, a value of 8 representing the higher end of the range suggested by Hoek and Brown was adopted for the design. Figure 4. Unconfined Compressive Strength vs Elevation 4.2.3

Rock Mass Modulus

To obtain the rock mass modulus the RMR can be used to account for the discontinuities of the rock mass. However, using RMR for calculating the vertical modulus (stiffness) of the rock mass can be inaccurate, especially if the

4.3

Analysis-Based Results

The factored ultimate geotechnical resistance at ultimate limit state was calculated to be 10,880 kPa for the base resistance and 630 kPa for the sidewall resistance. The design value for the factored ultimate geotechnical resistance based on the analysis was 17,500 kN and 19,000 kN for the approximately 2.0 m and 2.5 m minimum rock socketed lengths, respectively. The factored

serviceability geotechnical resistance for 10 mm of settlement was 17,500 kN and 18,500 kN for the 2.0 m and 2.5 m rock socket length, respectively. AXIAL BI-DIRECTIONAL STATIC LOAD TEST

An axial bi-directional static load test (BDSLT, also referred to as an Osterberg cell or “O-cell” test) was completed on a test shaft that was drilled near the west pier of the Twin Bridge. The purpose of the BDSLT was to measure the ultimate base and sidewall geotechnical resistances of the rock socket, for comparison to and verification of the factored geotechnical resistances used in the design of the production drilled shafts for the piers. 5.1

GROUND SURFACE

1.06 m

~76.06 m

1.44 m

TOP OF CONCRETE

74. 62 m

TELLTALE S.G. 73.94 m

Test Methodology

The test shaft was constructed to match the production drilled shafts and included a permanent steel casing with a 1524 mm outside diameter and 1500 mm inside diameter, with a 1500 mm diameter rock socket. The BDSLT was conducted using a 27 MN-rated Osterberg cell, having a diameter of 860 mm and using a steel base plate of 940 mm diameter in the “Chicago method” of testing, as described in FHWA Publication No. FHWA-NHI-10-016 (Drilled Shafts: Construction Procedures and LRFD Design Methods). The nominal 1500 mm diameter rock socket was designed with a minimum 3.0 m rock socket length in the slightly weathered to fresh bedrock, plus an additional drilling depth of about 0.6 m to accommodate the O-cell assembly (load cell and steel plates, plus concrete levelling layer). Based on the results from Golder’s boreholes within the west pier footprint, the rock socket was designed with its base at Elevation 64.5 m and top at about Elevation 68.2 m; the permanent casing was required to extend a minimum of 0.5 m into the weathered bedrock, and not to extend below Elevation 68.2 m. A profile illustrating the test setup is provided in Figure 5. Inspection of the test shaft construction was completed as follows: SONICaliper measurement of the rock socket to confirm the diameter and integrity; rock socket base inspection via SID (shaft inspection device) and SQUID (Shaft Quantitative Inspection Device) to confirm base cleaning procedures; and crosshole sonic logging (CSL) and thermal integrity profiling (TIP) to assess concrete integrity. The axial bi-directional static load test was conducted in general accordance with ASTM 8169-18 (Standard Test Method for Deep Foundations Under Bi-Directional Static Axial Compressive Loading) using Procedure A: Quick Test loading schedule, until termination of the test upon reaching the ultimate loading condition. The test progressed with the Osterberg cell assembly pressurized in ten nominally equal increments, resulting in a maximum bi-directional load of 13.37 MN applied to the shaft above and below the Osterberg cell. Each successive load increment was held constant for eight minutes by manually adjusting the Osterberg cell pressure, followed by an approximately one-minute period to increase to the next loading increment. The loading was halted after increment

77. 12 m

AS-BUILT

3.8 m

5

1L-10 because the ultimate capacity had been reached on the sidewall of the rock socket above the Osterberg cell assembly, and higher loads could not be sustained. The test shaft was then unloaded in five decrements.

1525 mm O.D. CASING

72.26 m (May 13, 2021– 8:05AM)

OVERBURDEN S.G. 71.44 m 70. 5 m

RIVER STONE

~69.46 m

EL. 68.86 m

WEATHERED ROCK

68.15 m

S.G. 68.44 m S.G. 67.94 m

SLIGHTLY WEATHERED TO FRESH ROCK

S.G. 66.94 m

~3.0 m

S.G. 65.94 m

65.01m 0.505 m

64.5 m 0m

1m

2m

3m

Figure 5. Profile of the test shaft showing elevations of relevant components Throughout the test, the top of shaft displacement was monitored using a pair of automated digital survey levels from an average distance of approximately 16 m. The upper compression displacement was measured using 6 mm telltale rods positioned inside casings embedded within the test shaft and monitored by linear vibrating wire displacement transducers (LVDTs) attached to the top of the shaft. The Osterberg cell expansion was measured using LVDTs. All instrumentation was monitored using a datalogger collecting data at 30 second intervals. 5.2

BDSLT Results

The instrumentation installed in the test shaft allowed for the determination of the based strength and sidewall shear strength (or mobilized stresses) if the failure load is not reached). The stress strain relationship (stiffness) of the base of the base and sidewall was also obtained. These parameters are important for the assessment of the design capacity of the production drilled shafts, the settlement, and the load sharing between the sidewall and the base. The results of the BDSLT on the test shaft are shown in Figure 6.

zones above and below showed a consistent response and were used to obtain the sidewall shear strength for the moderately weathered to fresh bedrock. Similarly, the sidewall shear strength for the weathered shale zone was obtained from instruments bounding the weathered zone of the rock.

Figure 6. O-Cell load-displacement – gross loaddisplacement graph for the test shaft base and sidewall 5.2.1

Sidewall Resistance

The ultimate sidewall capacity of the test shaft was calculated using Equation 9. 𝑃 𝜋𝐷 × 𝐿

[9]

where P is the maximum applied load before significant upward movement, D is the shaft diameter and L is the rock socket length. The diameter of the test shaft was 1.5 m and the length of the test socket consisted of approximately 0.7 m and 3.15 m of weathered and fresh rock, respectively, for a total length of 3.85 m. The test shaft was loaded to 13,400 kN before significant upward movement of the test shaft socket was observed without increase in the load. The resulting shear strength profile is shown in Figure 7. Based on this result, an average ultimate sidewall capacity of 740 kPa was obtained. However, for this test pile the average ultimate sidewall capacity is not considered appropriate for assessment of the ultimate capacity of the test shaft and production rock sockets due to defects observed in the lower section of the test shaft (above the O-Cell). The lower average ultimate side friction capacity of the test shaft socket in the Osterberg cell test is attributed primarily to an anomaly indicative of a reduced concrete cover in the lowest zone of the test shaft socket which was observed in both the CSL and TIP testing, discussed in further detail below. For design purposes based on the results of the bidirectional static load test, the instrumented zone of the moderately weathered to fresh rock socket where the anomaly was not observed and all the strain gauges in the

Figure 7. Shear strength profile - socket shear strength profile and net load vs displacement during O-cell test loading The following ultimate (unfactored) sidewall capacities were obtained from the bi-directional static load test: • Moderately weathered to fresh shale: 1,200 kPa • Weathered shale: 600 kPa 5.2.2

Base Resistance

When significant upward movement of the test shaft socket occurred (P = 13,400 kN), the axial bi-directional static load test had not reached the ultimate resistance at the base. While the ultimate base resistance was not fully mobilized, the test proved a base resistance of at least 19,300 kPa over the loaded area (base plate of 940 mm diameter) using the Chicago method. The resulting mobilized base strength versus displacement is shown in Figure 8. 5.2.3

Inspection and Quality Assurance

concrete cover in this zone which was deemed acceptable for the O-Cell test. The anomaly from 8.8 m to 9.4 m corresponded with lower sidewall shear strength measurements as discussed previously. 6

Figure 8. Mobilized base strength vs displacement for 0.94 m diameter base plate in Chicago method, and adjusted for 1.5 m diameter base plate The results of the inspection or quality assurance testing aided in the interpretation of the results as indicated in earlier sections. The SONICaliper results indicate that the calipered diameter of the rock socket above the Osterberg cell assembly was 1500 mm, in accordance with the design for the test and production shafts. Based on observations from the downhole camera video, the shale bedrock in the rock socket sidewalls was observed to be of good to excellent quality from the bottom of the casing to the water level (which was drawn down to as low as 1.4 m above the base of the shaft). The SID camera inspection of the socket base showed up to 10 mm to 20 mm of sediment in the west quadrant of the socket, and additional cleaning of the base was completed prior to SQUID inspection. Based on the SQUID test results, average thickness of sediments of less than 8 mm, at least 50% of the base having less than 8 mm of sediment was achieved, and the maximum thickness of sediment at any place on the base did not exceeding 15 mm. These measurements met the criteria specified for the production drilled shafts on this project. The CSL and TIP results were consistent with each other and demonstrated no major anomalies in the test shaft, although minor anomalies were noted from top of concrete to a depth of 0.3 m interpreted by to be due to the concrete not being fully cured at the time of CSL testing which was considered acceptable for the O-cell test as the test shaft will not be subject to top-down loading. A minor anomaly was also noted from a depth of 8.8 m to 9.4 m which is just above the top plate of the O-cell assembly which the result of the CSL and TIP indicative a reduced

DRILLED SHAFT CAPACITIES BASED ON STATIC TEST RESULTS

Based on the results of the BDSLT, geotechnical resistance factors of 0.6 and 0.9 were applied for ULS and SLS, respectively, and a consequence factor of Ψ = 0.9 was applied. The results derived from the axial bi-directional static load test were directly applicable to the design of the production drilled shafts. The sidewall shear strength obtained provided appropriate design values for the moderately weathered to fresh shale in the rock socket, and for the weathered shale above the top of the rock socket and below the permanent liner. Moreover, based on the borehole results and geotechnical laboratory testing from boreholes drilled within the pier footprints, the average unconfined compressive strength and modulus was considered to be consistent at and below the production drilled shaft founding level. The results obtained from the bi-directional static load test results were used to create load deformation plots and loading profiles for a single drilled shaft using RSPile (by Rocscience Inc.). For the analysis the caisson was divided into 50 elements. For estimating the serviceability geotechnical resistance for 10 mm of settlement the analysis included the compression of the caisson as well as the geotechnical settlement. The estimated unfactored and factored ULS and SLS for the 1.5 m diameter caisson with 2.0 m and 2.5 m long rock sockets are summarized in Table 1. Table 1. Estimated Geotechnical Resistance Values from Static Load Test Minimum Rock Socket Length:

2.0 m

2.5 m

ULS – Unfactored

50,000 kN

53,000 kN

ULS – Factored

27,000 kN

28,600 kN

SLS – Unfactored

26,800 kN

28,060 kN

SLS – Factored

21,700 kN

22,700 kN

For 2.0 m and 2.5 m long rock sockets the results of the analysis indicated that at the early stages of loading, the sidewall and the base share the load approximately equally; however, as the load increases beyond approximately 20,000 kN, the sidewalls behave in a more plastic manner and the contribution of the base to the capacity becomes the major component. 7

COMPARISON OF ANALYSIS AND STATIC TEST DESIGN

The factored geotechnical resistances obtained from the axial bi-directional static load test were slightly higher than those obtained from the static analyses completed in the bid and execution stage design, which were in turn significantly higher than those used in the Reference Concept Design for the QEW/Credit River Twin Bridge. The resulting unfactored geotechnical resistances are summarized in Table 2 and show that the design values from analysis and static load test methods produced similar results; the corresponding factored ULS and SLS values are also summarized below, noting the increased resistance factors employed for BDSLT.

Bridge Foundation Report), and 30M12-513 (Bi-Directional Static Load Test – Credit River Twin Bridge).

Table 2. Summary of the Geotechnical Resistances Estimated from the Analysis and BDSLT Methods

ASTM. 2018. ASTM 8169-18 Standard Test Method for Deep Foundations Under Bi-Directional Static Axial Compressive Loading Bieniawski, Z.T. 1976. Rock mass classification in rock engineering. Exploration for rock engineering, proc. of the symp., (ed. Z.T. Bieniawski), Cape Town: Balkema 1: 97-106. Canadian Geotechnical Society (CGS). 2006. Canadian Foundation Engineering Manual, fourth edition (4th Ed.) Canadian Standards Association, 2019. Canadian Highway Bridge Design Code (CHBDC) and Commentary on CAN/CSA-S6-19. CSA Group. FHWA. 2010. FHWA Publication No. FHWA-NHI-10-016 Drilled Shafts: Construction Procedures and LRFD Design Methods Golder Associates Ltd. 2021. Summary Report: Bidirectional static load test – Credit River Twin Bridge, QEW/Credit River Improvement Project, Mississauga, Ontario, Infrastructure Ontario RFP No. 19-110. Report No. QEWCR-SUB-GEN-GEO-QEWCR-SUBGEN-GEO-RPT-002-R1 O-Cell Test (19122889D) Goodman, R. E. 1989. Introduction to Rock Mechanics (2nd Ed.). John Wiley & Sons. Hoek, E., Carranza-Torres, C.T., and Corkum, B., 2002. Hoek-Brown failure criterion – 2002 Edition. 5th North American Rock Mechanics Symp., Toronto, Canada. 1: 267-273. Horvath, R.G. and Kenney, T.C. 1979. Shaft Resistance of Rock Socketed Drilled Piers. Symposium on Deep Foundations, ASCE, New York, N.Y. 1: 182–214. International Society of Rock Mechanics (ISRM). 1985. Suggested method for determining point load strength. Elsevier BV.In Int. J. of Rock Mech. Min. Sci. & Geomech. Abstr. 22, (2): 51–60. Marinos, P., and Hoek, E. 2000. GSI: a geologically friendly tool for rock mass strength estimation. ISRM international symposium. OnePetro. Pells, P.J.N., Rowe, R.K. and Turner, R.M. 1980. An experimental investigation into side shear for socketed piles in sandstone. Int. Conf. Structural Foundations on Rock, Sydney, Balkema. Rowe, R.K. and H.H. Armitage. 1987. A Design Method for Drilled Piers in Soft Rock, Canadian Geotechnical Journal. 24: 126–142. Sowers, G. F. 1970. Introductory Soil Mechanics and Foundations. Macmillan New York, pp. 395–6 Wyllie, D. C. 1999. Foundations on rock: Engineering practice, second edition (2nd ed.). Spon Press.

Maximum Rock Socket Length

Analysis

BDLST

Unfactored ULS (kN) 2.0 m 2.5 m 2.0 m 2.5 m

48,610 52,780

50,000 53,000

Analysis

BDSLT

Factored ULS (kN)

17,500 19,000

27,000 28,600

Unfactored SLS (kN)

Factored SLS (kN)

24,305 25,695

17,500 18,500

26,800 28,060

21,700 22,700

The unit factored ultimate axial resistances (per square metre) for base and sidewall are summarized in Table 3. Table 3. Factored Axial Geotechnical Resistances at ULS Calculation Method

8

Base (kPa)

Sidewall (kPa)

Analysis

10,880

630

BDLST

10,695

650

CONCLUSIONS

The results of the BDSLT validated the use of higher geotechnical resistances for shorter rock sockets in the Georgian Bay shale formation, and offers information for the design of more efficient rock sockets on other sites and contracts in this shale formation. SQUID testing allows assessment of the base cleaning procedures and confirmation that the target sediment thicknesses have been achieved, giving greater confidence to design with high load transfer to the drilled shaft base. Quality assurance testing (CSL and/or TIP) allows for both confirmation of the integrity of the drilled shafts as well as greater insight into interpretation of the O-cell test results if anomalies are observed in the drilled shaft, such as those seen in the test shaft. For additional information the foundation reports related to the QEW/Credit River Twin Bridge piers and BDSLT are available at https://foundation.mto.gov.on.ca/ under GEOCRES numbers 30M12-500 (QEW/Credit River Twin

ACKNOWLEDGEMENTS All team members involved in the design, installation, oversight, and reporting of the BDSLT are acknowledged. Special thanks are extended to Infrastructure Ontario, MTO Major Projects Office, lead designer AECOM, EDCO (EllisDon-Coco Group Joint Venture), LoadTest, and Subsurface Geotech Inc. REFERENCES

An Improved Approach for Frost Depth Evaluation Considering Unfrozen Water in Frozen Soil Greg Qu and Nam Pham WSP, Oakville, Ontario, Canada

ABSTRACT The frost depth is known to depend on the unfrozen water content in frozen soil. However, current engineering practice of frost depth calculation often ignores the unfrozen water and assumes that all water in the soil completely freezes below 0°C. Fine-grained soil can contain significant amounts of unfrozen water at freezing temperature, particularly for finegrained soil in the range of temperatures of practical importance for frost depth. The assumption of fully frozen water in soil often leads to a less conservative underestimation of the frost depth by about 15% to 30% for fine-grained soils. There is a need to develop a practical and industry-friendly approach for frost depth assessment, which takes account of the unfrozen water content in frozen soil. The work by Tice et al. (1976), Anderson and Ladanyi (2003) recommended a correlation between unfrozen water in soil and temperature with a good match with the test data. But this approach requires a non-standard laboratory test (liquid limit test with N=100), which limits its application in engineering practice for commercial projects. This paper proposed an improved approach, which simplifies the correlation and does not need any non-standard tests for the input parameters. It requires only two basic input soil parameters, total initial water content and liquid limit from routine Atterberg limit tests, both of which are usually available in most engineering projects. A sensitivity study was carried out to evaluate the proposed approach using laboratory tests of about 20 soils from the literature. The results shows that the proposed correlation matches well with the lab data and conventional approach and is considered suitable for practical engineering assessment of frost depth. RÉSUMÉ La profondeur de gel est connue pour dépendre de la teneur en eau non gelée dans le sol gelé. Cependant, la pratique technique actuelle du calcul de la profondeur du gel ignore souvent l'eau non gelée et suppose que toute l'eau du sol gèle complètement en dessous de 0 °C. Le sol à grains fins peut contenir des quantités importantes d'eau non gelée à la température de congélation, en particulier pour les sols à grains fins dans la plage de températures d'importance pratique pour la profondeur de gel. L'hypothèse d'eau entièrement gelée dans le sol conduit souvent à une sous-estimation moins prudente de la profondeur de gel d'environ 15% à 30% pour les sols à grains fins. Il est nécessaire de développer une approche pratique et adaptée à l'industrie pour l'évaluation de la profondeur du gel, qui tienne compte de la teneur en eau non gelée dans le sol gelé. Les travaux de Tice et al. (1976), Anderson et Ladanyi (2003) ont recommandé une corrélation entre l'eau non gelée dans le sol et la température avec une bonne correspondance avec les données d'essai. Mais cette approche nécessite un test de laboratoire non standard (test de limite de liquidité avec N = 100), ce qui limite son application dans la pratique de l'ingénierie pour les projets commerciaux. Cet article propose une approche améliorée, qui simplifie la corrélation et ne nécessite aucun test non standard pour les paramètres d'entrée. Il ne nécessite que deux paramètres de sol d'entrée de base, la teneur en eau initiale totale et la limite de liquidité des tests de limite d'Atterberg de routine, qui sont généralement disponibles dans la plupart des projets d'ingénierie. Une étude de sensibilité a été réalisée pour évaluer l'approche proposée à l'aide d'essais en laboratoire d'environ 20 sols de la littérature. Les résultats montrent que la corrélation proposée correspond bien aux données de laboratoire et à l'approche conventionnelle et est considérée comme appropriée pour l'évaluation technique pratique de la profondeur du gel.

1

INTRODUCITON

The current engineering practice to calculate frost depth often ignores the unfrozen water and assumes that all water in the soil completely freezes below 0°C (see Canadian Foundation Engineering Manual, 2006 and the guidelines by U.S. Army, 2012). The volumetric latent heat of fusion, as a key parameter for the frost depth (see the modified Berggren equation in CFEM 2006) is governed by the content of the water in soil, which can be frozen into ice. This assumption leads to underestimating the frost depth, particularly for fine-grained soils, where the water in

soil freezes over a wide range of temperatures from -20°C to 0°C. Frost action in the fine-grained soil can cause significant damage to the structure due to the form of ice lenses and consequent frost jacking. There is a need to develop a practical and industryfriendly approach for frost depth assessment, which takes account of the unfrozen water content in frozen soil, using the routine standard laboratory tests available for most engineering projects. The currently available approaches (Anderson and Tice 1972, Anderson and Morgenstern 1973, Tice et al. 1976, Anderson and Ladanyi 2003 and Hu et al. 2020) require inputs from non-standard laboratory

The studies have been carried out to quantify the unfrozen water content in frozen soil by researchers (Anderson and Tice 1972, Anderson and Morgenstern 1973, Aderson et al. 1973, Tice et al. 1976, and Anderson and Ladanyi 2003). The formula below was proposed by these researchers to represent the correlation between unfrozen water content with temperature, as shown in Figure 1a. One limitation of this correlation is that the 𝑤𝑢 approach to an infinitely high value which may even exceed the total water content as the temperature approaches to 0℃. where

𝑤𝑢 (𝑇) = 𝛼 × (−𝑇)𝛽 𝑇 is the temperature in Celsius degree (℃).

40%

Correlation proposed in this paper Correlation by Tice et al. 1976 Reference Points

35%

30% 25% 20% 15% 10% 5% 0%

-5

𝑋 = frost depth 𝐼𝑠 = surface freezing index 𝑘𝑓 = thermal conductivity of the frozen soil 𝐿𝑠 = volumetric latent heat of the soil 𝜆 = a dimensionless coefficient One key input for the frost depth calculation is the volumetric latent heat of fusion (𝐿𝑠 ) for soil, which is the amount of energy required to freeze the unfrozen water in soil to the frozen state. Therefore, the unfrozen water in frozen soil has a significant impact to the latent heat and consequently the frost depth. As shown in the formula below, the input of 𝑤𝑐 is usually assumed to be the total water content (CFEM 2006), ignoring the unfrozen water in frozen soil. 𝐿𝑠 = 𝛾𝑑 𝑤𝑐 𝐿 where 𝛾𝑑 = dry unit weight of soil 𝑤𝑐 = water content of soil which is assumed to be completely frozen into ice 𝐿= latent heat of fusion of water to ice, 334 kJ/kg

-4

where

-3

𝑋 = 𝜆√2𝑘𝑓 𝐼𝑠 ⁄𝐿𝑠

-2

The theoretical methodology for frost depth has been well established and the engineering practice usually adopts the modified Berggren equation, as recommended by CFEM (2006) and U.S. Army (2012).

-1

BACKGROUND

0

2

𝛼 = 𝑤𝑢1 represents the unfrozen water content in frozen soil at 𝑇 = −1℃. 𝛽 is a material parameter. Anderson and Tice (1972) proposed a correlation between the soil specific-area and the parameters of 𝑤𝑢1 and 𝛽. However, the lab test to determine specific area of soil is complicated and is often not available for most engineering projects. With further studies, the research team (Tice, Anderson and Banin 1976) later proposed a correlation using a liquid limit test and a non-standard liquid limit test to obtain the water content corresponding N=100 (where N is the number of blows required to close the standard groove in the liquid limit test). Although the correlation worked very well to match the lab test data, this correlation has not been widely used in practice, likely due to the following two factors: 1. It requires the input parameters of 𝐿𝐿𝑁=25 (Liquid Limit) and 𝐿𝐿𝑁=100 from non-standard lab tests (i.e., non-standard liquid limit test of N=100, N is the number of blows required to close the standard groove in the liquid-limit test). The nonstandard lab tests are usually not available in commercial engineering projects to support the assessment of frost depth using this correlation. 2. The 𝑤𝑢 approaches to an infinitely high value, as the temperature approaches 0℃. This may cause confusion and numerical issues in the analyses. There are some recent research studies to assess unfrozen water content in frozen soil, as summarized by Hu et al. (2020) and Anderson and Ladanyi (2003). However, these studies focused more on accurate estimation of unfrozen water content, which inevitably introduces more fitting parameters or required further advanced lab tests. The objective of this paper is to develop a practical correlation of unfrozen water content for engineers to calculate the frost depth using only standard lab tests (Atterberg limit test).

Unfrozen Water Content, %

tests (i.e., the specific surface areas of soil particles, liquid limit test with N=100, or nuclear magnetic resonance tests), which limits their application in practice. This paper presents an improved and practical approach to take into account the impact of unfrozen water in frost depth evaluation. This approach requires two input parameters, initial water content and liquid limit of soil, both of which are available from standard laboratory tests in most engineering projects. This approach intends to support a preliminary assessment of frost depth in engineering practice. For critical projects, design engineers should consider a field test (thermistor) or advanced laboratory tests, for example, nuclear magnetic resonance (NMR) test.

Temperature, degree C Figure 1a. Comparison of Correlation of 𝑤𝑢 versus T proposed in this paper and that by Tice et al. 1976. (𝑤𝑐 =22%, LL=40%, 𝛼= 𝑤𝑢1 =10.6%, 𝛽= - 0.25)

Lab Test Data (Tice et al. 1976)

3.

20% 10%

Correlation using Test Data in Literature

3

PROPOSED CORRELATION FOR UNFROZEN WATER CONTENT IN FROZEN SOIL

The following presents the correlation formula proposed to estimate the unfrozen water content in frozen soil. 𝑤𝑢 (𝑇) = {

𝑤𝑐 − (𝑤𝑐 − 𝑤𝑢1 ) × (−𝑇) 𝑓𝑜𝑟 𝑇 ∈ (0, −1℃) [1𝑎] 𝑤𝑢1 × (−𝑇)𝛽 𝑓𝑜𝑟 𝑇 < −1℃ [1𝑏] [1]

𝑤𝑢1 = 𝑤𝑢,𝑇=−1℃ = 0.35 × 𝐿𝐿 − 3%

[2]

where 𝑤𝑢1 is the unfrozen water content in frozen soil at 𝑇 = −1℃, which can be estimated using the equation [2] for preliminary evaluation or specific tests. 𝛽 is a material parameter with a default value of -0.25. Alternatively, this parameter can be obtained by fitting the data from laboratory tests. Section 4 provides further discussion for this parameter. Figure 2 illustrates the correlation from Eq. [1]. In general, the unfrozen water content (𝑤𝑢 ) decreases with the lower temperature. For temperature from 0 to -1°C, 𝑤𝑢 reduces linearly from the total water content, 𝑤𝑐 to 𝑤𝑢1 , see Eq. [1a]. It is noted that literature data suggests a correlation between 𝛼 and the limit liquid of soil. Below 1°C, the 𝑤𝑢 reduction is in a linear log-log correlation which is governed by the slope of the log-log plot, 𝛽, see Eq. [1b]. The following describes the main features of the proposed approach. 1. For the temperature range from 0 to −1℃, the proposed approach adopts a linear correlation of unfrozen water content from the total water content (𝑤𝑐 ) to 𝑤𝑢1 , as shown in Figure 1a. Eq. [1a] presents the mathematic formula. 2. This approach uses a default value of 𝛽 and eliminates the requirement for non-standard liquid limit tests, see Eq. [1b]. This simplification was

4

PARAMETER OF 𝛽

For the 20 soils listed in Table 1b, the soil parameter 𝛽 varies typically from - 0.15 to - 0.40, with an average of about - 0.25. Figure 2 shows the upper and lower bounds of the curve with 𝛽= - 0.15 and - 0.4, respectively. Given the narrow range of the soil parameter 𝛽, this paper proposed to use the default value of 𝛽 = -0.25. Section 6 presents the results of sensitivity analyses of 𝛽 for the impact to frost depth within the range from -0.15 to - 0.4. It is shown that the influence of 𝛽 is insignificant for this range, less than 5% of frost depth.

30% Eq. for T > -1°C 25%

Eq. for T < -1°C

𝑤𝑐

20%

Reference Points Upper Bound

15%

Lower Bound

𝑤𝑢1

10% 5% 0%

-20

Figure 1b. Comparison of Correlations of Liquid Limit and 𝑤𝑢1 proposed in this paper and that by Tice et al. 1976.

Table 3 summarizes the calibrated 𝑤𝑢1 and 𝛽 for about 20 different soils from the literature data. Figure 3a shows the calibrated fitting curves using Eq. [1] match reasonably well with the test data from Anderson and Morgenstern (1973). In Figure 3b, the unfrozen water content is normalized with the value at -1°C. It shows the test data falls in a relatively narrow range. The values of 𝛽 for these four soils vary from – 0.24 to - 0.6 correspondingly.

0

Liquid Limit (LL), %

100%

80%

60%

40%

20%

0%

0%

-15

30%

Linear (Lab Test Data (Tice et al. 1976))

-10

Correlation from Eq. [2]

considered to have negligible impact on the frost depth calculation, see the assessment in Section 4 for details. The proposed approach agrees well with the original one proposed by Tice et al. (1976) regarding the correlation between liquid limit and the unfrozen water content in frozen soil at 𝑇 = −1℃. Figure 1b shows a comparison with the test data of eight soils indicates, indicating both correlations match well with the data. Table 1a listed the parameters for the eight soils in the assessment.

-5

40%

Unfrozen Water Content, Wu

Unfrozen Water Content at -1 C deg, Wu1

50%

Temperature, degree C

Figure 2. Illustration of Correlation (Equation [1]) Between Unfrozen Water Content in Frozen Soil and Temperature. (Notes: 𝑤𝑐 =22%, LL=40%, 𝛼 = 𝑤𝑢1 =10.6%, 𝛽 = -0.25 for base-case, 𝛽= - 0.4 for lower bound and 𝛽= -0.15 for upper bound)

0.0

Assuming fully frozen water β= -0.25 (Default) β= -0.15 β= -0.4

Temperature, -T No.2 Hawaiian Clay No.4 Dow Field Silty Clay

No.1 Umiat Bentonite No. 3 Suffield Silty Clay

Frost Depth, m

10

9

8

7

6

5

4

3

2

1

0.5

0

Unfrozen Water Content,%

100 90 80 70 60 50 40 30 20 10 0

Figure 3a. Correlation for the test data from Anderson and Morgenstern (1973).

1.0

1.5

2.0

2.0 1.8 1.6 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0

Site 1

Site 2

Site 3

3.0

10

Temperature, -T No.1 Umiat Bentonite No. 3 Suffield Silty Clay

No.2 Hawaiian Clay No.4 Dow Field Silty Clay

Figure 3b. Correlation for the test data from Anderson and Morgenstern (1973).

100%

Frost Depth Ratio of F.f / F.u

9

8

7

6

5

4

3

2

1

Figure 4a. Sensitivity of Frost Depth at Three Sites for Four Cases: (1) Assuming fully frozen water (ignoring Wu), (2) Considering unfrozen water in frozen soil using Eq. [1] with β = - 0.25, (3) Eq.[1] with β = - 0.4, (4) Eq.[1] with β = - 0.15.

0

Unfrozen Water Content, Normalized

2.5

90% 80% 70%

Site 1

Site 3

60%

10%

20%

30%

40%

Initial Water Content, Wc Figure 4b. Sensitivity of Frost Depth with Initial Water Content. (Frost Depth Ratio: Frost Depth for the Case Ignoring Wu divided by that for the Case Considering unfrozen water in frozen soil using Eq. [1] with β = - 0.25)

5

INCOPERATION IN FROST DEPTH CALCUATION

The following summarizes the main findings: 1. The assumption of fully frozen water in frozen soil (i.e., ignoring the unfrozen water, 𝑤𝑢 ) would lead to an unconservative estimate of frost depth, by about 15% to 30%, as shown in Figure 4b. The underestimation of frost depth appears more significant for the soil with lower initial water contents, as shown in Figure 4b. 2. The frost depth is not sensitive for the range of 𝛽 from -0.15 to -0.4, which yields a difference of less than 5% (about 2% to 4%) in comparison with the base-case value (𝛽 = - 0.25), see Table 3. The variation of frost depth within the range of 𝛽 is less than 4%. As such, using the default value of 𝛽 (-0.25) is considered acceptable for most engineering evaluations.

This section presents the proposed procedure in the frost depth calculation (i.e., modified Berggren approach) to consider the unfrozen soil water content. The average unfrozen water content within the frost depth can be estimated using the equation below: 0

𝑤𝑢_𝑎𝑣𝑔 = ∫ 𝑤𝑢 (𝑇)𝑑𝑇 𝑇𝑠

where 𝑇𝑠 represents the equivalent surface temperature in ℃. The ice content in frozen soil can be obtained using the formula below to account for the unfrozen water content in frozen soil. 𝑤𝑖 = 𝑤𝑐 − 𝑤𝑢_𝑎𝑣𝑔 The volumetric latent heat of the soil can be estimated from the equation below: 𝐿𝑠 = 𝛾𝑑 × 𝑤𝑖 × 𝐿 where 𝛾𝑑 is dry soil unit weight. L is the latent heat of fusion of water to ice, with the typical value of 334 kJ/kg. The impact of unfrozen water is negligible for the volumetric heat capacity and thermal conductivity (Nixon and McRoberts 1973). The following provides the procedure to take these into account for reference only. The volumetric heat capacity for frozen soil can be estimated using the relationship to account for 𝑤𝑢 : 𝐶𝑓 = 𝛾𝑑 /𝑟𝑤 (0.17 + (𝑤𝑐 − 𝑤𝑢 ) + 0.5 × 𝑤𝑢 ) × 4.187 × 106 𝐽/ (𝑚3 ℃) The thermal conductivity for frozen soil can be obtained by using 𝑤𝑖 instead of 𝑤𝑐 for Kersten approach or other acceptable equivalent, as shown below for the fine-grained soil type. 6

7

This paper presented a practical approach for engineers to calculate the frost depth considering the unfrozen water in frozen soil. In comparison with the conventional approach which assumes all water is frozen in soil, this approach provides a more conservative frost depth (typically about 15% to 30%) for the fine-grain soil. The frost depth calculation approach was coded in Mathcad and Excel formats and is available for download at the website listed in the reference (Qu, 2023). The following summarizes the main findings. 1.

2.

SENSITIVITY ANALYSES

The sensitivity analyses were conducted to investigation the following two questions: 1. What is the impact of unfrozen water content in frozen soil to the calculated frost depth. 2. How sensitive is the value of 𝛽 to the calculated frost depth? The three cases were evaluated with β = - 0.25 (base case, default value), -0.15 (upper bound) and -0.4 (lower bound). The frost depth calculation was conducted at three sites with different climate conditions. Site 1 is in south Ontario. Site 2 is in Alberta. Site 3 is in Northern Ontario. Table 2 presents the climate conditions for the three sites. Table 3 presents the calculated frost depths for the approaches accounting for the unfrozen water in frozen soil at the three sites with different climate conditions. Figure 4a shows the results. Figure 4b presents the impact of initial water content to the frost depths for the approaches accounting for the unfrozen water in frozen soil the at the three sites with different climate conditions.

SUMMARY AND CONCLUSIONS

3.

4.

The proposed approach is practical as it requires only two basic input parameters, i.e., total initial water content and liquid limit, which can be obtained from routine standard tests and are usually available in most projects. The proposed approach would lead to a more conservative frost depth (typically about 15% to 30%) for fine-grained soil, in comparison to the conventional approach ignoring the unfrozen water in frozen soil. For coarse-grained soil, the proposed approach yields a similar frost depth estimate with the conventional approach as the unfrozen water content in frozen coarse-grained soil is relatively low. The following summarizes the limitations of the proposed approach. o If salts are present in soil, a correction should be applied to the proposed approach for frost depth calculation, depending on the salinity measurement from the site. o Caution should be applied for soils with liquid limits over 100, as the correlation from the proposed approach is based on the soil test data with liquid limits less than 100. Future studies using laboratory or field tests will be helpful to verify the findings presented in the paper regarding the impact of unfrozen water in frozen soil on frost depth.

Table 1a. Soil properties with LL and 𝑤𝑢1 from literature Soil

𝐿𝐿, %

𝑤𝑢1 ,%

West Lebanon gravel (>B loaded almost uniformly along the footing length (i.e., plane-strain /2dimensional cases typical for retaining structures), the shape factors have no effect being essentially equal to 1.0. The inclination factors are always ≤ 1.0 and in some instances could be significantly less than 1.0. According to CHBDC, the inclination factors ic, iq and iɣ are described in Article 6.10.2 and depend exclusively on the load inclination angle from the vertical and the effective friction angle of the foundation soil. According to AASHTO, the inclination factors are described in Article 10.6.3.1.2a based on the classical theoretical considerations (Vesic, 1975) and recommended also in the Canadian Foundation Engineering Manual (CFEM, 2006). They incorporate the effects of the loads (H, V), effective soil friction angle and cohesion of the foundation soils, the azimuth angle and the ratio between the footing dimensions. In case of frictionless soils, iɣ =iq

= 1 and ic is less than 1.0 depending on the undrained shear strength, H, B, L, Nc and ϴ. It is worthwhile to note that for both codes it is conceivable that for some combinations of load inclinations, soils strength parameters, and footing dimensions, the inclination factors may become zero, or even breakdown (negative, or imaginary values). CHBDC is rather ambiguous on the use of the inclination factors for the calculation of the bearing resistance. The main Article 6.19.9.4 states: “For the purpose of calculating the bearing resistance of the gravity mass of the MSE wall system at ultimate limit states, an equivalent strip footing shall be assumed having width of dimension B at the foundation level”. From this it can be interpreted that all the provisions stipulated for the determination of the bearing resistance should be considered for the regular strip footings. However, Article C6.19.9.4 in the Commentaries reads: “The effect of eccentricity and load inclination is accommodated by the introduction of an effective width, B' = L-2e, instead of the actual width”. Therefore, this statement may be interpreted that the inclination factors could be ignored because the eccentricity, e, is not influenced by the ratio H/V. AASHTO at the commentaries to Article 10.6.3.1.2a addressed the theoretical determination of the bearing resistance of shallow footings includes a statement reading: “Most geotechnical engineers nationwide have not used the load inclination factors”. AASHTO further added: “In practice, therefore, for footings with modest embedment, consideration may be given to omission of the load inclination factors”. These statements seem like an inducement to ignore the effects of the inclination that have quite a strong contribution in the reduction of the bearing resistances especially at structures subjected to substantial and permanent lateral loads. 4

SEISMIC HAZARD AND DESIGN LOADS

The basic design seismic hazard level in the Canadian codes (CHBDC 2019, NBC 2020) applicable to the forcebased design is defined for a 2% probability of exceedance in 50 years (about 2475-year return period) while in the USA code (AASHTO 2020) the design earthquake is defined for a 7% probability of exceedance in 75 years (about 1035-year return). Hence, right from here it should be expected that significantly higher design seismic loads are considered in Canada. It is true that the Canadian codes also include provisions for performance based-design methods that are allowed to consider lower levels of hazards such as 475-year and 975-year return periods. As an example, consider a particular geographic zone at the Canadian-USA border as shown in Figures 2a and 2b. Due to the geographic proximity it is conceivable that the actual intensity of the seismic hazard should be the same. The reference Peak Ground Accelerations (PGAref) for the two sites have been determined from the Earthquake Canada website Canada Seismic Hazard Tool (CSHT) according to the 2020 National Building Code of Canada (NBC), and from

the PGA contour maps provided in AASHTO 2020 under Article 3.10.2.1. For Canada the three values of PGA shown in Figure 2a are for the 2475-year, 975-year and 475-year return periods, respectively. Interestingly, the 2020 CHST now provides directly the seismic design parameters for all site classes from A to E and accordingly the site coefficients F(PGA) required in CHBDC, Table 4.8, to determine the site adjusted PGA are no longer necessary. However, it appears that the 2020 NBC seismic hazard levels may have been increased compared to 2015 NBC levels. As a quick comparison, according to CHBDC 2019 the site adjusted PGA for stronger earthquakes (PGAref ≥ 0.3) would decrease for site classes D and E since F(PGA) < 1.0. However, the updated 2020 CSHT indicates a tangible increase of the site-adjusted PGA for the same lower site categories compared to what is obtained using the CHBDC methodology but with the latest values for the reference PGA (i.e., PGA for Site Class C). For example, at the location in Figure 2 and the 2475-year event, CSHT indicates PGA=0.488 for Site Class C and the CHBDC site factor becomes F(PGA)=0.884 for a Site Class D. Hence, according to CHBDC approach, the adjusted PGA for Site Class D becomes 0.431. The latter is by more than 22% less conservative than the PGA=0.528 obtained from NBC 2020 CSHT for Site Class D.

a. Canada PGA = 0.488 / 0.347 / 0.257 (Site Class C)

correct comparison between the two codes, a site correction factor, F(PGA), interpolated between 1.1 and 1.0 must be applied to the US PGAref, corresponding to PGAref=0.3 and PGAref>0.4, respectively (AASHTO Table 3.10.3.2-1). Hence, the site adjusted design PGA in AASTHO becomes 0.33 for the example site and Site Class C. From Figures 2a and 2b it is obvious that for the Site Class C the intensity of the design seismic hazard in Canada expressed by the PGA at the selected wall location could be by about 48% larger than in the US design. Even assuming similar return period around 1000 years, the Canadian uses PGA by 5.1 to 15.7% larger (more conservative) than in US. 4.1

The intensity of the design seismic loads for retaining structures is calculated using the seismic coefficient, kh. Both CHBDC and AASHTO indicated that the seismic horizontal acceleration coefficient (k h) should be taken as half the site-adjusted PGA where the walls and the backfill can move 25 to 50 mm. For walls that are restrained against lateral movement, the seismic lateral earth pressures should be obtained using the Mononobe-Okabe (M-O) formulation, or the General Limit Equilibrium (GLE) methods using a seismic horizontal acceleration coefficient (kh) equal to the siteadjusted PGA. AASHTO 2020 allows for additional reduction of the kh by a factor α = 1+0.01h(0.5FvS1/kho-1) on the accounts of the wave scattering, if the walls / slopes are taller than 6.0 m and the Site Class are C, D, or E. The expression for α is empirical and as such the wall height, h, should be expressed in feet. Fv is the Site Factor for long-period range of acceleration spectrum S1 (T=1.0 s), and kh0 is the site adjusted peak acceleration for zero displacement. Based on the above, for the example MSE wall located within a Site Class C environment kh and the seismic earth pressure coefficient, Kae, using the M-O expression are summarized in Table 4 below. The results in Table 4 illustrate significant conservatism by more than 60% of the design seismic coefficient, kh, and by 23% at the seismic earth pressure coefficient, Kae. 4.2

b. USA PGAref = 0.3 (Site Class B) PGA = 0.33 (Site Class C) Figure 2. Location of MSE Wall Example (Figure 2a from CSHT and Figure 2b from AASHTO) By convention, the seismic hazard reference parameters in CHBDC were provided for Site Class C (Very Dense Soil and Soft Rock) while in AASHTO the reference parameters are provided for Site Class B (Rock with Vs=[2500 to 5000 ft/s]). Therefore, for a

Design Seismic Coefficient

Wall Inertial Loads

According to the Canadian practice the wall inertia forces are calculated as khW, where W is the weight of the entire wall. However, the weight and thickness of the facing are typically ignored in the external stability analyses. According to AASHTO method, since the MSE wall is not a rigid block, only a portion of the reinforced soil mass, called “effective mass”, corresponding to 50% of the “effective” wall height would shake in phase with the maximum peak acceleration. The effective height is equal to the total height of the wall if the top of the wall is horizontal. The inertial effects of the mass of the facing are added to the effective mass inertia. In the case of the

wall example with a typical cruciform facing of 150 mm width (linear weight of 24kN/m3x0.15mx7.3m =26.28kN/m), the resulting wall inertia loads, Pir, are summarized in Table 5 below.

Kae-Canada / Kae-USA 1.23 1.02 -

1.12

Kae 0.469 0.39 0.382

0.428

kh 0.244 0.129 0.152

0.173

α1 1 0.92

1

1

F v1 1 1 1.95

1

S1 0.521 0.248 0.225

0.352

Site Class C Adjusted PGA 0.488 0.347 0.257 0.33-

Canada 2475yr Canada 475yr USA 1035yr

Parameters not used in CHBDC

1

Table 5. Example Wall 100% Inertia Loads, Pir (kN) Site Class C Canada Adjusted PGA 0.488-(Canada 207.7 2475yr) 0.347-(Canada147.3 975yr) 0.257-(Canada109.8 475yr) 0.33-(USA1035yr) 1 NA = Not Applicable 2 Without wave scattering effect

USA

RESULTS EXAMPLE MSE WALL

A comparison of the inclination factors obtained for the example MSE wall using the CHBDC and AASHTO methods is made in Table 6. Because only cohesionless foundation soil was considered, the ic factors are not included in Table 6. The results of the external design are shown in Table 7 and are compared thru the synthetic Capacity / Demand (C/D) ratios for the relevant load combinations. AASHTO stipulates that Combination ID= 3 (Extreme I) consider two subcategories of combination of the inertial and seismic earth pressures, namely: •

Canada 975yr

Design Event

Table 4. Design Seismic Coefficients

5

NA1

Pir - Canada / Pir - USA 3.03

NA1

2.15

NA1

1.60

68.42

-

The example suggest that the inertial load is by more than 200% more conservative in CHBDC than in AASHTO. The guidelines regarding the evaluation of the inertial loads and their combination with the seismic earth pressures is not detailed at all in CHBDC for the non-liquefiable soils. As such there is no basis for scaling down for the combination of 100% of inertia with 100% of seismic earth pressure which likely is largely overconservative.

•

Case 1 consisting of 50% wall inertia + 100% seismic earth pressure; and Case 2 consisting of 100% wall inertia + 50% seismic earth pressure but not less than the static earth pressure).

Table 6. Comparisons Inclination Factors Load Combination ID 1 2 3

CHBDC iɣ

iq

AASHTO iɣ iq

0.47 0.45 0.04

0.79 0.78 0.51

0.65 0.62 0.51

0.79 0.77 0.69

In Table 7, Combination ID = 3 compares the regular CHBDC ULS-5 with the AASHTO most conservative between Case 1 and Case 2 under Extreme-I combination. In CHBDC such type of subcategories of combinations of seismic earth pressures and wall inertial forces are not distinctly specified. The Commentaries to CHBDC (Article C6.14.9) mentions that various international agencies adopt subcategories of load combinations including different proportion of the inertial forces from 25% to 100%, but these are in conjunction with 100% of the kinematic effects in the case of liquefiable soils. Table 7 illustrates that for the studied example wall at SLS/Service combinations, CHBDC design is more conservative by more than 14% at bearing mostly due to the difference in the definition of the inclination factor, iϒ. At sliding CHBDC is more conservative by more than 24% at sliding while AASHTO is marginally (by 3.8%) more conservative at eccentricity. At static ULS-1/Strength-I combinations CHBDC is by 49.7% more conservative at bearing. At sliding the level of conservatism in CHBDC narrows to 9.9%. At eccentricity the differences are less than 1.0%. At seismic ULS-5/Extreme-I the CHBDC design indicates catastrophic failures at bearing and sliding for the example wall arrangement while AASHTO suggests tangible overdesign with C/D > 3 at bearing and C/D > 1.3 for sliding. According to CHBDC method, the example wall to meet C/D = 1.0 at sliding under ULS-5

Table 7. Summary Results - Example MSE wall Relevant Load Combination ID

Capacity / Demand (C/D) Control Ratios BEARING

SLIDING

CHBDC

AASHTO

CHBDC

AASHTO

CHBDC

AASHTO

1

4.9 (14.1%)

5.59

1.57 (24.8%)

1.96

2.83 (-2.8%)

2.75

2

1.61 (49.7%)

2.41

1.21 (9.9%)

1.33

3.68 (-0.8%)

3.65

3

0.39 (736%)

3.26

0.80 (65%)

1.32

1.81 (66.3%)

3.01

combination will require an increased width to 9.3 m, hence a B/H ratio of 1.27 compared to B/H = 0.73 for the stable wall design according to AASHTO method. In other words, for the selected example the Canadian design leads to a wall by 75% wider (more massive) than in USA. At eccentricity all checks pass comfortably with C/D > 1.8 for CHBDC and C/D > 2.75 for AASHTO. 6

CLOSING REMARKS

The study was limited to the external stability and fully drained conditions. The Canadian design of the external stability of MSE wall is more conservative than the USA although the load factors in the USA code trend higher than in the Canadian code. The sources of the conservatism originate from lower resistance factors by an average of 28% in CHBDC combined with a significantly and inherently lower inclination factor iɣ recommended by the Canadian code. A particularly significant impact has the inclination factor at ULS-5 where, according to CHBDC, iɣ rapidly approaches zero, and hence, virtually annihilates the bearing resistance in the case of drained cohesionless foundation soils. Another major source of the difference between CHBDC and AASHTO originates from the significantly higher levels of the design earthquake in Canada (approximate 2475-year return) versus in USA (approximate 1000-year return). This difference is further amplified by the differences in the methods of considering the wall inertial loads and their combination with the seismic earth pressures. The seismic hazard levels according to NBC 2020 seemed to have trended upwards, deepening the gap between the CHBDC and AASHTO regarding the specified levels for the design seismic hazard in Canada and USA, respectively. 7

ECCENTRICITY

ACKNOWLEDGEMENTS

The authors are deeply grateful to the WSP E&I Canada

Limited management and to Taner Aktas, P.Eng., for the logistic support in the preparation of this paper. 8

REFERENCES

AASHTO, LRFD Bridge Design Specifications, 9th Edition, 2020. Canadian Research Council Canada, National Building Code of Canada, 2020, Volume 1. Canadian Research Council Canada, National Building Code of Canada, 2015, Volume 1. Canadian Geotechnical Society, Canadian Foundation Engineering Manual, 4th Edition, 2006. CSHT, 2020 National Building Code of Canada Seismic Hazard Tool (earthquakescanada.nrcan.gc.ca). CSA Group, Canadian Highway Bridge Design Code, CSA S6:19. Vesic A. S. 1975. Bearing Capacity of Shallow Foundations, Foundation Engineering Handbook, Editors Winterkorn, H.F. and Fang H-Y., Van Nostrand Reinhold Co.

Conception et construction d’une passerelle à motoneiges sur la rivière Aisley Pierre Vannobel, ing., M.Sc.A. & Jose Marcel Bustamante Bedoya, ing., M.Sc. Hydro-Québec, Montréal, Québec, Canada Benoît Turgeon, ing. WSP, Ville de Saguenay, Québec, Canada ABSTRACT Three snowmobile auxiliary bridges were built in 2022 and 2023 to provide a safe crossing of the Romaine Salmon River and of the Aisley River. The auxiliary bridges, on the edge of Route 138, are connected to the Trans-Québec no. 3 snowmobile trail that links Tadoussac to Baie-Johan-Beetz on the north shore of the St. Lawrence River. This article specifically addresses the design and construction of the abutment’s foundations of the Aisley River auxiliary bridge. They consist of deep steel piles driven to refusal in overburden and to rock. These foundations, constructed in the shelter of riverbank sheet piles, are located in the limits of the Goldthwait Clay Sea. Vibrations during pile driving to refusal were measured to ensure compliance with MTQ vibration criteria at the nearby Route 138 bridge. Dynamic monitoring was done on piles to confirm their capacity. RÉSUMÉ Trois passerelles à motoneiges ont été construites en 2022 et 2023 afin de pouvoir traverser sécuritairement la rivière à saumon Romaine ainsi que la rivière Aisley. Les passerelles, situées en marge de la route 138, sont agencées au sentier de motoneiges Trans-Québec no. 3 qui relie Tadoussac à Baie-Johan-Beetz en rive nord du fleuve St-Laurent. Cet article traite particulièrement de la conception et de la construction des fondations des culées de la passerelle sur la rivière Aisley. Elles sont constituées de pieux profonds en acier foncés au refus dans le mort-terrain et au roc. Ces fondations, construites à l’abri de palplanches, sont situées dans les limites de la mer d’argile Goldthwait. Les vibrations lors du battage des pieux au refus ont été mesurées afin d’assurer le respect des critères de vibration du MTQ au pont de la route 138 à proximité. Des analyses dynamiques ont été effectuées sur les pieux afin de confirmer la capacité des pieux.

1.

INTRODUCTION

Hydro-Québec a réalisé les nouveaux aménagements hydroélectriques du complexe La Romaine sur la rivière Romaine. Or, Hydro-Québec et les citoyens du milieu anticipaient que l’exploitation des nouveaux aménagements hydroélectriques du complexe La Romaine allait affecter la formation du couvert de glace sur les rivières Romaine et Aisley. Conséquemment, les motoneigistes ne pourraient plus passer de façon sécuritaire sur ces rivières (Figure 1). Pour remédier à cette problématique, dans un premier temps, un service d’escorte automobile 24 heures sur 24 a été mis en place afin que les motoneigistes puissent circuler sécuritairement sur les ponts de la route 138, au droit des rivières Romaine et Aisley. Puis en 2022 et 2023, trois passerelles à motoneiges ont été construites par Hydro-Québec pour permettre aux motoneigistes de traverser de façon sécuritaire la rivière à saumon Romaine et la rivière Aisley adjacente, tributaire de la rivière Romaine. Ces travaux ont été réalisés sous la supervision d’Hydro-Québec, également maître d’œuvre. Ces trois passerelles ont été conçues par la firme WSP. Elles ont été construites par l’entreprise SEIE avec l'aide de ses deux sous-traitants: SBP et Les Constructions Mackenzie Inc.

Figure 1. Couvert de glace affecté par les nouveaux aménagements sur la rivière Aisley Ces passerelles en acier sont agencées au sentier de motoneiges Trans-Québec no 3, qui relie les villes de Tadoussac et Baie-Johan-Beetz, qui se trouvent respectivement sur la Côte-Nord et la moyenne Côte-Nord (rive nord du fleuve St-Laurent). Ces passerelles à motoneiges sont situées en marge de la route 138 vers le

PK 3 de la rivière Romaine, à une cinquantaine de kilomètres en aval de l’aménagement hydroélectrique Romaine-1 le long de la rivière (Figure 2). La passerelle de la rivière Aisley a été construite à proximité d’un pont existant, édifié en 1964 et présent le long de la route 138 sur la rivière Aisley, en aval de la nouvelle passerelle. Les culées en béton armé de ce pont existant reposent sur des pieux en bois battus au refus au till ou au roc. Or, la distance la plus courte entre les culées de ce pont et celles de la nouvelle passerelle à motoneiges est, en son point le plus rapproché, de seulement 26 m, ci qui implique des précautions à prendre lors des travaux. Cette passerelle en acier galvanisé, d’une longueur de 34,5 m, a la particularité d’être située dans les limites de la mer d’argile Goldthwait (Figure 3). En 1980, un très grand glissement par étalement dans l’argile avait emporté la route 138 à quelques kilomètres de cette passerelle, ce qui dénote la grande sensibilité de ce dépôt argileux marin dans ce secteur.

Figure 3. Limites de la mer Goldthwait (Figure modifiée de Leroueil et coll., 1985) Route 138 Sentier de motoneiges Passerelles à motoneiges

2.1

Figure 2. Localisation des passerelles à motoneiges Cet article traite principalement, du point de vue géotechnique, de la conception et de la construction des fondations des culées de cette nouvelle passerelle de la rivière Aisley, située dans les limites de la mer d’argile Goldthwait. 2.

CONCEPTION ET CONSTRUCTION DE PASSERELLE ET AGENCEMENT AU SITE

Dépôt d’argile de la mer Goldthwait

L’argile marine du type CL de la mer Goldthwait, au site de la passerelle Aisley, montre une faible plasticité et un indice de liquidité de l’ordre de 2,1 à 3,3, ce qui lui confère une grande sensibilité au remaniement (Tableau 1). Sa résistance au cisaillement non drainé Su est de 35 kPa min alors que sa résistance remaniée Sur est de l‘ordre de 0,1 kPa. La grande sensibilité de l’argile a été considérée par le concepteur, ce qui a permis le bon déroulement des travaux d’excavation et de construction de la passerelle.

LA

Les deux culées en béton armé de la passerelle à motoneiges, munies de murs en retour en console, reposent sur des pieux foncés au refus au roc ou au mortterrain en rives de la rivière (Figure 4). Les fondations profondes des culées de la passerelle ont été ceinturées de palplanches en rives de la rivière au niveau du dépôt d’argile sensible à très sensible de surface. Chaque culée est appuyée sur six pieux verticaux et six pieux inclinés. Lors de la phase des travaux, la passerelle a été modélisée en 3D par Hydro-Québec à l’aide du logiciel Catia, afin de permettre de bien ajuster l’agencement de la passerelle avec les conditions au site. Cette vue 3D selon l’axe ne montre que la moitié de la structure (Figure 4).

Figure 4. Concept de la passerelle Aisley

Tableau 1. Caractéristiques de l’argile de type CL testée Forage F-3

Forage F-4

Teneur en eau (%)

48,1

44,7 à 51,4

Limite liquide (%)

34

29

Limite plastique (%)

21

20

Sable (%)

2,4

1,8

Silt

46,5

54,3

Argile

51,1

43,9

2.2

Excavations du mort-terrain

Les excavations de mort-terrain aux deux appuis de la passerelle ont été requises afin de permettre la réalisation sécuritaire des travaux et la mise en place de plateformes temporaires stables. Ceci a ainsi permis de mettre en place des palplanches et des pieux puis de manipuler et de mettre en place la structure en acier de la passerelle. Les pentes d’excavation étaient de 2H :1V avec protection en enrochement afin d’assurer la stabilité des pentes. 2.3

Palplanches

Des palplanches ont été foncées au site sur une profondeur de plus de 5 m, en guise de batardeaux temporaires. Ainsi, il a été possible d’atteindre le niveau requis d’arasement des pieux foncés aux deux appuis sous le lit de la rivière et réaliser à sec la structure de béton armé de la culée au-dessus. La grande sensibilité de l’argile au site a fait en sorte qu’elle se remanie et perd donc de sa résistance sur le périmètre des palplanches sur quelques décimètres d’épaisseur lors de leur fonçage par vibrations (Figure 5). Ce phénomène a dû être considéré lors de la vérification de la stabilité des caissons en palplanches.

Figure 5. Remaniement de l’argile lors du fonçage des palplanches 2.4

Conception des pieux

Avec le peu de capacité portante des sols en place, le choix d’utiliser des pieux comme support de la structure s’est vite imposé. Les pieux sont du type circulaire en acier, à bout fermé à l’aide d’une pointe de fonçage, d’un diamètre de 0,323 m et d’une épaisseur d’un peu plus de 8 mm. Une

analyse par équation d’ondes a été réalisée à l’aide du logiciel GRLWeap par la firme spécialisée en fondation profonde CBF. Cette analyse était nécessaire afin de confirmer la capacité géotechnique des pieux au refus et d’évaluer le critère de refus requis, dans le but de fournir la bonne capacité portante escomptée. Selon cette analyse, un critère de refus de 25,4 mm d’enfoncement au maximum pour 10 chutes d’un marteau d’un poids de 29,9 kN avec une chute de 0,91 m permettrait d’atteindre la capacité portante géotechnique pondérée requise (ÉLUL) de 600 kN. La contrainte de compression dans l’acier était estimée à environ 210 kPa max, soit inférieure à la limite élastique de l’acier de 310 kPa. 2.5

Vibrations engendrées par le battage des pieux au refus

La vitesse des particules au pic qui se produit à un endroit donné, en fonction de la distance du fonçage d’un pieu par battage, ainsi que la sévérité et la perceptibilité des vibrations transmises peuvent être évaluées à partir des travaux de Bay (2003) en fonction de la compacité des sols en place. L’évaluation de la transmission des vibrations est influencée par plusieurs autres paramètres comme la présence d’une croûte de sols raides en surface, d’une excavation profonde, de la proximité du socle rocheux ou de la présence d’affleurements rocheux. Une telle évaluation ne peut en aucun cas être substituée à la mesure des vibrations réelles au site lors des travaux de mise en place des pieux. Les vibrations transmises au pont existant du Ministère des Transports du Québec (MTQ) lors du battage au refus des pieux de la nouvelle passerelle à motoneige ont été évaluées en fonction de nos conditions au site et en se fondant sur les travaux de Bay (2003). Les vibrations, estimées empiriquement, sont d’environ 1 mm/s. Elles sont donc fortement perceptibles, mais ne génèrent pas de dommages au pont existant du MTQ et respectent le critère du Ministère des Transports du Québec, qui est de 3 à 8 mm/s max. Deux sismographes munis d’un géophone triaxial ont été installés sur le béton et sur les remblais d’approche du pont existant sur la route 138. Ces instruments, qui permettent de mesurer l’amplitude et la fréquence des ondes vibratoires selon les trois composantes orthogonales indépendantes (longitudinale, verticale et transversale), ont été installés afin de vérifier et de confirmer le respect des critères de vibration du MTQ lors du battage des pieux au refus à la nouvelle passerelle. Les vibrations mesurées au pont existant, lors du battage des pieux à la passerelle au refus au till ou au roc, étaient de l’ordre de 0,1 à 1,5 mm/s, soit sensiblement égales à celles préalablement estimées empiriquement (Figure 6). Il est à noter que le simple déplacement de l’instrument par un technicien a engendré une valeur enregistrée de l’ordre de 2 mm/s. La fréquence verticale mesurée était de l’ordre de 8 à 70 Hz. La faible valeur des vibrations mesurées confirme que le battage des pieux à la passerelle n’était pas dommageable pour l’intégrité du pont existant et de ses fondations.

Figure 6. Vibrations mesurées au pont existant sur la 138 lors du battage des pieux (Source : Englobe) 2.6

Contrôle qualitatif sur les pieux battus au refus

Une analyse dynamique a été effectuée par une firme spécialisée sur deux des 12 pieux foncés au refus à la culée ouest afin de confirmer, à l’aide du logiciel Capwap, leur capacité portante pondérée adéquate au refus dans le mort-terrain constitué en profondeur de till glaciaire. Suite à la première analyse dynamique effectuée sur le premier pieu battu au refus, le critère de refus a été sécuritairement ajusté à 19 mm d’enfoncement au maximum pour 10 chutes de 0,91 m d’un marteau d’un poids de 29,9 kN. Les résultats ont démontré une capacité portante pondérée supérieure à celle estimée; elle était de 900 kPa avec un coefficient de tenue géotechnique de 0,5 au lieu de 600 kPa en tête du pieu estimé sécuritairement initialement en conception avec un coefficient de tenue géotechnique de 0,4 (Figure 7). Aucun endommagement aux pieux n’a été noté (Figure 8). Quant à la capacité portante pondérée des 12 pieux au refus franc au roc à la culée est, elle n’a pas été testée par un essai dynamique.

Le principal point contrôlé pour les pieux battus au refus franc au roc a consisté à vérifier précisément que les pieux ne relèvent pas suite à leur enfoncement. Un des 12 pieux au roc à la culée Est a justement relevé sous l’action du principe d’Archimède, avant que du béton ne soit appliqué dans ces 12 pieux. Les 12 pieux foncés à la culée Ouest ne pouvaient pas relever étant donné que la forte friction latérale sur les fûts des pieux était supérieure à la poussée d’Archimède vers le haut sur ces pieux.

Figure 8. Sommet des pieux en acier 2.7

Figure 7. Exemple de résultat d’analyse dynamique à l’aide du logiciel Capwap (Source : PDA Consultants Inc.)

Culées en béton armé et structure en acier

Les deux culées en béton armé ont nécessité la mise en place de 125 m3 de béton armé contenant 8 300 kg d’acier. Des coffrages temporaires en bois ont été préalablement installés afin de permettre le bétonnage des culées (Figure 9). Des remblais de protection des rives et des culées, constitués de blocs de 0,5 à 0,9 m de dimensions, ont été placés en rive.

2.8

Assemblage et mise en place de la passerelle à motoneiges

La passerelle a été assemblée sur place à l’appui gauche (Figure 10). Elle a été poussée en place longitudinalement le 24 mai 2023 (Figure 11). 3.

Figure 9. Culées en béton armé

CONCLUSION

La passerelle à motoneiges Aisley, ainsi que les deux autres sur la rivière Romaine à proximité, seront en opération à l’hiver 2023-2024. Ce type de passerelle à motoneiges, qui doit être conçue en fonction des conditions propres à chaque site et construite en respect de l’environnement, de la sécurité de chacun et de la qualité, constitue une mesure de compensation efficace pour assurer à long terme la sécurité et l’agrément des usagers du milieu. 4.

REMERCIEMENTS

Les auteurs remercient leurs collègues des départements suivants qui se sont assurés de la conformité du présent article: Intégration et ingénierie – Géotechnique Unité Expertise et conception – Hydraulique, hydrologie et barrages en remblai – Direction Sécurité des barrages et infrastructures; Direction Expertise et soutien technique – Sécurité des barrages et infrastructures; Ingénierie du Chantier Romaine-4; Administration de contrats Chantier Romaine-4; Communications entreprise II. Figure 10. Assemblage sur place de la passerelle en acier à l’appui gauche (Source : LP Drone)

Figure 11. Passerelle en acier en place (Source : LP Drone)

5.

RÉFÉRENCES

Bay, J.A. 2003. A summary of the Research on Pile Driving Vibrations, Proceedings of the Pile Driving Contractor’s Association 7th Annual Winter Roundtable, Atlanta, Georgia, United States. Dionne, J.-C. 1977. La mer de Goldthwait au Québec, Géographie physique et Quaternaire, 31 :6180. Englobe, 2023. Rapport de suivi de vibrations du 02 au 08 février 2023, No de référence 35-02207319.000-0100HS-R-0123-00, Varennes, Québec, Canada. Forage CBF. 2022. Pieux – Calculs structuraux – Passerelle de motoneige – Au-dessus de la rivière Aisley, Havre-Saint-Pierre, Québec, Canada. GHD, 2018. Étude géotechnique - Nouvelles passerelles – Rivières Romaine et Aishley – Route 138, Havre-SaintPierre, Québec. No de référence : 11184188A1, Rimouski, Québec, Canada. GHD, 2022. Avis géotechnique – Construction d’une passerelle au-dessus de la rivière Aisley – Route 138, Havre-Saint-Pierre, Québec. No de référence : 12592108-A2, Québec, Québec, Canada. Hydro-Québec. 2020. Bilan des activités environnementales 2019. Complexe de la Romaine.

Hydro-Québec. 2022. Fourniture et installation de passerelles pour motoneigistes près de la route 138 sur les rivières Aisley et Romaine – Contrat R3-1006. Aménagement hydroélectrique de la Romaine-3. Clauses techniques particulières – Pour construction. No de référence : WSP 181-13003-01. Hydro-Québec. 2022. Note interne – Visite chantier et tranchées d’exploration pour installation de passerelles à motoneige – amont du pont Benoît-Vigneault (P02495W), amont du pont Alexandre –Tanguay (P02498E) et amont du pont P-02497. Code de classement : CI-2022-0027-01. Hydro-Québec. 2023. Maquette Catia 3D, Montréal, Québec, Canada. Leroueil S. et coll. 1985. Remblais sur argile molle. Technique et documentation Lavoisier, Paris, France, 342 pages. Ministère des Transports du Québec, 2022. Courriel du MTQ à Hydro-Québec. R3-10-06 – Demande de permis de voirie – contrôle des ondes vibratoires. 22 juillet 2022. PDA consultants inc. 2023. Rapport Final – culée 1 – Analyse de battage de pieux. Passerelle de motoneige – Au-dessus de la rivière Aisley, Havre-Saint-Pierre (Québec). Longueuil, Québec, Canada. TR3E Experts-Conseils inc. 2022. Conception du batardeau de l’axe 1. Batardeau – Passerelle Aisley. Notes de calcul. Rimouski, Québec, Canada. TR3E Experts-Conseils inc. 2022. Conception du batardeau de l’axe 2. Batardeau – Passerelle Aisley. Notes de calcul. Rimouski, Québec, Canada.

Wednesday, October 4, 2023

NUMERICAL MODELS II

Probabilistic analysis of an embankment under different rainfall events considering spatial variability of soil strength parameters Leila Baninajarian1, Sina Javankhoshdel1 & Rashid Bashir2 1Rocscience, Inc., 54 St. Patrick St., Toronto, ON, M5T 1V1 Canada 2York University, 11 Arboretum Lane, Toronto, ON, M3J 1P3 Canada ABSTRACT Studies have shown that embankments built with fine materials such as silt are more susceptible to extreme rainfall events than those built with coarse materials. In most of these studies, it was assumed that soil strength and hydraulic parameters such as unit weight, friction angle, cohesion, and hydraulic conductivity are random variables. However, soil properties and hydraulic properties can vary spatially within a soil profile. This has been shown by several researchers by carrying out stochastic analyses. Therefore, as a preliminary study, this research investigates the influence of spatial variability of soil strength parameters including cohesion, friction angle and unit weight on the probability of failure and a simplistic assumption of constant hydraulic conductivity throughout the soil profile is made. The probabilistic analysis is carried out for an extreme rainfall event. The results are compared with analyses in which assumption of random variable soil parameters is made. The results show a reduction in the value of the total probability of failure for the case with spatially variable soil strength parameters compared to the case with random soil parameters. The spatially variable case better describes the practical site condition. RÉSUMÉ Des études ont montré que les remblais construits avec des matériaux nobles tels que le limon sont plus sensibles aux précipitations extrêmes que ceux construits avec des matériaux grossiers. Dans la plupart des études mentionnées cidessus, on a supposé que certains des paramètres hydrauliques tels que la conductivité hydraulique sont des variables aléatoires, tout en tenant compte de la variabilité aléatoire de l’angle de frottement du sol. Cependant, les propriétés du sol et les propriétés hydrauliques peuvent varier spatialement à l’intérieur d’un profil de sol. Cela a été démontré par plusieurs chercheurs en effectuant des analyses stochastiques. Par conséquent, en tant qu’étude préliminaire, cette recherche étudie l’influence de la variabilité spatiale des paramètres de résistance du sol, y compris la cohésion, l’angle de frottement et le poids unitaire sur la probabilité de défaillance. Une hypothèse simpliste de conductivité hydraulique constante dans tout le profil du sol est faite. L’analyse probabiliste est réalisée pour un événement pluvieux extrême. Les résultats sont comparés à des analyses dans lesquelles l’hypothèse de paramètres pédologiques variables aléatoires est faite. Les résultats montrent une réduction de la valeur de la probabilité totale de rupture pour le cas avec des paramètres de résistance du sol spatialement variables par rapport au cas avec des paramètres de sol aléatoires. Le cas spatialement variable décrit mieux l’état pratique du site.

1

INTRODUCTION

Studies have shown that water infiltration, commonly occurring during rainfall, can have a significant impact on the stability of soil embankments. Additionally, the moisture levels in the slope before rainfall can also influence the vulnerability of soil embankments to instability. Soil geotechnical characteristics, which govern the stability of earth embankments, can vary spatially due to the inherent heterogeneity of soils. Moreover, uncertainties arising from measurement errors and inadequate testing further contribute to the uncertainty surrounding the soil properties used in analyses. Therefore, it is crucial to consider all these uncertainties when evaluating the stability of embankment slope (Baninajarian et al., 2020). The surface of soil embankments interacts with the atmosphere, and the exchange of water at the soilatmosphere boundary affects soil water storage,

distribution of pore water pressure, and ultimately, the stability of the soil embankments (Pk et al., 2019). This research aims to examine how the spatial variations in soil strength parameters, such as friction angle cohesion, and unit weight, influence the likelihood of failure in the soil embankments. The study employs a probabilistic analysis that considers the spatial variability of soil parameters for an embankment subjected to an extreme rainfall event with varying initial saturation conditions. The results obtained are then compared to those from deterministic analysis and probabilistic analyses ignoring spatial variability of some soil parameters Mentioned in the following 1.1

Background

The stability of both natural and constructed soil slopes can be influenced by changes in extreme rainfall patterns

caused by climate change. For instance, embankments which are constantly exposed to atmospheric conditions are susceptible to slope failures triggered by rainfall. Baninajarian et al. in (2020), conducted a research study focusing on the impact of extreme rainfall on a soil embankment slope located in Niagara Falls, Ontario. The primary objective of their study was to develop fragility curves for the slope, considering two distinct types of failures: general failures and shallow failures. To assess the slope's stability throughout extreme events over time, the researchers employed a coupled hydro-geotechnical model as a means of evaluation. The fragility curves were developed using the First Order Second Moment (FOSM) method, and the associated reliability analyses assumed that various soil parameters were random variables. The findings from their research revealed that embankments constructed using fine materials, specifically silts, exhibit heightened susceptibility to an elevated likelihood of failures during extended rainfall events characterized by longer return periods. In 2017, Javankhoshdel et al. investigated the effect of random and spatial variability of soil strength parameters, and cross-correlation between them on probability of failure of simple slopes with cohesive and cohesive frictional soil. They performed several analyses using 2D random finite element method (RFEM) and 1D and 2D random limit equilibrium method (RLEM) to identify to compare RLEM and RFEM approaches. Cami et al., (2020) provided a comprehensive database of horizontal and vertical scale of fluctuation values obtained from various locations and materials, based on published case studies which represents the spatial correlation length essential for accurately characterizing and simulating a spatially variable field. Chash, Javankhoshdel et al. (2022) conducted a comparative study to assess the accuracy and time efficiency of the calculation process in probabilistic and stochastic slope stability analyses, specifically focusing on the RFEM and RLEM approaches. They concluded that RLEM demonstrates superior performance compared to RFEM in slope stability analyses. Additionally, they noted that RFEM's performance is highly influenced by the mesh size and field discretization required to create a spatially variable field. On the other hand, RLEM is less sensitive to these factors as it utilizes the method of slices, where adding more slices does not significantly impact computation time. Griffiths et al., (2009) investigated the probability of failure of slopes using the first-order reliability method and the random finite-element method. The results showed that simplified probabilistic analyses which do not consider spatial variability of soil properties can lead to unconservative estimates of slope failure probability and more advanced probabilistic methods are warranted. In this study, the RLEM approach is utilized to perform a probabilistic analysis using the Slide2 software, which is a 2D limit equilibrium slope stability program (Rocscience, 2023). This software is employed to conduct finite element seepage analysis for groundwater and assess the factor of safety (FOS) using LEM. Moreover, the probability of

failure (PF) in the soil embankment under rainfall events with variably saturated initial conditions is estimated. The Metaheuristic approach known as Non-circular Particle Swarm Search is used as the search method to identify the critical slip surface, while the General Limit Equilibrium/Morgenstern-Price method (Price & Morgenstern, 2015) is applied in this study (Rocscience, 2022). In this research, the shear strength of soil is only based on cohesion and frictional strength. In order to be more conservative, the contribution of suction to shear strength is neglected. Simulations are conducted considering transient flow analysis, and the factor of safety of the embankment is evaluated at various time steps. Deterministic analyses are performed to establish a benchmark for comparing the results of probabilistic analyses, demonstrating the impact of input parameter variability on the probability of failure. The probabilistic analyses aim to evaluate the probability of slope failure, by considering the inherent uncertainties associated with certain soil parameters such as friction angle, cohesion, and unit weight. Spatial variability analyses are carried out to quantify the effect of spatial variation of soil parameters within a soil mass on the probability of failure. When employing the RLEM approach for probabilistic analyses, it is necessary to define random variables and their corresponding statistical parameters. This includes determining the probability distribution function (PDF) of the variables, as well as establishing the standard deviation and scale of fluctuation in both horizontal and vertical directions. 1.2

Geometry and Materials

The embankment profile analyzed in this study is depicted in Figure 1. The embankment has a symmetrical geometry, and only half of the domain is simulated. It has a height of 8 meters with side slopes of 2H:1V. Additionally, a 3-meter width unpaved shoulder is assumed at the top of the embankment. To minimize the influence of side boundary conditions, the distance between the slope toe and the right side of the model is set to more than three times the height of the slope (Rahardjo et al., 2010). The embankment fill material studied in this research is sandy silt, hereafter referred to as "silt." The material properties considered for the silt include a saturated unit weight (𝛾) of 19 kN/m3, a friction angle (ϕ) of 27°, and a small value of 2 kPa for the effective cohesion. For simplicity, a constant hydraulic conductivity function is assumed throughout the soil profile in this study. The hydraulic behavior of unsaturated soils is characterized by the soil water characteristic curve (SWCC) and the unsaturated hydraulic conductivity function (HCF), which are determined using the van Genuchten-Mualem approach (Mualem, 1976). Figure 2 shows the Soil-water Characteristic Curves for silt based on (Baninajarian, 2020).

Figure 1. Embankment considered in this study (modified from Baninajarian, 2020)

The initial moisture condition influences the pore water pressure within the embankment, which significantly impacts slope stability. Baninajarian, (2020) evaluated slope moisture conditions by calculating the average degree of saturation within the slope area and performed statistical analyses to identify the average and maximum moisture conditions within the slope and determine critical times when the distribution of pore water pressure (PWP) poses a risk to slope stability. In the current study, longterm variations of the spatial distribution of pore water pressure (PWP) within the embankments corresponding to the 90th percentile of saturation (P90%) is used. The water table was conservatively assumed to be at the natural ground surface, located 4m below the slope toe. At the soil-atmosphere interface, a flux boundary representing an actual rainstorm pattern, known as the Chicago design storm, based on Keifer & Chu, (1957), was applied. This approach has been recommended by the Ministry of Transportation Ontario for assessing the impact of storms on drainage systems (MTO, 1997). Figure 3 illustrates the design storms used in this study. The Chicago curve, developed by (Baninajarian, 2020) is based on future Intensity-Duration-Frequency (IDF) curves for a 48-hour rainfall event with a 100-year return period in the city of Niagara Falls. However, the simulation was extended beyond the duration of the rainfall event to 60 hours due to high water retaining capacity of silt.

Figure 2. Soil water characteristic curves for silt material, used in this study (modified from Baninajarian, 2020) In this study, the variability of three crucial soil parameters: the angle of internal friction, cohesion, and unit weight are examined. Various researchers have suggested a coefficient of variation (COV) ranging from 2% to 20%. for the angle of internal friction, 10% to 50% for the cohesion, and 3% to 7% for the for the unit weight (Harr, 1984, Kulhawy, 1992, Phoon & Kulhawy, 1999, Javankhoshdel & Bathurst, 2016). In this study, a COV of 15%, 50%, and 5% are chosen for the angle of internal friction, cohesion, and unit weight, respectively. The probability distribution function chosen for ϕ, c, and 𝛾 is the lognormal distribution. The soil parameters considered in the probabilistic analyses are summarized in Tables 1, and for the probabilistic slope stability analyses, the LatinHypercube method is employed with 10000 samples. Table 1. Mean values and standard deviations of soil parameters considered in this study Parameters Friction Angle Unit Weight Cohesion

1.3

Mean value

Standard Deviation

27 19 2

4 1 1

Initial and Boundary Conditions

Figure 3. Chicago curve for 48-hour extreme rainfall – City of Niagara Falls (modified from Baninajarian, 2020) 2 ANALYSIS AND RESULTS 2.1

Seepage and Deterministic Analysis

PWP distribution at different times after the extreme precipitation event are illustrated in Figure 4. The change in the phreatic surface and pore water pressure due to the infiltration of the water into the slope are shown in the following figures. Figure 4(a) indicates the initial PWP conditions in the slope. At this time, rainfall starts and the pore water pressure in the embankment starts to increase by the infiltration as it can be seen from figure 4(b) to 4(d),

which show the PWP distribution at the peak of the rainfall, end of it, and after 60 hours respectively. Due to the low permeability of material, the PWP increases even 12 hours after the end of the rainfall. (a)

(b) Figure 5. Temporal variation of FOS obtained from the deterministic analysis 2.2

(c)

(d)

Figure 4. Spatial distribution of PWP in the slope at the (a) initial stage, after (b) 18.5 hours, (c) 48 hours, and (d) 60 hours The temporal variation of FOS of slope at various times during the rainfall event is presented in Figure 5. The saturated conditions in the slope and increase of pore water pressure due to the rainfall results in reducing the effective soil stress and soil shear strength and consequently, the factor of safety decreases. However, the variation of FOS is rather insignificant, under the rainfall, at different stages due to the higher retention and lower conduction capacity of the silt. To investigate the influence of the variation of soil input parameters on the probability of failure, probabilistic analyses are carried out and discussed in the following sections.

Single Random Variability Analysis (SRV)

For the probabilistic analyses, soil unit weight, friction angle, and cohesion are considered random variables which their values are as presented earlier in Table 1. The probability of slope failure and values of mean FOS at different stages obtained from SRV analysis are shown in Figure 6. This figure shows an insignificant change in the mean FOS over time. The mean FOS changes from around 1.275 at initial hours of rainfall to almost 1.26 after 60 hours thereafter. These values are relatively close to values obtained from deterministic analyses. In the current study, 1.10 is considered as the target FOS for the estimation of PF. The variation of probability of failure over time is shown in Figure 7. It shows the probability of failure obtained from SRV analyses varies from around 20% to 22%. It is noticeable that these values are relatively high considering an acceptable mean FOS of about 1.3 and based on target PF = 0.01% for permanent well-engineered and constructed unreinforced soil slopes and embankments (Silva et al., 2008). This indicates that ignoring the spatial variability of soil properties results in an unrealistically high probability of failure. A comparison between the results from current study based on the critical FOS of 1.0 and previous study conducted by Baninajarian et al., (2023) shows that although the value of mean factor of safety obtained from probabilistic analyses in both studies are the same, the PF increases to around 30%. This shows even a low value of soil cohesion plays a critical role on the probability of slope failure. 2.3

Stochastic Analysis

2.3.1 Spatial Variability Analysis To consider spatial variability of soil parameters, horizontal correlation of 33.2, and vertical correlation of 2.08 as recommended by Cami et al. (2017) are considered in the analyses. The variation of mean FOS and

PF over time obtained from stochastic analysis is presented in Figure 6 and Figure 7. The stochastic analyses yield a probability factor (PF) ranging from 6% to 8% for a factor of safety (FOS) approximately equal to 1.23, which is significantly lower than the PF obtained from SRV analyses. Nevertheless, when comparing these findings to the results of a prior study conducted by Baninajarian et al. (2023), it is evident that considering cohesion in the analyses leads to a more pronounced increase in the PF. 2.3.2 Spatial Variability Analysis Considering CrossCorrelation Slope stability analyses using cross-correlated soil properties can predict reasonable probabilities of failure. In this study, a negative cross-correlation of -0.5 between cohesion and friction angle and a positive cross correlation of 0.5 between friction angle and unit weight are considered (Javankhoshdel & Bathurst, 2016). The negative cross correlation between friction angle (Phi) and cohesion can be observed from Figure 8 which illustrates the friction angle and cohesion contours for a specific slip surface. It can be seen that in the area with greater Phi value, there exists a lower cohesion value. As shown in Figure 6 and Figure 7, mean FOS resulted from stochastic analyses considering above mentioned cross correlation coefficients is around 1.23 with a PF of about 2%. These figures indicate that considering the mean factor of safety of around 1.2, stochastic analyses without cross-correlated variables lead to a relatively high probability of failure compared to the analyses with crosscorrelated variables.

Figure 7. Variation of PF over time - SRV Analysis and Stochastic Analysis

Figure 8. Friction angle and cohesion contours for a specific slip surface

3

Figure 6. Temporal variation of FOS over time - SRV Analysis and Stochastic Analysis

CONCLUDING REMARKS

This research aims to examine how the spatial variations in soil strength parameters, such as friction angle cohesion, and unit weight, influence the likelihood of failure in soil embankments. The study employs a probabilistic analysis that takes into account the spatial variability of soil parameters for an embankment subjected to an extreme rainfall event with different initial saturation conditions. The RLEM approach has been for the probabilistic analysis. Results showed that considering spatial variability of soil properties and the negative cross-correlation between the cohesion and friction angle will reduce the probability of failure significantly.

4

REFERENCES Baninajarian, L. (2020). Effect of Future Extreme Precipitation Events on the Stability of Soil Embankments Across Ontario. https://yorkspace.library.yorku.ca/xmlui/handl e/10315/37975 Baninajarian, L., Bashir, R., Ghassemi, A., DeSira, A., & Sangiuliano, T. (2020). Developement of fragility curves for soil embankment slopes due to future extreme rainfall events. Cami, B., Javankhoshdel, S., Lam, J., & Bathurst, R. J. (2017). Probabilistic Analysis of a Tailings Dam using 2D Composite Circular and NonCircular Deterministic Analysis, SRV Approach, and RLEM. Geo Ottawa. Cami, B., Javankhoshdel, S., Phoon, K.-K., & Ching, J. (2020). Scale of Fluctuation for Spatially Varying Soils: Estimation Methods and Values. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 6(4). https://doi.org/10.1061/AJRUA6.0001083 Griffiths, D. V., Huang, J., & Fenton, G. A. (2009). Influence of Spatial Variability on Slope Reliability Using 2-D Random Fields. Journal of Geotechnical and Geoenvironmental Engineering, 135(10), 1367–1378. https://doi.org/10.1061/(ASCE)GT.19435606.0000099 Harr, M. E. (1984). Reliability-based design in civil engineering. Henry M. Shaw Lecture, Dept. of Civil Engineering, North Carolina State University, Raleigh, NC. Javankhoshdel, S., & Bathurst, R. J. (2016). Influence of cross correlation between soil parameters on probability of failure of simple cohesive and c-φ slopes. Canadian Geotechnical Journal, 53(5), 839–853. https://doi.org/10.1139/CGJ-20150109/ASSET/IMAGES/CGJ-20150109IEQ45.GIF Javankhoshdel, S., Luo, N., & Bathurst, R. J. (2017). Probabilistic analysis of simple slopes with cohesive soil strength using RLEM and RFEM. Http://Dx.Doi.Org/10.1080/17499518.2016.12 35712, 11(3), 231–246. https://doi.org/10.1080/17499518.2016.12357 12 Javankhoshdel, S., Rezvani, M., Fatehi, M., & Jamshidi Chenari, R. (2022). RLEM versus RFEM in Stochastic Slope Stability Analyses in Geomechanics. 241–250. https://doi.org/10.1061/9780784484036.025 Keifer, C. J., & Chu, H. H. (1957). Synthetic Storm Pattern for Drainage Design. Journal of the Hydraulics Division, 83(4), 1–25. Kulhawy, F. H. (1992). On the Evaluation of Soil Properties. Geotechnical Special Publication, 31, 95–115. Malekpoor, P. S., Lopez-Querol, S., & Javankhoshdel, S. (2023). Spatial Variability of

Input Motion in Stochastic Slope Stability. Proceedings of the TMIC 2022 Slope Stability Conference (TMIC 2022), 179–198. https://doi.org/10.2991/978-94-6463-1043_17 MTO. (1997). Drainage Management Manual. Ronin House Publishing, under contract from Ministry of Transportation of Ontario, Ottawa, Ontario, Canada. Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12(3), 513–522. https://doi.org/10.1029/WR012i003p00513 Phoon, K.-K., & Kulhawy, F. H. (1999). Evaluation of geotechnical property variability. Pk, S., Bashir, R., & Beddoe, R. (2019). Effect of climate change on earthen embankments in Southern Ontario, Canada. Environmental Geotechnics, 8(2), 148–169. https://doi.org/10.1680/JENGE.18.00068 Price, V. E., & Morgenstern, N. R. (2015). The Analysis of The Stability of General Slip Surfaces. Https://Doi.Org/10.1680/Geot.1968.18.3.393, 18(3), 393–394. https://doi.org/10.1680/GEOT.1968.18.3.393 Rahardjo, H., Nio, A. S., Leong, E. C., & Song, N. Y. (2010). Effects of Groundwater Table Position and Soil Properties on Stability of Slope during Rainfall. Journal of Geotechnical and Geoenvironmental Engineering, 136(11), 1555–1564. https://doi.org/10.1061/(ASCE)GT.19435606.0000385 Rocscience, S. 2D limit equilibrium slope stability analysis. (2022). Slide2 Overview. https://www.rocscience.com/help/slide2/overvi ew Silva, F., Asce, M., Lambe, ; T William, Asce, H. M., Marr, W. A., & Asce, F. (2008). Probability and Risk of Slope Failure. Journal of Geotechnical and Geoenvironmental Engineering, 134(12), 1691–1699. https://doi.org/10.1061/(ASCE)10900241(2008)134:12(1691) Vanapalli, S. K., Fredlund, D. G., Pufahl, D. E., & Clifton, A. W. (1996). Model for the prediction of shear strength with respect to soil suction Characterization of the Mechanistic-Empirical Pavement Design Guide Input Parameters for the Resilient Modulus of Ontario Subgrade Soils View project Model for the prediction of shear. Article in Canadian Geotechnical Journal. https://doi.org/10.1139/t96-060

Seepage and stability analyses for flood embankment design Huang, Ellen & Liu, Xiteng Geotechnical Services Team, Stantec Consulting Ltd., Edmonton, Alberta, Canada Wood, Matt & Ofield, David Water Services Team, Stantec Consulting Ltd., Edmonton, Alberta, Canada ABSTRACT This paper provides a case study on the geotechnical design of a flood barrier system to reduce the risk of flooding for EPCOR’s Water Treatment Plants (WTP), located on the lower terrace of the North Saskatchewan River (NSR) in Edmonton. Earthen embankments were adopted as the main flood barrier in the site. The flood barrier was designed following the US Army Corps of Engineers (USACE) design guidelines for various loading scenarios and conditions. Coupled seepage and stability analyses were performed to evaluate the potential seepage and stability of the proposed flood embankments using GeoStudio SEEP/W and SLOPE/W programs. Based on the analysis results, the thickness and uniformity of the “impervious” blanket layer (native clay or clay fill) beneath the flood embankments is critical to seepage flow and heaving during the flood event. While the amount of seepage flow is insignificant, the uplift pressure at the downstream toe of the flood embankment could cause concerns of internal erosion and/or heaving. The USACE method treats flood as a steady state condition. In reality, due to the transient nature of the flood event, the majority of the embankment fill and foundation soil will remain unsaturated during the 1:500-year flood event. Such results and conclusions bring some discussions on the design criteria for similar flood embankments design. The case study also provides more value for future applications with similar conditions for economical design. RÉSUMÉ Cet article présente une étude de cas sur la conception géotechnique d'un système de barrière anti-inondation visant à réduire le risque d'inondation pour les usines de traitement des eaux (WTP) d'EPCOR, situées sur la terrasse inférieure de la rivière Saskatchewan Nord (NSR) à Edmonton. Des remblais en terre ont été adoptés comme principale barrière antiinondations sur le site. La barrière antiinondation a été conçue conformément aux directives de conception de l'US Army Corps of Engineers (USACE) pour différents scénarios et conditions de charge. Des analyses couplées d'infiltration et de stabilité ont été effectuées pour évaluer l'infiltration potentielle et la stabilité des digues proposées en utilisant les programmes GeoStudio SEEP/W et SLOPE/W. D'après les résultats de l'analyse, l'épaisseur et l'uniformité de la couche de couverture "imperméable" (argile naturelle ou remblai d'argile) sous les digues d'inondation sont critiques pour le flux d'infiltration et le soulèvement pendant l'événement d'inondation. Bien que la quantité de flux d'infiltration soit insignifiante, la pression de soulèvement au niveau du pied aval de la digue d'inondation pourrait causer des problèmes d'érosion interne et/ou de soulèvement. La méthode de l'USACE traite l'inondation comme une condition de régime permanent. En réalité, en raison de la nature transitoire de l'événement d'inondation, la majorité du remblai de la digue et du sol de fondation restera non saturée pendant l'événement d'inondation de 1:500 ans. Ces résultats et conclusions amènent des discussions sur les critères de conception pour les digues d'inondation similaires. L'étude de cas fournit également plus de valeur pour les applications futures avec des conditions similaires pour une conception économique. 1

INTRODUCTION

EPCOR Water Services Inc. operates two water treatments plants (WTPs) in the City of Edmonton – Rossdale WTP and E.L. Smith WTP. Both plants are located on the lower terrace of the North Saskatchewan River (NSR) valley and can therefore be vulnerable to flooding. Due to the major flooding event across Alberta in 2013, EPCOR’s insurance provider FM Global was prompted to assess the Edmonton WTPs’ vulnerability to flood from the NSR. Stantec was retained by EPCOR to provide preliminary engineering design for earthen embankments at the WTPs that could protect the facilities from a 1 in 500-year flooding event. Geotechnical analyses were performed to evaluate the potential seepage and stability of the proposed earthen embankments under various conditions.

This paper takes one of the WTPs – Rossdale WTP as a case study, presents the methodology used for the geotechnical design of a flood barrier system to reduce the risk of flooding, summarizes the seepage and stability analysis results, and discusses the design criteria for similar flood embankment design. 2

SITE DESCRIPTION AND LOCAL GEOLOGY

The Rossdale WTP site is located on the north side of the NSR terrace immediately east of Walterdale bridge in downtown Edmonton, Alberta. The Rossdale WTP has been in operation for over 100 years. The plant supplies Edmonton and its surrounding areas with drinking water. The site is relatively level with a gentle slope towards south to the NSR. Majority of the site is at elevations between 623 m and 625 m. Published local geology of Edmonton (Bayrock and Hughes 1962, Kathol and

McPherson 1975) indicates that the NSR flood plain is underlain by alluvial deposits and followed by interbedded clay shale and sandstone bedrock of the Edmonton Formation. The alluvial deposits consist predominately of fine to medium grained sand with some silt and clay. Coarse sand and gravel is present in many places, especially in or close to the river channel. Based on the historical boreholes data, the general soil stratigraphy at the site consists of various fills overlying 4 m to 7 m thick alluvial deposits overlying interbedded bentonitic shale bedrock. The alluvial deposits typically include silty clays and/or clayey silts, sands, and gravel in descending order. Bedrock is expected to be encountered approximately 9 m to 14 m below existing ground surface (an approximate elevation of 612 m to 615 m). Based on the historical borehole data, the groundwater level was at approximately 7 m to 10 m below ground surface (bgs) (corresponding elevation of 616 m to 614 m) within the sand/gravel layer. 3

GEOTECHNICAL DESIGN OF FLOOD EMBANKMENT

Two types of flood protection system - earth embankment and pile supported concrete wall are proposed. Earthen embankments are proposed as the main flood barrier at the site. Flood walls are proposed in areas where spatial constraint exists. The site plan and proposed flood embankment locations of the Rossdale WTP site are shown in Figure 1. The top of the flood embankment is set to protect a 1:500-year flood plus a 1.0 m freeboard, which equals to approximate elevation of 625.6 m at this site. In general, the proposed earthen embankments will be constructed on the existing ground with a crown width of 3.0 m and 3H:1V side slopes. The flood barrier was designed following the USACE design guidelines for various loading scenarios and conditions. Coupled seepage and stability analyses were performed to evaluate the potential seepage and stability of the proposed flood embankments using GeoStudio SEEP/W and SLOPE/W programs.

3.1

Seepage analyses

Seepage analyses were conducted to estimate location of the phreatic surface, potential seepage to landside (plant side), and uplift pressure on the base of top stratum at the landside toe of the embankments. A 2-Dimensional (2-D) finite element seepage program Seep/W 2019 was used to assess event-based seepage potential on the preliminary embankment design. The analyses focused on the local (fluvial) aquifer and the potential for seepage immediately under or on the plant side of the embankment. Seepage analyses were performed on three selected representative cross sections (namely A-A’, B-B’, C-C’) at the site (Figure 1). The results of Section B-B’ were presented and discussed in the paper. The thickness of top stratum (blanket layer) and underlying alluvial deposits were defined by reviewing the historical borehole logs in the surrounding area of. The 2-D finite element model was analyzed for both steady state seepage and transient seepage conditions. For steady state seepage analyses, the peak flood level based on the 1:500-year flood was applied to the upstream side (river side) of the embankment as a total head boundary. Free seepage was allowed on the plant side. For transient seepage analyses, the 1:500-year flood design hydrographs were used as boundary condition (Tetra Tech 2020). The initial groundwater conditions were based on regional groundwater levels obtained from previous field investigations. In the seepage analyses, both saturated and unsaturated hydraulic conductivities were used to properly account for water migration in unsaturated zone above phreatic surface and to better predict the phreatic surface. For transient analyses, water storage functions were also assigned to each soil unit above the bedrock. The saturated hydraulic conductivities used in the analyses are summarized in Table 1. They generally represent upper bound values of previous field test results (Stantec 2021) for conservatism. The unsaturated hydraulic conductivities and water storage functions were estimated based on the soil gradation curves (Seep/W 2012, Benson 2014). Table 1. Saturated Hydraulic Conductivities

1

Material Type

Horizontal Hydraulic Conductivity kx, m/s

Vertical Hydraulic Conductivity kz, m/s

Embankment Fill1

5.0 x 10-9

1.0 x 10-9

Clay Fill

1.0 x 10

2.0 x 10-9

Silt

1.0 x 10

2.0 x 10-7

Silty Sand

2.0 x 10

2.0 x 10-5

Sand and Gravel

1.0 x 10

1.0 x 10-4

Bedrock

1.0 x 10

1.0 x 10-11

-8 -6 -5 -4 -10

medium to high plastic clay with plastic index between 20 and 40

Seepage analysis results of Section B-B’ are presented in Figure 2 and Figure 3 and summarized in Table 2 below.

Figure 1. Rossdale WTP Site Plan and Flood Embankments Location

Figure 2. Total Head Contour Peak Flood - Transient Flow

Figure 3. Total Head Contour Peak Flood - Steady State Seepage Table 2. Seepage Analysis Results during Peak Flood Scenario

Landside Seepage (m3/d/m)

Uplift Pressure (kPa)

Steady State Seepage 2.0 x10-4

19.9

Transient Flow

0

0

From the seepage analysis results, under steady state flood condition, the estimated seepage to plant side was very small, up to 2.0 x10-4 m3/day per linear meter of the embankment. Furthermore, the weight of the surficial “impervious” soil stratum was greater than 1.5 times the calculated uplift pressures underneath the surficial soil. Therefore, no drainage or pressure relief is required on the plant side. Under transient condition, no potential seepage or uplift issues occurred on the plant side under the 1:500year flood event. 3.2

Slope stability analyses

Slope stability analyses were carried out using a 2-D limit equilibrium computer program Slope/W 2019. The selected sections and corresponding finite element models developed in the seepage analyses were used for slope stability analyses. The pore-water pressure conditions for stability analyses were imported from the seepage analysis results (i.e., coupled stability and seepage analysis).

For the proposed flood mitigation earthen embankments, various loading conditions to which embankments and foundations may be subjected were considered in the slope stability analyses. According to USACE EM-1110-2-1913 the loading conditions to be considered are as follows: Case I - End of construction. This case represents undrained conditions for impervious embankment and foundation soils, i.e., excess pore water pressure is present under additional loading. Analysis for the End of construction case was considered by using both total stress approach (i.e., undrained shear strength) and effective stress approach with excess pore pressure. For each loading situation, only the case with lowest resulting Factor of Safety (FOS) are presented. Case II – Peak flood. This condition represents the water level reaching peak flood. Plant side embankment slope stability was examined using the following two pore water pressure regimes: • Case IIA: steady state seepage during peak flood which means the peak flood stays long enough so that steady state seepage occurs. This is the case required by USACE EM-1110-2-1913.

Case IIB: transient flow under 1:500-year return period flood event. Under transient flow, the embankment and foundation soil may not be saturated during flooding and is considered more representative of likely conditions. Case III - Rapid drawdown. Upstream slope failures can result from a rapid drawdown of the water level, whereby the river level falls faster than the soil can drain. This causes the development of excess pore water pressure in the embankment. The pore pressure that might remain or develop in the embankment during the rapid drawdown of the reservoir was estimated through seepage analysis, and the result was applied to analyze the stability of the upstream slope. Similar to peak flood, two pore water pressure regimes were used in the rapid drawdown analyses: • Case IIIA: assumed water retreat in 0.5 day from steady state seepage after peak flood. This is the case required by USACE EM-1110-2-1913. • Case IIIB: assumed water retreat in 0.5 day from transient flow after 1:500-year return period flood event. Again, this case has low pore pressure in general and is considered more representative of likely conditions. •

Case IV - Earthquake. Earthquake loading was modelled as a constant force in the pseudo-static (seismic) analysis. The effects of earthquake shaking in the horizontal and vertical directions were modelled using seismic coefficients kh and kv, respectively. Based on the NBC-AB (2019), the Peak Ground Acceleration (PGA) for this site was estimated to 0.083. The coefficient kh was taken as 50% of the PGA at the ground surface. A vertical seismic coefficient kv equal to 2/3 of kh was used. The kv acting in both upward and downward directions were analyzed, and the lowest factor of safety was selected. The minimum required safety factors for the slope stability analyses under the aforementioned loading conditions are defined according to the USACE EM-11102-1913 and Canada Dam Safety Guidelines (2013 Edition). Soil parameters used for slope stability analysis are presented in Table 3. The values were estimated based on the field and laboratory test results from previous investigations (Stantec 2021) and published data of similar materials. Slope stability analyses were performed on the representative cross sections. River side and plant side slope stabilities were examined. Results of the slope stability analysis of Section B-B’ are presented in Figures 4 to 9 and summarized in Table 4 below.

Table 3. Soil Parameters for Slope Stability Analysis Material Type

Soil Model

Unit Weight, (kN/m3)

Cohesion, c’ (kPa)

Friction Angle, Excess Pore φ’ (°) Pressure, Ru

Cu2 (kPa)

Embankment Fill

M-C

19

5

24

0

-

Clay Fill

M-C

18

2

24

0.25

30

Silt

M-C

18

0

25

0

30

Silty Sand

M-C

18

0

30

0

-

Sand and Gravel

M-C

20

0

32

0

-

Bedrock

High Strength

21

-

-

-

-

1

assumed to be impenetrable 2 undrained shear strength 1

Table 4. Summary of Slope Stability Analysis Results Sub-Case

Factor of Safety Target

Calculated

End of Construction

1.3

2.43

Peak Flood - Steady State Seepage

1.4

2.11

Peak Flood - Transient Flow

1.4

2.77

Rapid Drawdown - Steady State Seepage

1.0

1.26

Rapid Drawdown - Transient Flow

1.0

2.21

Seismic Loading

1.0

2.46

Figure 4. Slope Stability Analysis Results - End of Construction

Figure 5. Slope Stability Analysis Results - Peak Flood (Steady State Seepage)

Figure 6. Slope Stability Analysis Results - Peak Flood (Transient Flow)

Figure 7. Slope Stability Analysis Results - Rapid Drawdown (Steady State Seepage)

Figure 8. Slope Stability Analysis Results - Rapid Drawdown (Transient Flow)

Figure 9. Slope Stability Analysis Results – Seismic Based on the slope stability analyses, the calculated FOS for all cases met the targeted FOS. The designed 3H:1V side slope is considered adequate for both river side and plant side slopes from a stability perspective. 3.3

•

Summary of Analyses Results

Based on the geotechnical engineering analyses results, the proposed earthen embankments in both WTP sites can satisfy the seepage and slope stability requirement from a geotechnical perspective. The findings are summarized as follows: • Under steady state 1:500-year peak flood, negligible seepage will be encountered on the plant side of the embankment due to the presence of relatively thick (typically greater than 2.0 m) “impervious” blanket consisting of native clay, clay fill, and/or silt fill. The uplift pressure under the “impervious” blanket was counterbalanced by its self-weight and shear strength of the blanket with

•

adequate factor of safety. Therefore, no drainage and/or pressure relief measures plant side of the embankment is required. Due to the transient nature of the flood event, majority of the embankment fill and foundation soil will remain unsaturated during the 1:500-year flood. This further reinforces the unnecessity of any seepage control measures stated above. The designed 3H:1V embankment side slopes can satisfy the stability requirements for various loading scenarios specified in USACE EM-1110-2-1913.

4

CONCLUSIONS AND DISCUSSIONS

The flood embankment was analyzed and designed in accordance with the USACE Method in terms of both seepage and stability. Coupled seepage and stability analyses were conducted for the embankment design. The USACE method treats flood as a steady state condition. In reality, due to the transient nature of the flood event, the majority of the embankment fill and foundation soil will remain unsaturated during the 1:500-year flood event. From seepage and slope stability analysis results of the proposed flood embankments, it can be seen that the USACE method gives very conservative results for the proposed flood embankment design. Such results and conclusions bring some discussions on the design criteria for similar future applications with similar conditions for more economical design. 5

ACKNOWLEDGEMENTS

The authors wish to thank the EPCOR for sponsoring the Rossdale WTP Flood Mitigation Embankments Design project and their support. 6

REFERENCES

Bayrock, L.A. and Hughes, G.M. 1962, Surficial Geology of the Edmonton District. Alberta, Alberta Research Council. Benson, C.H., Chiang, I., Chalermyanont, T. et al. 2014. Estimating van Genuchten Parameters α and n for Clean Sands from Particle Size Distribution Data, Geotechnical Special Publication. February 2014. Canadian Dam Association, 2013. Dam Safety Guidelines 2007 (2013 Edition). GEO-SLOPE International Ltd. 2012. Seepage Modeling with SEEP/W - An Engineering Methodology, July 2012 Edition. Kathol, C. and McPherson, R. 1975, Urban Geology of Edmonton. Bulletin 32, Alberta Research Council. National Research Council of Canada. 2019. National Building Code of Canada Alberta Edition (NBC-AB, 2019), Volume 1. Stantec Consulting Ltd. 2021. EPCOR WTP Flood Mitigation Embankments –Geotechnical Desktop Study Technical Memorandum. File No. 110146440, March 2021 Tetra Tech Canada Inc. 2020. Technical Memo: North Saskatchewan River Flood Hydrographs and Bathymetry for Groundwater Modelling Study of Rossdale and E.L. Smith Water Treatment Plants, Edmonton Alberta, September 28,2020. File: ENG.EGEO03539-01 US Army Corps of Engineers (USACE). 2020. Engineering and Design - Design and Construction of Levees, Manual No. EM 1110-2-1913. April 2020

Analysis of Structure Damage from an Adjacent Excavation S.J. Boone, Ph.D., P.Eng. Ground Rules Engineering Inc., London, ON J.L. Carvalho, Ph.D., P.Eng. WSP, Mississauga, ON M. Kanungo, M.E.Sc., P.Eng. GHD, Mississauga, ON

ABSTRACT During construction of a water treatment plant addition, the existing structure suffered significant damage, had to be removed from service and a forensic investigation ensued. Ground and structure responses to the adjacent excavation were evaluated using analytical methods and finite difference numerical modelling. Input parameters for the soil constitutive model were calibrated to laboratory data and preloading responses of the site predating construction of the original treatment plant. The numerical model simulated the entire construction history of the existing building and neighbouring excavation. The numerical modelling, coupled with analytical evaluation of other contributing ground displacement mechanisms, corresponded to displacement measurements and remedial grout volumes, and provided valuable insight into issues that are vital in selection, design, and construction of excavation support systems. RÉSUMÉ Lors de la construction d'une usine de traitement des eaux, la structure existante a subi des dommages importants, a dû être retirée du service et une enquête médico-légale s'en est suivie. Les réponses du sol et de la structure à l'excavation adjacente ont été évaluées à l'aide de méthodes analytiques et d'une modélisation numérique aux différences finies. Les paramètres d'entrée pour le modèle constitutif du sol ont été calibrés sur les données de laboratoire et les réponses de préchargement du site avant la construction de la station de traitement d'origine. Le modèle numérique a simulé tout l'historique de construction du bâtiment existant et des fouilles avoisinantes. La modélisation numérique, associée à l'évaluation analytique d'autres mécanismes de déplacement du sol contributifs, correspondait aux mesures de déplacement et aux volumes de coulis correctifs, et a fourni des informations précieuses sur les problèmes vitaux dans la sélection, la conception et la construction des systèmes de support d'excavation. 1

INTRODUCTION

During construction of a potable water treatment plant addition, the existing facility suffered significant damage, had to be removed from service and a forensic investigation ensued. Ground and structure responses to the adjacent excavation were evaluated using analytical methods and finite difference numerical modelling. Input parameters for the soil constitutive model were calibrated to laboratory data and preloading responses of the site predating construction of the original treatment plant. The numerical model simulated the entire construction history of the existing building and neighbouring excavation. The numerical modelling, coupled with analytical evaluation of other contributing ground displacement mechanisms, corresponded to displacement measurements and remedial grout volumes, and provided valuable insight into issues that are vital in selection, design, and construction of excavation support systems. 2

SITE & CONSTRUCTION HISTORY

Development of the water treatment plant site evolved over more than a century, initially using a gallery of intake pipes buried in the natural sand and gravel of a former river meander. A new treatment facility was built on the site in the late 1990s (see Figure 1). A deep layer of saturated,

loose, laminated silt, clay and fine sand at the site prompted a preloading program to control settlement and allow use of a relatively shallow and rigid raft foundation. Preload settlement was measured for about 12 weeks; however, monitoring was started about five days after fill placement started to allow for protection of monitoring instrument cables, thus early ground responses were missed. Measured settlement remained unchanged after the first 45 days. After removing the preload, the treatment facility was constructed where the southern two thirds of the building housed treatment tanks and the northern third included process equipment rooms and a three-story open equipment bay. The building and tanks were constructed as an integral structure of reinforced concrete walls, floors, and roofs (Figure 1). Until the planned plant expansion, an earth berm surrounded three sides of the chorine contact chamber structure. Six key events in building the existing plant are summarized in Table 1 (Stages 1 through 6). In the late-2000s construction began on a major facility expansion immediately beside the existing plant. The facility addition required a 5.5 m deep excavation adjacent to the chlorine contact chambers (water storage tank) and the new structure and internal equipment was to be supported on driven H piles. Interlocking 10 m long Z60 steel sheets were driven about 1 m away from and parallel to the exterior western wall of the chlorine contact chamber. Soil and rock anchors provided resistance to

Figure 1. Profile through existing treatment plant, area of addition and ground conditions. Inset illustrates treatment plant structure in northern third of building. horizontal earth and surcharge pressures. Steel HP250x85 piles were driven at 2.4 m centers, inboard of and abutting the sheets, intended to resist the vertical load component from the anchors. Interlocking sheet piles were also used to support a smaller excavation about 25 m west of the existing facility. Score marks on the north and west concrete walls of the chlorine contact chamber were established for elevation monitoring points once the earth berm was removed. The temporary surveying benchmark was located on an exterior concrete pad near the north side equipment bay door frame. Shortly after the lower level of anchors was installed from within a narrow “notch” excavation (Stage 15), plant operators heard “two loud bangs” emanating from somewhere within the existing structure. The following morning, personnel found leaking cracks and a void beneath the western edge wrapping around the north and south sides of the chlorine contact chambers. About two weeks later, similar cracking or “bangs” were heard, leaks worsened, and new cracks opened. The plant owner initiated a building structural inspection and forensic investigation. A supplementary instrumentation and monitoring program was initiated immediately by the contractor following the damage event. Instruments included precision survey targets on the sheet piles, the supplementary H-piles, the chlorine contact chamber, and other areas of the structure, vertical and horizontal electrolevels, tilt meters, extensometers, and inclinometers. Key stages during the plant expansion are also summarized in Table 1 (Stages 7 through 17). The last stage considered as part of the forensic analysis was when the chlorine contact chamber was unloaded to help mitigate further damage to the overall structure.

Table 1. Key stages of site development & construction Stage 1 2 3 4 5 6 7 8 9

3

Description Open site Preloading Remove preload Construct plant Fill tanks Construct berm Remove berm Excavate support sump bench Drive sheet piles

Stage 10 11 12 13 14 15 16 17

Description Partially excavate Dewater Deepen sump Top anchors Install H piles Excavate notch Lower anchors Drain contact chamber tanks

SUBSURFACE CONDITIONS

In the mid-1990s, ground conditions were investigated with 12 boreholes (two of which were in the structure area) and conventional standard penetration testing (SPT), three test pits, dynamic cone penetration tests, one flat-plate dilatometer (DMT) sounding, 10 grain size distribution tests, one oedometer test, and two direct shear tests. These explorations identified the stratigraphy of sand and gravel fill (reworked local materials), natural sand and gravel, fine-grained deposits (silt, clay and fine sand) and bedrock as illustrated in Figure 1. In the late 2000s, two different geotechnical consultants completed investigations at this site for the treatment plant expansion. The first investigation included five boreholes, none of which penetrated the bedrock and the second consisted of two boreholes in the plant expansion area, one of which included rock coring. Laboratory testing was limited to water content determinations. Figure 1 illustrates the subsurface conditions at the treatment plant and addition.

Immediately following the damage, the owner initiated a subsurface investigation consisting of eight boreholes and eight grain size distribution tests, with the boreholes scattered around the existing facility and new addition footprint. Another investigation by the contractor included pre-bored Ménard pressuremeter tests (PMT) in the finegrained deposit within four boreholes, two borehole shear tests (BST) in the fine-grained soils, three boreholes with conventional standard penetration testing, and flat-plat dilatometer testing at five locations. Laboratory testing for both investigations consisted of water content determinations. Later, during the forensic investigation, two additional boreholes were completed, one of which included large volume sampling of the native and fill sand and gravel and a porous tip piezometer installed in the fine-grained deposits. Piezocone penetration testing (CPTu) was also conducted through the saturated fine-grained soils within which nine pore water pressure dissipation tests were completed. Laboratory testing included natural water content determinations, mechanical and hydrometer grain size distribution analyses, two oedometer tests and four isotropically consolidated undrained triaxial compression tests on thin-wall tube samples, three large box direct shear tests and a maximum-minimum density test of reconstituted samples of the sand and gravel. Table 2 summarizes basic geotechnical properties of the strata as derived from the field and laboratory testing.

Table 2. Summary of geotechnical parameter values Parameter Sand & Gravel Silt, Clay & Sand SPT 'N' Value 12-52 7-12 Gravel (%) 46-71 0 Sand (%) 24-42 0-9 Silt (%) 69-90 5-12 Clay (%) 6-25 Water content (%) 2-25 13-42 Liquid Limit 18-39 Plastic Limit 13-18 Plasticity Index np-23 Su (kPa) 70-240 ' (degrees) 41-42.5 30-40 Dry density, d (kN/m3) 19-21.5 Relative density, Dr (%) 35-65 Wet density, w (kN/m3) 21-22 16-20 ch x10-1 cm/s 2 to 7 cv x10-4 cm/s 6-23 OCR 1.7-4 Cr 0.005-0.05 Cc 0.05-0.37 Su = undrained shear strength; = effective stress angle of internal friction; cv = horizontal coefficient of consolidation; cv = vertical coefficient of consolidation; OCR = overconsolidation ratio; Cr = recompression index; Cc = virgin compression index; np = non-plastic

4

NUMERICAL MODELING

Figure 2. Examples of calibrating numerical model input parameters to site-specific laboratory and field testing.

4.3

Selection & calibration of geotechnical input parameters

To overcome biases in forensic numerical analyses when the end-performance is known (e.g., Lambe 1973; Boone 2005), input values to the numerical model were calibrated to laboratory oedometer and triaxial test results, with interpretation of field variability based on site-specific correlations to indicator parameters of water content and CPTu tip resistance (see examples in Figure 2). The calibrated FLAC input data was then used to simulate site preloading and compared to available monitoring data (Figures 2 and 3). 4.4

Figure 3. Simulation of preload and comparison to measured settlement. 4.1

Model

The FLAC (Itasca 2008) finite difference software was used for this analysis for its ability to model large strains and coupled mechanical and fluid flow problems. The model geometry was based directly on drawings of the existing pre-treatment structure, preload construction, and shoring using a uniform square mesh of 0.25 m, adjusted to triangular elements at any angled zones. The boundaries were set at more than 3 times the total soil thickness to the nearest structural element. Vertical boundaries permitted vertical displacement but prevent horizontal displacement. The bottom boundary prevented vertical and horizontal displacement to simulate a rough soil-rock contact so that failure paths would be forced through the soil mass. The pore water pressure profile at the vertical and bottom boundaries was fixed to represent the pre-construction ground water conditions and cross-boundary flow was permitted. 4.2

Soil Constitutive Behaviour

The cap-yield model was used to simulate soil behaviour since it considered non-linear stress-strain soil behaviour, dilation or contraction of the soil under loads and coupled fluid-flow and mechanical stress-strain (pore water pressure) responses to loading. The model included strainhardening/softening behaviour, an elliptical volumetric yield surface and deformation modulus values defined as functions of confining stress and simulation of pore water pressure responses to contraction or dilation under shear or compression stresses. Key geotechnical input 𝑒 parameters included the tangent shear modulus, 𝐺𝑟𝑒𝑓 , slope of isotropic laboratory mean normal stress-strain 𝑖𝑠𝑜 𝑜𝑒𝑑 response, 𝐾𝑟𝑒𝑓 , oedometer modulus, 𝐸𝑟𝑒𝑓 , all at the reference effective pressure of 100kPa, and an exponent controlling the confining stress-dependent change in modulus values, m. Site stratigraphy was modelled by a system of 0.5 m thick layers to account for geotechnical parameter variability.

Structural Components

All structural members, except the tiebacks, were modelled as two-dimensional beam elements. The tiebacks were modelled using one-dimensional axial elements attached to the grid along the bond zone length so that cable element forces could be tracked. The reinforced concrete structure was modelled by simplifying the beam and column construction along two east west planes, representative of the northern third and southern two thirds of the facility. The simplified beams were all considered to be rigidly connected and capable of moment transfer. To avoid unnecessary complications associated with self-weight beam bending the beams were considered weightless. Between the beams and columns, walls were modelled as materials with strength and deformation properties consistent with reinforced concrete with deformation modulus values reduced proportional to the cross-sectional area perpendicular to the model plane. The simulated density of the walls was chosen to ensure appropriate contact stresses between the building structure and soil. Based on an evaluation of building structure dimensions and materials the raft-soil contact pressure was expected to be 55 to 60 kPa. In those areas with water tanks, the contact pressure was estimated to be about 135 kPa, with an overall average contact pressure (north and south building sections) estimated to be about 105 kPa. Interfaces between the beam elements and ground were modelled with stiffness of at least one order of magnitude greater than the soil modulus values to avoid inappropriate influence on overall behaviour. The interfaces were also modeled to be incapable of resisting tensile forces but capable of transmitting shear forces so that separations between the structure and ground could occur depending on relative displacement and stiffness characteristics. In one of the modelling scenarios, the chlorine contact chamber was permitted to rotate at a joint in the raft foundation at the junction with the remaining treatment plant building to understand the influence of the multi-story open equipment bay that was not as heavily interconnected with the chlorine contact tank as the southern 2/3 of the structure. 4.5

Modelling Results

Results of the numerical modelling are illustrated in Figures 4 and 5. The open equipment bay within the northern part

Figure 4. Simulation of chlorine contact chamber exterior wall settlement compared to survey monitoring measurements. Coloured circles and numbers refer to the corresponding line colours and the stages of construction in Table 1. of the structure likely acted as a structural fuse such that when stresses exceeded the tensile strength of the few beams and upper part of the raft, the structure no longer redistributed stresses eastward and the chlorine contact chamber section became free to rotate. Prohibiting formation of a structural joint for the southern part of the structure and allowing joint formation on the northern part clearly matched well with the available survey data. 5

VOIDS

While the numerical modelling, calibrated to past site performance, provided a reliable method for simulating and tracking performance of the excavation and building at many stages, it did not address other observations at the site. Voids were identified beneath the exterior perimeter of the chlorine contact chamber west wall and the western half of the north and south walls. Once the chlorine contact chamber was emptied (Stage 17), the floor was cored in multiple locations to define the extent of the voids and allow filling with low-mobility grout. Figure 6 illustrates the locations that accepted grout and showed communication between holes and those that did not. In some instances, void thicknesses on the order of 25 mm were identified in holes that eventually did not accept grout. It was concluded that the inferred void height was associated with disturbances from the core hole drilling and that the void did not propagate further from the hole. The grouted area shown in Figure 6 was based on assuming that grout travelled half the distance between holes that accepted grout and those that did not. Grout communication was confirmed among all holes shown as accepting grout. One additional large volume void was identified during grouting over a location where a tieback was drilled through one of the buried and abandoned water intake pipes. Ground losses were not evident during the tieback drilling other than loss of drilling fluid circulation and excessive grout injection. Given the patterns of void thickness and grout volumes, the cause of the voids was suspected to be vibration-induced densification of the granular soils. Limited vibration monitoring of the concrete structure indicated that sheet pile driving likely resulted in typical structural peak particle velocities (ppv) between at least 5

Figure 5. Simulation of east-west chlorine contact chamber settlement profile (top) and lateral displacement of the sheet pile support system (bottom). Coloured circles and numbers refer to the corresponding line colours and the stages of construction in Table 1. and 25 mm/s with many instances well over this range to as much as 112 mm/s. Using the methods of Whyley and Sarsby (1992) and Hope and Hiller (2000) and equipment data for the ICE 812 vibratory and B-4505 impact hammers, ground ppv values were estimated to be as much as 100 mm/s at the piles to about 50 mm/s at the closest point of the chlorine contact chamber. Vulnerability of the soils to vibration-induced liquefaction and densification was evaluated using CPTu and SPT data and methods of Massarsch (2002), PDI (2010) and Taylor (2011). The evaluation determined that pile-drivinginduced liquefaction and settlement (hazard extent) was probable for about 50% of the soil profile depth within a radial zone of 4 to 8 m from each pile. Based on the work of Clough and Chameau (1980) and Drabkin et al. (1996), settlement at the boundary of the chlorine contact chamber from driving individual sheet piles was estimated to be on the order of 35 to 70 mm. Driving the supplementary H piles and building piles exacerbated settlement. Figure 6 illustrates contours of likely cumulative pile-driving-induced ground settlement based on these analyses. As a comparison, the temporary benchmark used by the contractor for monitoring surveying, located on a concrete pad outside the north side equipment door, settled 9 mm during the time that the sheet piles were installed and prior to driving the structural piles.

Figure 6. Plan view of physical features of chlorine contact chamber, excavation support, exploratory core holes, grout injection results, and estimated settlement induced by pile driving. Contours of settlement shown in 25 mm increments from zero (green) to >150 mm. Numbers next to hole markers indicate measured thickness of voids (mm). While the analysis of vibration-induced densification of the ground was inexact, the results were rational and consistent with the injected grout volumes, measurements of void thickness (except one area), grout communication between injection holes, and the temporary benchmark settlement in both pattern and magnitude. This mechanism was thus considered the likely cause of the perimeter void around the otherwise heavily loaded chlorine contact chamber.

• • •

• 6

COMPLICATING FACTORS

During the forensic investigation, multiple other problems related to the shoring design and initial post-failure investigations were identified: • • • • •

the first design of the shoring system did not account for the surcharge pressures of the existing treatment facility the wale-to-sheet pile distance was smaller than the H pile depth so the wales had to be cut and reinforced to permit supplementary H pile driving the supplementary H piles were not connected to the wales or wall at the time the upper level of anchors were stressed or when damage occurred the buried and abandoned pipes were not identified as part of the contract information the temporary benchmark used for surveying was too close to the ground and building that were subject to displacement and was ultimately within the zone of influence and moved

• •

•

there was no monitoring of the shoring system or adjacent ground prior to the damage event DMT data were not reliable, and settlements estimated using Schmertmann’s (1987) methods would have resulted in values 10 times those measured BST tests could not discern pore-water pressure effects on the results and the tests were not conducted slowly to reduce pore water pressure influences on the results PMT holes were not controlled well, pore water pressure effects and interpretation methods were inappropriate for the highly layered and silty ground conditions hammer systems (e.g., automatic, rope and cathead) used for obtaining SPT data were not consistently identified unidentified “corrections” were applied to SPT data on the borehole records from one consultant, obfuscating field data and prohibiting rational comparison to other SPT data other numerical models used to explain the site behaviour used traditional finite-element software, linear-elastic, perfectly plastic Mohr-Coulomb constitutive models (ignoring unloading and small strain responses), and modelled the building as a simple, continuous rigid or flexible beam – and none matched the observed conditions

These issues led to a variety of alleged causes of the problems experienced at this site, such as:

• • • • •

the buried and abandoned water intake pipes were the primary source of settlement dewatering, necessitated by the design, was the source of settlement ground conditions were different (softer) than expected construction of the berm surrounding the north, west and south sides of the chlorine contact chambers created the observed voids removal of the berm, necessitated by design, resulted in rebound of the structure and the observed cracking

None of these other alleged causes matched the patterns and sequences of displacement and damage; however, the alleged causes prompted the need for detailed evaluations to discern and weigh the importance of each in the overall forensic investigation. 7

CONCLUSIONS

The detailed numerical modelling, calibrated to sitespecific laboratory and field testing and a prior site preload enabled the full sequence of site development to be reliably simulated. Key to this work was developing a rigorous model of the building structure and adapting it to recognize the different stiffness and weak point in the northern third of the structure. The results of both modelling scenarios (south and north building models) explained the different building settlement responses identified by the surveying as well as the rationale for the loud “bangs” heard when concrete fractured in tension. Coupled with analytical evaluation of vibration-induced settlement, the work described and summarized in this paper fully explained the complex responses of the building, shoring and ground, and was supported by limited monitoring data. With results available for each construction stage, many of the alleged causes of building damage could be evaluated and ultimately dismissed. Boone, S.J. 2005. General Report: Deep Excavations. Fifth International Symposium on Geotechnical Aspects of Underground Construction in Soft Ground, International Conference on Soil Mechanics and Geotechnical Engineering, Balkema, 81 – 92. Clough, G.W. and Chameau, J.-L. 1980. Measured Effects of Vibratory Sheetpile Driving. Journal of the Geotechnical Engineering Division, ASCE, 106(10), 1081 – 1099. Drabkin, S., Lacy, H., and Kim, D.S. 1996. Estimating Settlement of Sand Caused by Construction Vibration. Journal of Geotechnical Engineering, ASCE, 122(11), 920 – 928. Hope, V.S. and Hiller, D.M. 2000. The prediction of groundborne vibration from percussive piling. Canadian Geotechnical Journal, Vol. 37, 700 – 711. Itasca 2008. FLAC2D user manual. Itasca Consulting Group Inc., Minneapolis, Minn. Lambe, W.T. 1973. Prediction in soil engineering. Geotechnique, 23(2), 149 – 202

Massarsch, K.R. 2002. Effects of Vibratory Compaction. Vibratory Pile Driving and Deep Soil Compaction – TRANSVIB2002, Proceedings of the International Conference on Vibratory Pile Driving and Deep Soil Compaction, Belgium, Holeyman et al. eds., Balkema, 33 – 42. Pile Dynamics Inc. (PDI). 2010. GRLWEAP™ - Wave Equation Analysis of Pile Foundations. Pile Dynamics Inc. Schmertmann, J. 1987. Dilatometer to Compute Foundation Settlement. Use of In Situ Tests in Geotechnical Engineering, Geotechnical Special Publication No. 6, ASCE, 303-321. Taylor, O-D., S. 2011. Use of an Energy-Based Liquefaction Approach to Predict Deformation in Silts Due to Pile Driving. Ph.D. Dissertation, University of Rhode Island, 303 pp. Whyley, P.J., and Sarsby, R.W. 1992. Groundborne vibration from piling. Ground Engineering, 32–37. Woods, R.D. and Sharma, V.M. 2004. Dynamic Effects of Pile Installations on Adjacent Structures, International Edition. A.A. Balkema Publishers, New York, 163 pp.

Understanding the influence of boundary conditions and thermophysical soil parameters on thermal modelling in permafrost regions Konstantin Ozeritskiy Department of Civil Engineering – University of Calgary, Calgary, AB, Canada Jocelyn L. Hayley Department of Civil Engineering – University of Calgary, Calgary, AB, Canada Alexey Gunar Department of Geocryology – Lomonosov Moscow State University, Moscow, Russia ABSTRACT This article uses an approach which was developed at the Department of Geocryology of Lomonosov Moscow State University. This approach provides a method to calibrate the thermal boundary condition on the ground surface using borehole soil temperature measurements in combination with thermophysical soil parameters. This article presents thermal calculations using one-dimensional heat conduction. Some errors may occur during surveys when determining the thermophysical properties of soils and surface boundary conditions. The paper examines the influence of these errors on simulated thermal fields. Suggestions for minimizing the influence of errors in the thermal modelling arising from the thermophysical soil properties are suggested. The performed calculations identify that the quality of boundary conditions affects the result of thermal modelling more than the quality of thermophysical properties. In addition, to conduct more accurate thermal modelling, it is necessary to take a more detailed approach to the characterization of snow and vegetation covers. RÉSUMÉ Cet article utilise une approche qui a été développée au Département de Géocryologie de l'Université d'État de Moscou Lomonossov. Cette approche fournit une méthode pour étalonner la condition limite thermique à la surface du sol en utilisant des mesures de température du sol dans les trous de forage en combinaison avec des paramètres thermophysiques du sol. Cet article présente des calculs thermiques en utilisant la conduction de chaleur unidimensionnelle. Des erreurs peuvent survenir lors des enquêtes pour déterminer les propriétés thermophysiques des sols et les conditions limites de surface. Le document examine l'influence de ces erreurs sur les champs thermiques simulés. Des suggestions sont proposées pour minimiser l'influence des erreurs dans la modélisation thermique découlant des propriétés thermophysiques du sol. Les calculs effectués montrent que la qualité des conditions limites affecte davantage le résultat de la modélisation thermique que la qualité des propriétés thermophysiques. De plus, pour réaliser une modélisation thermique plus précise, il est nécessaire d'adopter une approche plus détaillée de la caractérisation des couvertures de la neige et de la végétation. 1

INTRODUCTION

The development of territories with permafrost is inevitably accompanied by significant environmental disturbances, changes in the thermal regime of soils, and geocryological conditions, leading to negative cryogenic processes that have the potential to cause damage to infrastructure. An accurate geotechnical forecast makes it possible to develop preventive measures to mitigate negative consequences and improve the quality of design solutions necessary for the development of territories and the preservation of a safe ecological state of the environment. One of the main components of the geotechnical forecast is thermal modelling. Setting the appropriate initial and boundary conditions is critical to accurately model the thermal behaviour of permafrost. This requires careful consideration of the local environment and the physical processes occurring in the soil. While the mathematical component of the thermal solution does not require further development, there

remains a significant gap in the methodology for determining and verifying the initial boundary conditions. The lack of a generally accepted approach to the implementation of thermal modelling is a serious scientific challenge since predictive thermal modelling is one of the crucial parts in the design of engineering structures on permafrost soils and, thus, in the development of the northern regions. This paper considers the influence of errors in the initial and boundary conditions of the model on the predicted temperature regime of soils. 2

MODERN ISSUES OF GEOTECHNICAL FORECAST

One of the main tasks in solving the problems of developing territories with permafrost soils is predicting changes in geocryological conditions. The development of such forecasts should be based on a comprehensive study

of possible changes in climatic, natural, and environmental conditions, which is a complex problem (Ershov et al., 2001). Having carried out geocryological studies and knowing the nature of anthropogenic/technogenic impacts, it is possible to make a geotechnical forecast. A geotechnical forecast is a way to predict the qualitative or quantitative characteristics of changes in the permafrost under the influence of certain types of impacts in different areas. Ground-surface conditions heavily influence the thermal regime of permafrost soils. They vary based on local air temperature, wind speed, precipitation, vegetation type and cover, snow cover, slope and aspect, and solar radiation. Anthropogenic impacts can change one or more components of the energy balance, leading to a change in the temperature regime of permafrost. Different landscapes, as well as relief affect snow cover distribution. Various studies show that the direction of the prevailing winds and the positioning of the slopes of natural irregularities affect snow depth (Gascoin et al., 2013, Draebing et al., 2017). In the Arctic, snow tends to have ecosystem-specific properties (Sturm et al., 1995), generally associated with landscapes and climates. For example, increased accumulation of snow at the bottom of the embankment occurs in the tundra due to wind redistribution and has a warming effect on the temperature regime of the soil (O'Neill et al., 2017). Simulation results for Barrow, Alaska, for the period 1977–1998 showed that about half of the warming of permafrost at a depth of 20 m was associated with an increase in the thickness of the snow cover and the rest with an increase in air temperature (Stieglitz et al., 2003). The results of another study indicate that snow cover can lead to higher soil temperatures in northern regions over a long-term period, especially in areas covered by continuous permafrost (Park et al., 2015). On the other hand, the absence of seasonal snow cover in discontinuous and sporadic permafrost regions may be a key factor for permafrost development (Zhang, 2005). According to a study by Grünberg et al. (2020), to determine the relationship between vegetation and permafrost, a strong influence of vegetation on snow accumulation was found. The ability of vegetation to trap snow has been described previously (Frost et al., 2018). Furthermore, snow in shrub areas tends to be thicker and more effective as a heat insulator per unit volume than the snow outside of shrub patches (Sturm et al., 2001). The development of territories in permafrost regions has been a significant factor in forming modern landscapes. In addition, the development of territories has also led to the creation of new infrastructure and transportation networks, such as highways and railways. Human activities such as mining, oil and gas development, and infrastructure construction can alter the landscape by removing vegetation cover and altering the surface topography. Therefore, geotechnical forecasting in developed areas is complicated, and many factors cause uncertainties in surface conditions. The task of geotechnical forecasting is precisely the elimination of these uncertainties.

3

INITIAL CONDITIONS

The initial temperature distribution in the soil is a critical input for the thermal modelling of permafrost. This is typically obtained from measurements of the soil temperature at depth. The other important factor to consider is the thermophysical properties of the soils. The thermophysical properties of soils during the conductive transfer of thermal energy are evaluated by three main characteristics: heat capacity, thermal conductivity, and thermal diffusivity. The thermophysical properties of soils are characterized by a significant dependence on the composition, structure, and state of the soils. 3.1

Heat capacity

The volumetric heat capacity of soils (kJ/(m3°C)) is the amount of heat required to change the temperature of a unit volume of soil by one degree. Due to the heat capacity of a substance being equivalent to the sum of its components’ heat capacity, the volumetric heat capacity of soil can be obtained by adding the volumetric heat capacity of its different constituent parts. 3.2

Thermal conductivity

The thermal conductivity of soils (W/(m°C)) can be expressed as the amount of heat that flows across a surface of a unit area of soil in a unit of time under a unit of the temperature gradient of the soil surface. There is an intrinsic relation between thermal conductivity and mineral content, porosity, degree of water saturation, and temperature. There are laboratory and calculated methods for determining the thermal conductivity of soils. In the laboratory, researchers have used transient line heat source methods to measure thermal conductivity. Typically, a probe for this measurement consists of a needle with a heater and temperature sensor inside (Decagon Devices, Inc., 2016). The accuracy of determining thermophysical parameters is ±10% in the interval from 0.2 to 4.0 W/(m°C). In more recent times, three studies by Balland and Arp (2005), Coté and Konrad (2005), and Lu et al. (2007) have tried to improve on Johansen’s model for calculating thermal conductivity of soils. The comparison results by Barry-Macaulay (2015) demonstrate that, in general, all four models show good agreement between experimental thermal conductivity and modelled thermal conductivity. The root mean square error value can reach 0.15 for finegrained soils and 0.20 for coarse-grained soils. 3.3

Thermal diffusivity

The rate of change of the temperature field over time in a medium without internal heat sources is determined by the thermal diffusivity (m2/s). In heat transfer analysis, thermal diffusivity is the thermal conductivity divided by volumetric capacity at constant pressure.

4

BOUNDARY CONDITIONS

The assignment of boundary conditions on the ground surface is complicated, which is explained by the presence of three types of heat transfer there simultaneously: conductive, convective, and radiant. Consequently, many parameters determine this heat transfer. The surface energy balance equation can be written for a heat transfer analysis as follows: (qns-qnl ) = qsens+qlat+qg

[1]

where qns is net solar radiation, qnl is net terrestrial radiation, qsens is sensible heat flux, qlat is latent heat flux, and qg is ground heat flux. Convective heat transfer involves the combined processes of conduction and heat transfer by bulk fluid flow, referred to as heat advection. The rate equation describing convective heat transfer (Newton’s Law of Cooling) is: q = h(Tg-Ta)

[2]

where q is the surface heat flux due to convection, W/m2, h is the convection heat transfer coefficient, W/(m2°C), Tg temperature of the surface, °C, and Ta the temperature of the fluid outside the thermal boundary layer, °C. Based on Equations 1 and 2, the surface temperature can be written as: Tg = Ta+(R-P)/h

[3]

where R is the radiation balance of the soil surface, W/m2, and P is heating loss from the day surface due to evaporation and heating of underlying rocks and phase transitions in them, W/m2. Based on the works of Khrustalev L.N., semi-imperial formulas for calculating these parameters were derived (Ershov et al., 1999): R = 0.61TSI-20 P = 0.49TSI-60

[4] [5]

where TSI is the total solar irradiance, W/m2. Ershov (1999) proposes the following formula for determining the heat transfer coefficient due to wind speed: h = 2.4v+4.3, if v4.6 m/s

[6] [7]

where v is wind speed, m/s. Based on the above, the surface temperature can be used considering the so-called radiative correction (Eq. 4 and 5), and the heat transfer coefficient due to wind can be used based on Eq. 6 or 7. Since there are usually natural covers in the form of snow or vegetation on the soil surface, the heat transfer coefficient between the soil surface and air can be written in the following form: htotal = 1/(1/h+Rsnow+Rvegetation)

[8]

where Rsnow is the thermal insulance of snow cover, (m2°C)/W, and Rvegetation is the thermal insulance of vegetation cover, (m2°C)/W. In turn, thermal insulance is determined in terms of the thermal conductivity of the material: R = l/λ

[9]

where l is the length of the heat circuit section, m, and λ is the thermal conductivity of the material, W/(m°C). The equation obtained by Proskuryakov B.V has been widely used in engineering practice: λ = 0.021+1.01·10-3·ρ

[10]

According to a comparative study conducted by Osokin et al. (2017), values calculated using Equation 10 correlate well with the data obtained from field measurements. The values of the thermal conductivity of vegetation for frozen and non-frozen conditions depend on humidity (Gavriliev, 2004; Aleksyutina et al., 2011). The value of thermal conductivity of vegetation can vary in a wide range of values, from 0.05 to 0.46 W/(m°C) for the thawed state and from 0.07 to 1.14 W/(m°C) for the frozen state. However, empirical relationships have not yet been developed. The article by Gorelik and Pazderin (2017) proposes to select the upper boundary conditions so that under these conditions, the soil temperature at the depth of zero annual amplitude and the depth of seasonal thawing should coincide with their measured values during surveys. Thus, the problem of determining the initial boundary conditions can be reduced to the selection of the value of the thermal resistance of the vegetation cover. Scientists have long identified the surface cover’s poorly constrained thermal properties as a critical source of uncertainty for ground temperature modelling (Westermann et al., 2013; Decharme et al., 2016). Careful selection and accurate specification of boundary conditions are essential for obtaining reliable predictions of temperature distribution. 5

BASIS OF SIMULATION

A borehole located on the Yamal Peninsula was selected to perform thermal modelling. The landscape of this area is a well-drained patchy polygonal tundra (including slope edges), mainly with herbaceous-moss-lichen vegetation. The profile along the borehole is an interbedding of fine and silty non-saline sands to a depth of 13.5 meters. Then, to a depth of 21.5 m, there are light loams of medium salinity. Heavy, moderately saline loams were found from a depth of 21.5 m to 25 m. Table 1 shows the values of the physical characteristics of soils. Wtot – in situ volumetric water content; λf and λth - thermal conductivity in frozen and unfrozen state, W/(m°C); Cf and Cth heat capacity in frozen and unfrozen state, kJ/(m3°C); and freezing point, °C. The nearest meteorological station is located 120 km from the borehole and located near Seyaha. Climatic characteristics are given in Table 2.

Soils

Wtot

λf

λth

Cf

Cth

FP

Silty sand

0.271

2.48

1.91

1818

2614

-0.12

Fine sand

0.231

2.56

2.09

1854

2590

-0.15

Silt

0.161

1.44

1.43

2509

2905

-1.07

Silty clay

0.152

1.45

1.44

2541

2686

-2.04

active layer thickness differs from 0.9 to 1.1m, the temperature at the depth of zero annual amplitude is -5.2°C and the depth of zero annual amplitude is 16.0m.

Depth, m

Table 1. Physical properties of soils

Temperature, °C

-25

-20

-15

-10

-5

0

5

0

25.08.18

5

01.01.19 01.04.19

10

01.01.20 01.04.20

15

01.10.21 01.01.22

20

01.04.22 01.10.22

25

Figure 2. Initial temperature curve 6 Figure 1. Space image of the borehole location Table 2. Average monthly climatic characteristics of the Seyakha meteorological station Month

Temperature (°C)

Snow depth (m)

Snow density (t/m3)

Wind speed (m/s)

Jan

-23.8

0.31

0.27

6.0

Feb

-23.7

0.33

0.29

5.6

Mar

-19

0.33

0.31

5.8

Apr

-13.9

0.33

0.32

5.7

May

-5.3

0.35

0.23

6.0

Jun

3.1

-

-

5.3

Jul

8.9

-

-

4.4

Aug

8.9

-

-

5.2

Sep

4.2

-

-

5.9

Oct

-4.3

0.13

0.03

6.2

Nov

-15.1

0.22

0.15

6.1

Dec

-19.8

0.28

0.21

6.3

Jan

-23.8

0.31

0.27

6.0

Temperature measurements were regularly taken in the borehole, which is shown in Figure 2. According to the measurements, the depth of zero annual amplitudes, the temperature at the depth of zero annual amplitudes and also the thickness of the active layer were determined. The

THERMAL MODELLING

A transient analysis in TEMP/W was used to perform the initial simulation. Measured ground temperatures on 01 October 2021 were implemented as the initial ground temperature condition for the original scenario. A Convective Surface boundary condition was used to estimate the ground’s thermal response to simulate the complex soil-climate interaction. The Convective Surface boundary condition applies Newton’s Law of Cooling (Eq. 2) to calculate a heat flux. The temperature was set considering the radiative correction according to Equations 4 and 5. As a result of the calibration modelling, such heat transfer coefficient values were selected at which the temperature values at the depth of zero annual amplitudes and the thickness of the active layer corresponded to those measured during surveys. The selected values of the temperatures and heat transfer coefficient, given in Table 3, gave satisfactory results relative to those measured in the field. The simulated depth of the active layer and temperature at the depth of zero annual amplitudes were found to be in close agreement with field measurements, with errors not exceeding 6%. Figure 3 shows the temperature curves of the simulated and field data for April and October. The deviation in the simulated data under 1.0m in April may occur because the model uses monthly mean snow depths. For more accurate modelling, it is necessary to use values that change over 10 days. At the same time, the difference in the upper 1.0 m is because under natural conditions the atmospheric temperature can vary greatly during the day, and the

sensor located close to the surface is very sensitive to this change. However, the fit obtained here is considered sufficient for the purposes of this paper.

Depth, m

Table 3. Values used for the surface boundary condition in the original simulation scenario Month

Temperature (°C)

Heat transfer coefficient (W/(m2°С))

Jan

-23.8

1.120

Feb

-23.7

1.101

Mar

-16.3

1.039

Apr

-10.5

1.008

May

-1.5

1.431

Jun

7.5

3.097

Jul

14.0

2.946

Aug

12.5

3.083

Sep

6.7

3.173

Oct

-2.1

3.581

Nov

-15.1

1.428

Dec

-19.8

1.294

Jan

-23.8

1.120

7.1

-12.0

-8.0

-4.0

Influence of thermal conductivity

According to Figure 4, an error in determining the thermal conductivity gives a slight deviation of the temperature curve from the original scenario. The temperature at a depth of zero annual amplitudes differs by 0.1°C, comparable to the accuracy of temperature determination in the borehole. The deviation of the temperature curves from the original scenario in the range of 2–15 m does not exceed 0.3°C. In a specific model, no influence on the depth of the active layer can be traced. Table 4. Thermal conductivity values (W/(m°C)) used for sensitivity analysis

Temperature, °C -16.0

35%, while the wind speed can fluctuate by 50% in both sides. However, for a more detailed assessment of the effect of snow cover over time, an intermediate value of 10% in snow depth was taken. Table 6 shows the heat transfer coefficient values used for sensitivity analysis. Additionally, the values of the thermal conductivity of soils were changed by the amount of possible error in their determination. According to available data, the value of such an error can be ±10%. Table 4 lists the thermal conductivity values used for sensitive analysis.

0.0

4.0

0

Soils

+10% λf

+10% λth

-10% λf

-10% λth

Silty sand

2.73

2.10

2.23

1.72

Fine sand

2.81

2.30

2.30

1.88

Silt

1.59

1.57

1.30

1.28

Silty clay

1.59

1.58

1.30

1.29

4 6

01.04.20 01.10.21 Simulated 01.04

Depth, m

2

Temperature,°C -6.0

2

10

4

12

6

14

8

16

10

18 Figure 3. Comparison of field data with simulated temperatures SENSITIVITY ANALYSIS

The main objective of this study is to compare the influence of the initial boundary conditions on the model. To fulfill the set goal, components of surface conditions were changed, such as the thickness of the snow cover and wind speed. According to data analysis from the Seyakha weather station over the past 20 years, the deviation of snow cover from the average value can reach

-3.0

-2.0

-1.0

0.0

12 14 16

7

-4.0

0

8

Simulated 01.10

-5.0

Original scenario +10% Thermal conductivity

-10% Thermal conductivity 18 Figure 4. Influence of thermal conductivity on the temperature curve By correcting the surface conditions in the winter period by about 1.5 - 2.5%, it is possible to achieve a decrease in the influence due to thermal conductivity on the temperature curve and thus minimize the effect of error in

determining the thermal conductivity. It should be noted that these values may differ depending on the geotechnical profile. The values of heat transfer on the surface used in the calibration are given in Table 6, and the results of the calibration of the models are shown in Figure 5. In this case, the deviation of the temperature curves from the original scenario in the range of 2 - 15 m does not exceed 0.1°C. Table 5. Values of convective heat transfer coefficient (W/(m2°C)) used for calibration of models with different thermal conductivity profiles Month

+10% λ

-10% λ

Jan

1.137 1.118 1.055 1.024 1.452 3.097 2.946 3.083 3.173 3.635 1.449 1.313 1.137

1.092 1.074 1.013 0.983 1.395 3.097 2.946 3.083 3.173 3.492 1.392 1.261 1.092

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Influence of boundary conditions

According to Figure 6, an error in determining the depth of snow gives a significant deviation of the temperature curve from the original scenario, even with a deviation of 10%. The temperature at a depth of zero annual amplitudes differs by 0.3°C for a 10% snow deviation and by 1.2°C for a 35% snow deviation. In a specific model, no influence on the depth of the active layer can be traced. Nevertheless, according to Figure 7, an error in determining the wind speed gives insignificant deviation. Table 6. Values of convective heat transfer coefficient (W/(m2°C)) used for sensitivity analysis Month

+10% Snow depth

-10% Snow depth

+35% Snow depth

-35% Snow depth

+50% Wind speed

-50% Wind speed

Jan

1.231

1.027

1.640

0.850

1.146

1.080

Feb

1.210

1.010

1.609

0.837

1.129

1.064

Mar

1.143

0.953

1.525

0.788

1.063

1.005

Apr

1.110

0.924

1.481

0.764

1.031

0.977

May

1.569

1.315

2.067

1.094

1.474

1.367

Jun

3.097

3.097

3.097

3.097

3.346

2.826

Jul

2.946

2.946

2.946

2.946

3.244

2.744

Aug

3.083

3.083

3.083

3.083

3.336

2.818

Sep

3.173

3.173

3.173

3.173

3.396

2.875

Oct

3.853

3.345

4.757

2.871

3.849

3.201

Nov

1.566

1.312

2.064

1.091

1.469

1.363

Dec

1.421

1.187

1.884

0.985

1.326

1.240

Jan

1.231

1.027

1.640

0.850

1.146

1.080

Temperature, °C -6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0 2

Depth, m

Depth, m

Jan

7.2

Temperature, °C -7.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0

4

2

6

4 6

8

8

10

10

12 Original scenario

12

+10% Thermal conductivity

14

14 16

-6.0

-10% Thermal conductivity 18 Figure 5. Calibration of temperature curves for different thermal conductivity profiles

16

Original scenario +10% Snow depth -10% Snow depth +35% Snow depth -35% Snow depth

18 Figure 6. Influence of snow cover on the temperature curve

-6.0

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

0 2 4

Temperature, °C

Depth, m

Temperature, °C -7.0

Time, Years 0

20

40

60

80

100

-4.0 -4.5

-5.0

6

8

-5.5

10 -6.0

12 14 16

-6.5

Original scenario +50% Wind speed

-50% Wind speed 18 Figure 7. Influence of wind speed on the temperature curve 8

RESULTS AND DISCUSSION

This study uses model verification methods developed at the Moscow State University at the Department of Geocryology. According to the results obtained, the method used has good accuracy in determining the initial boundary conditions on the surface. The results of the sensitivity analysis can be divided into 5 points: • Errors in determining the thermal conductivity do not affect the ground thermal regime process significantly. • A slight deviation caused by an error in determining the thermal resistance can be reduced by calibrating the surface boundary conditions. • Snow coverage significantly impacts the thermal regime of permafrost. An error in determining snow depth can lead to poorly constrained essential components in geotechnical design. • The wind component appears to have a negligible impact on the temperature regime of soils in natural conditions. Moreover, in natural conditions, the temperature regime of soils is mainly determined by the properties of the surface covers. • As demonstrated in Figure 8, even a relatively small error of 10% can have a significant impact on the early stages of building and structure operation. Furthermore, an error of 35% in determining the height of snow cover can lead to a substantial influence on the temperature regime, subsequently affecting the bearing capacity of frozen soils. In contrast, the other components investigated in this study were found to have a negligible impact on geotechnical projects, both in the short and long term.

Figure 8. Comparison of the influences of different factors on the temperature on the depth of zero annual amplitude over time 9

CONCLUSION AND NEXT STEPS

This study summarizes the results of thermal behaviour under the change in different factors, such as depth of snow cover, wind speed and thermal conductivity. Based on the results, error in determining the boundary conditions has a much more noticeable effect in thermal modelling. Under the influence of various types of development, many components of the natural environment can change. This study shows that it is essential to correctly determine these changes due to anthropogenic impacts for accurate thermal modelling. There is a need to improve the quality and accuracy of the geocryological forecast in order to understand and mitigate anthropogenic impacts on permafrost, leading to more sustainable and economic northern development. The long-term goal is to develop a formalized approach to accurately and quantitatively determine anthropogenic influences in geocryological studies. 10

ACKNOWLEDGEMENTS

We would like to express our gratitude to Sergey Pravov and Alexandra Popova for generously sharing the data used in this study. We would also like to thank Teddi Herring for insightful comments and suggestions, which greatly improved the quality of the paper. Financial support for this research was provided by the Natural Sciences and Engineering Research Council of Canada (NSERC).

11

REFERENCES

Aleksyutina, D. & Motenko, R. 2011. Thermal properties for vegetation cover for AGU Fall Meeting ’11, San Fransico, CA, USA. 0408-. Balland, V., & Arp, P. A. 2005. Modeling soil thermal conductivities over a wide range of conditions. Journal of Environmental Engineering and Science, 4(6), 549– 558. Barry-Macaulay, D, Bouazza, A., Wang, B., & Singh, R. M. 2015. Evaluation of soil thermal conductivity models. Canadian Geotechnical Journal, 52(11), 1892–1900. Côté, J. & Konrad, J.-M. 2005. A generalized thermal conductivity model for soils and construction materials. Canadian Geotechnical Journal, 42(2), 443–458. Decagon Devices, Inc. 2016. KD2 Pro Thermal Properties Analyzer Operator’s Manual. Decharme, B., Brun, E., Boone, A., Delire, C., Le Moigne, P., & Morin, S. 2016. Impacts of snow and organic soils parameterization on northern Eurasian soil temperature profiles simulated by the ISBA land surface model. The Cryosphere, 10(2), 853–877. Draebing, D., Haberkorn, A., Krautblatter, M., Kenner, R., & Phillips, M. 2017. Thermal and mechanical responses resulting from spatial and temporal snow cover variability in permafrost rock slopes, steintaelli, swiss alps: thermal and mechanical responses to snow in permafrost rock slopes. Permafrost and Periglacial Processes, 28(1), 140–157. Ershov, E.D. et al. 1999. Fundamentals of geocryology. Volume 5. Permafrost Engineering. Moscow University Press, Moscow, Russia (In Russ.). Ershov, E.D. et al. 2001. Fundamentals of geocryology. Volume 4. Dynamic geocryology. Moscow University Press, Moscow, Russia (In Russ.) Frost, G. V., Epstein, H. E., Walker, D. A., Matyshak, G., & Ermokhina, K. 2018. Seasonal and Long-Term Changes to Active-Layer Temperatures after Tall Shrubland Expansion and Succession in Arctic Tundra. Ecosystems (New York), 21(3), 507–520.Gascoin, S., Lhermitte, S., Kinnard, C., Bortels, K., & Liston, G. E. 2013. Wind effects on snow cover in Pascua-Lama, Dry Andes of Chile. Advances in Water Resources, 55, 25– 39. Gavriliev, I. 2004. Thermophysical properties of natural environment components in permafrost. Novosibirsk: Russian Academy of Science. 146 p. (In Russ.). Gorelik, J.B.; Pazderin, D.S. 2017. Correctness of formulation and solution of thermotechnical problems in forecasting temperature field dynamics in the foundations of constructions on permafrost. Kriosfera Zemli, 21, 49–59. (In Russ.) Grünberg, I., Wilcox, E. J., Zwieback, S., Marsh, P., & Boike, J. 2020. Linking tundra vegetation, snow, soil temperature, and permafrost. Biogeosciences, 17(16), 4261–4279. Lu, S., Ren, T., Gong, Y., and Horton, R. 2007. An improved model for predicting soil thermal conductivity from water content at room temperature. Soil Science Society of America Journal, 71(1): 8–14.

O’Neill, H., & Burn, C. R. 2017. Impacts of variations in snow cover on permafrost stability, including simulated snow management, Dempster Highway, Peel Plateau, Northwest Territories. Arctic Science, 3(2), 150–178 Osokin, N.I., Sosnovskiy, A.V. & Chernov, R.A. 2017. Effective thermal conductivity of snow and its variations. Earth's Cryosphere. 21. 60-68. Park, H., Fedorov, A. N., Zheleznyak, M. N., Konstantinov, P. Y., & Walsh, J. E. 2015. Effect of snow cover on panArctic permafrost thermal regimes. Climate Dynamics, 44(9-10), 2873–2895. Stieglitz, M., Dery, S. J., Romanovsky, V. E., & Osterkamp, T. E. 2003. The role of snow cover in the warming of arctic permafrost. Geophysical Research Letters, 30(13), 1721–n/a. Sturm, M., Holmgren, J., & Liston, G. E. 1995. A Seasonal Snow Cover Classification System for Local to Global Applications. Journal of Climate, 8(5), 1261–1283. Sturm, M., McFadden, J. P., Liston, G. E., Chapin, F. S., Racine, C. H., & Holmgren, J. 2001. Snow–Shrub Interactions in Arctic Tundra: A Hypothesis with Climatic Implications. Journal of Climate, 14(3), 336– 344. Westermann, S., Schuler, T. V., Gisnås, K., & Etzelmüller, B. 2013. Transient thermal modelling of permafrost conditions in Southern Norway. The Cryosphere, 7(2), 719–739. Zhang, T. 2005. Influence of the seasonal snow cover on the ground thermal regime: An overview: snow cover and ground thermal regime. Reviews of Geophysics (1985), 43(4).

Preliminary results of thermo-hydromechanical modelling of large-strain deformation of ground in Canadian cold regions subject to climate change Anna Pekinasova, Jocelyn L. Hayley Department of Civil Engineering, University of Calgary Brandon Karchewski Department of Geoscience, University of Calgary, Calgary, Alberta, Canada ABSTRACT Understanding the dynamic nature of frozen ground and thawing permafrost is vital for engineering adaptation of transportation infrastructure in cold regions to climate change. A large-strain thaw consolidation model is more appropriate than a small-strain model for ice-rich permafrost terrain, fine-grained soils, and frozen ground with excess ice. To forecast the impacts of climate change, it is important that models account for the complex and dynamic heat energy boundary conditions which may be influenced by short-wave and long-wave solar radiation, humidity, precipitation, and wind speed in addition to air temperature. This study builds on recent modelling work on nonlinear thermo-hydro-mechanical coupled large-strain consolidation (Dumais and Konrad 2018, Yu et al. 2020a, 2020b) and presents preliminary results towards developing an open-source tool for forecasting the impacts of climate change on infrastructure in Canadian cold regions. RÉSUMÉ Comprendre la nature dynamique du sol gelé et du dégel du pergélisol est essentiel pour l'ingénierie de l'adaptation des infrastructures de transport dans les régions froides au changement climatique. Un modèle de consolidation de dégel à grande déformation est plus approprié qu'un modèle à petite déformation pour les terrains de pergélisol riches en glace, les sols à grains fins et les sols gelés avec un excès de glace. Pour prévoir les impacts du changement climatique, il est important que les modèles tiennent compte des conditions aux limites complexes et dynamiques de l'énergie thermique qui peuvent être influencées par le rayonnement solaire à ondes courtes et à ondes longues, l'humidité, les précipitations et la vitesse du vent en plus de la température de l'air. . Cette étude s'appuie sur des travaux de modélisation récents sur la consolidation non linéaire couplée thermo-hydro-mécanique à grandes déformations (Dumais et Konrad 2018, Yu et al. 2020a, 2020b) et présente des résultats préliminaires en vue de développer un outil open source pour prévoir les impacts du changement climatique. changements sur les infrastructures dans les régions froides du Canada.

1

INTRODUCTION

Understanding the settlement and heaving of ground experiencing freeze-thaw cycling is a vital component of understanding and analyzing infrastructure built in northern and permafrost regions subject to climate change. Due to the increasing impacts of climate change in cold regions, there is an urgent need to advance the understanding of freezing and thawing ground behaviour in order to develop resilient infrastructure and promote sustainable socioeconomic climate adaptation in these important Canadian communities. Heaving and thawing of the ground are two major causes of infrastructure destruction in cold regions (Yu et al. 2020a). The freezing process can trigger migration of water from unfrozen to frozen zones (cryosuction) and formation of icelenses (Yu et al. 2020a). When ice-rich material thaws, the ground loses strength due to high pore water pressure, develops substantial thaw settlement, leads to instability, and increased runoff and flooding (cf. Hjort et al. 2018). These effects result in increases in repairs, cost of maintenance, and unreliable operations, all of which have undesirable impacts on the socioeconomic aspects of northern communities and the environment (cf. Hjort et al. 2018).

There is therefore a need for practical and openly available tools to evaluate and analyze existing and future infrastructure resilience to climate change. Such tools will aid in the identification of potential risks and budget allocation for operations and maintenance to reduce and offset the negative impacts of climate change. Climate change forcing in northern regions (e.g. increasing annual mean temperature, increasing precipitation, increasing relative humidity, surface ponding, streamflow, soil moisture, and/or droughts) causes regional and local changes by affecting the ground temperature distribution, the thickness of the active layer, and the depth to stable ground temperature (cf. Smith and Riseborough 1996, Riseborough et al. 2008, An et al. 2017, Harris et al. 2017). There have been significant advances in formulation and numerical analysis of freeze-thaw processes in cold regions and permafrost terrain and in modelling of surface energy boundary conditions, but these have not been fully integrated into a practical engineering analysis tool. Our research focusses on the impact of climate change on how the ground moves – thaw consolidation and frost heave – using a novel combination of coupled equations to accurately quantify the freeze-thaw processes with state-of-the-art models for the

surface energy balance. The goal of our work is to develop a practical open-source thermo-hydro-mechanical (THM) modelling tool to bridge the gap between disciplines of geotechnical engineering, hydrogeology, and climate science. This modelling tool will assist in the design and analysis of existing and future infrastructure for engineers, geoscientists, climate scientists, owners, and policymakers, and other stakeholders. 2

MATHEMATICAL FORMULATION

The theory of ground freeze-thaw and thaw consolidation involves thermo-hydro-mechanical coupled processes accounting for heat transfer, pore fluid migration, and deformation resulting from a nonlinear void ratiopermeability-effective stress relationship. It is important to recognize that in a layer experiencing seasonal variations in surface boundary conditions, the ground temperature distribution may oscillate with depth, and different parts of the surface layer may experience consolidation or heave processes simultaneously. This is more complex than a monotonic freezing or thawing process for which analytical solutions are available (Stefan 1889, Sumgin et al. 1940, Carlson 1952, Carslaw and Jaeger 1959, Kudryavtsev et al. 1977, Lunardini 1978, Wright et al. 2003, Andersland and Ladanyi 2004), since there are interannual variations in the thermal regime of the subsurface (Romanovsky and Osterkamp 1995, Riseborough et al. 2008), and necessitates numerical solution of a coupled system of partial differential equations. We follow the formulations presented by Dumais and Konrad (2018) and Yu et al. (2020a, 2020b), which we summarize here briefly for context. For more detail, we refer the reader to the original references and prior work. 2.1 Lagrangian coordinate transformation To capture the coordinate change due to thaw consolidation and heaving we can use a coordinate transformation technique that captures the moving boundary conditions which can be mapped onto non-moving Lagrangian coordinates to simplify the calculation of the THM formulation. In the case of large strain consolidation and heave, the change of coordinates is entirely described by the change in volume between any two reference points. As such, we can relate derivatives (and infinitesimals) with respect to the global (moving) coordinate z [m] to the fixed (Lagrangian) coordinate Z [m] via the following relationship (cf. Dumais and Konrad 2018) [1] where e = e(z, t) is the void ratio at some position z and time t [s], and e0 = e(z, 0) is the initial void ratio at the same reference position. There is a 1-to-1 mapping between any moving reference point z and a corresponding reference point Z in the Lagrangian coordinates. For brevity, we present the remaining equations in this document with respect to the Lagrangian coordinate system, though the physical relationships originate in the global (moving) coordinate system.

A useful application of the Lagrangian coordinate transformation is for the calculation of total settlement of a layer experiencing simultaneous consolidation and heave processes in terms of the void ratio distribution and the Lagrangian coordinate system [2] where H [m] is the original thickness of the layer, Z = z0 is the coordinate of the original ground surface, and Z = z0 + H is a point below which negligible deformation occurs (e.g. below the bottom of a permafrost layer). We can see that this calculation is simply the original layer thickness minus the volume change ratio integrated over the entire layer. Note that points with (1+e)/(1+e0) > 1 contribute to heave and points with (1+e)/(1+e0) < 1 contribute to a settlement which may both be occurring simultaneously at different depths within the soil column and a positive (negative) value for s(t) would represent a net settlement (heave) of the layer. 2.2 Thermal equations We write the heat energy balance in the unfrozen or frozen soil by accounting for conduction, advection, sensible heat storage due to temperature change, and latent heat storage due to phase change as follows (Dumais and Konrad 2018)

[3] where λ [J·s-1·K-1] is the bulk thermal conductivity, T [°C or K] is the temperature, qw [m·s-1] is the pore water flux rate per total unit area, Cw [J·K-1·m-3] is the volumetric heat capacity of water, C [J·K-1·m-3] is the bulk volumetric heat capacity, L [J·kg-1] is the specific latent heat of fusion of water, ρi [kg·m3] is the density of ice, S [-] is the degree of saturation of w unfrozen pore water, t [s] is time, and a superposed dot indicates a time derivative. Assuming fully saturated conditions (Sw + Si = 1), we compute the bulk thermal conductivity and bulk volumetric heat capacity using geometric and arithmetic averages, respectively, as follows (Côté and Konrad 2005) [4] [5] where subscripts s, w, and i refer to solids, water, and ice, respectively. To compute ∂Sw/∂T, we follow Yu et al. (2020b) in using the Sw – T relation by Nishimura et al. (2009) as follows [6]

where α and β are empirical values that depend on the material and temperature T must be in [°C] here. Eq. [6] only applies for T ≤ Tf where Tf = 0°C = 273.15 K is the freezing temperature of water and we assume that Sw = 1 and ∂Sw/∂T = 0 for T > Tf. 2.3 Hydraulic equations The pore fluid flux qw behaves differently depending on whether the soil is unfrozen or frozen. In the unfrozen soil (T > Tf), we assume that fluid flux is governed by the 1D large-strain consolidation process involving the transfer of excess pore pressure to effective stress, since at hydrostatic conditions the flux rate will be zero. We use Darcy’s law to relate the pore fluid flux qw to the gradient of effective stress σ′ as follows [7] where k [m·s-1] is the permeability (hydraulic conductivity) of the unfrozen soil, γw [N·m-3] is the unit weight of water, Gs [-] is the specific gravity of the solids, and we evaluate ∂σ′/∂Z using chain rule as (∂σ′/∂e)(∂e/∂Z) where ∂σ′/∂e is the slope of the effective stress – void ratio curve, which we discuss further in Section 2.4. The bracketed term in Eq. [4] is the difference between the effective stress gradient (∂σ′/∂Z)′ at hydrostatic conditions (i.e. buoyant unit weight γ′) and the current effective stress gradient ∂σ′/∂Z. This difference goes to zero as excess pore pressure dissipates approaching hydrostatic conditions. We also note that permeability depends on void ratio, and we follow Yu et al. (2020b) in writing [8] where Cku [-] is a permeability change index for unfrozen soil, {k0, eu0} are a reference permeability and corresponding void ratio, m ≥ 1.0 is a multiplier accounting for a permeability increase in thawed soil, and elim, emin, and etr are reference void ratios, which we do not explain in detail here. We present Eq. [8] here mainly to emphasize that we obtain the gradient ∂k/∂Z in the advection term of Eq. [3] via (∂k/∂e)(∂e/∂Z) similar to the effective stress gradient in Eq. [4]. For frozen soil (T ≤ Tf) we use a water flux function, which has a detailed history of investigation, although a complete review of the literature is outside of the scope of this paper. The fluid flux in frozen soil depends on several factors including temperature gradient, segregation potential, soil type, void ratio, and stress state (cf. Konrad and Morgenstern 1982, Michalowski 1993, Michalowski and Zhu 2006, Yu et al. 2020a). For the model herein, we follow Yu et al. (2020a) and write [9] where Ṫref [°C⋅s-1] is a small positive reference temperature rate, SP0 [m2⋅°C-1⋅s-1] is the segregation potential of the soil corresponding to void ratio e0 on the frozen front at steady

state with no overburden pressure, σ1 [MPa] is the local stress including overburden pressure and additional pressure associated with the void ratio, and b1 [-], b2 [°C-1], and b3 [MPa-1] are soil constants that should be determined experimentally (Yu et al., 2020a). In Eq. [9], the ± sign is positive when Ṫ < 0 and negative when Ṫ > 0. 2.4 Effective stress hydromechanical equations For the unfrozen soil, we follow Yu et al. (2020b) in explicitly coupling the hydraulic mass balance and the momentum balance equations assuming all deformation is due to the consolidation process to obtain the following balance equation in terms of effective stress σ′ and void ratio e [10] where all variables are as introduced previously. The effective stress – void ratio relationship used to calculate the stress-strain coefficient ∂σ′/∂e is the well known semilogarithmic curve as follows (Yu et al. 2020b) [11] where Ccu and Cru are the compression and rebound indices of unfrozen soil, pc′ is the preconsolidation pressure, and the points {σ′cu0, ecu0} and {σ′ru0, eru0} are reference locations for the normal consolidation line (NCL) and the unloadingreloading line (URL), respectively. Yu et al. (2020b) explain in more detail about upper and lower limits of validity of this model with respect to grain separation (above some void ratio esep) and particle breakage (below some void ratio emin), but we assume for the analysis herein that emin < e < esep. 2.5 Total stress mechanical equations For the frozen soil, there is deformation due to fluid migration, phase change, and total stress variation, which introduces some complication owing to the stress-strain relationships, as suggested by Konrad and Samson (2000) and Yu et al. (2020b). We can write the volume change due to fluid migration in the frozen soil as [12] where we use Eq. [9] to compute the water flux qw. There is also volume change due to phase change between water and ice which follows [13] where Gi [-] is the specific gravity of ice and the value (1 – Gi) ≈ 9% represents the expansion (contraction) of pore volume during a freezing (thawing) process. The deformation due to stress variation follows the momentum balance in terms of total stress σ

[14] where the second term represents the total unit weight of a partially frozen soil in which the pores are fully saturated by ice and water. We note that Yu et al. (2020b) present a simplified form of this relationship [15] where the second term implies an assumption that the initial reference state with void ratio e0 corresponds to unfrozen conditions. We cannot make that assumption for cases where we examine permafrost ground that is initially frozen at some locations. We write a semilogarithmic total stress – void ratio relationship for the frozen soil as [16] where the point {ef0, σf0} is a reference point and Cf is a temperature-dependent compression index (Li et al. 2014, Yu et al. 2020b) [17] where a1, a2, and a3 are empirical material parameters. 2.6 Boundary conditions For the boundary condition at large depth Z = z = z0 + H, we assume a fixed geothermal gradient G = dT/dz [°C·m-1] so we write the ground heat flux rate qg as [18] where the thermal conductivity λ is evaluated at the integration point nearest to the boundary where the void ratio e and degree of saturation with water Sw is approximately constant in time. Note that at the lower boundary we assume the contribution of advection to the ground heat flux to be negligible and only account for conduction in the geothermal temperature gradient boundary condition. We assume full drainage at the lower boundary so we apply a boundary condition for water flux qw as

At the surface boundary, we follow Yu et al. (2020b) in applying prescribed temperature boundary conditions with a harmonic seasonal variation, which we explain in more detail in Section 4.1. To accurately model the impact of complex climate forcing, in future work we will incorporate a surface energy balance (SEB) boundary condition (cf. Foken 2008, An et al. 2017, van Huissteden 2020, Westermann et al. 2023) incorporating incoming and reflected short-wave radiation, incoming and outgoing thermal radiation of Earth's surface, sensible heat flux exchange by convection of air, latent heat flux by evaporation or condensation of water, and ground heat flux by migration of heat into and out of the ground. Each term involves specific formulae and parameters that characterize the energy balance at the surface. Westermann et al. (2023) provide a detailed overview of these components within a framework of process-based tile modelling useful for medium to large scale regional climateground interaction modelling. We refer the reader to the original work and references therein for more detail. As with the lower boundary, we assume full drainage at the upper boundary so we apply a prescribed void ratio similar to Eq. [20], but assume that we can calculate the void ratio based on the applied stress conditions using Eq. [11] or [16] depending on whether the surface soil is frozen or unfrozen. 3

NUMERICAL IMPLEMENTATION

In this section we present a brief overview of the weak form equations for solving the system of thermo-hydromechanically coupled equations using the finite element (Galerkin) method, modified after Yu et al. (2020b), and outline the implicit time stepping scheme with iterative correction. To invite transdisciplinary collaboration building bridges between the disciplines of geotechnical engineering, hydrogeology, and climate science we implement the numerical code as an open-source Python package frozenground-fem available at: https://github.com/annapekinasova/frozen-ground-fem/ In the code repository we provide examples of usage, and we encourage those interested in collaboration to reach out for collaborator access and/or to fork the repository to modify implementation details to their own needs. 3.1 Thermal update equations We form the integrated residual heat balance equation from Eq. [3] and apply integration by parts to obtain

[19] where q̂w is a prescribed flux that we compute using Eq. [7] or Eq. [9] at the integration point nearest to the boundary if T > Tf or T ≤ Tf, respectively, at that point. This is equivalent to a fully drained boundary condition since we allow the prescribed fluid flux to be driven by other processes. We assume negligible deformation below the lower boundary so [20] where ê is a prescribed void ratio fixing the void ratio at the lower boundary to the initial void ratio.

[21] where δT represents residuals or errors in the approximate solution for T, a superposed dot represents a time derivative, and a subscript comma represents differentiation with respect to the adjacent variable. Subdividing evaluation of Eq. [21] over a discretized system of finite elements, interpolating solution variables and residuals with the same weighting

functions (i.e. Galerkin method), and summing element vectors and matrices respecting connectivity of nodes between elements leads to the following discretized global residual equation [22] where T is the global vector of nodal temperatures, Φ is the global heat flux vector (from the boundary term in Eq. [21]), H is the global conductive-advective heat flow matrix (from the first integral in Eq. [21]), C is the global heat storage matrix (from the second integral in Eq. [21]), and superscript T is the transpose operator. Since the residuals δT are arbitrary, the expression in parentheses in Eq. [22] must be zero, and applying an implicit time stepping approach leads to the following update equation

making use of Eq. [8] to calculate ∂k/∂e and k. Discretization and integration of Eq. [25] leads to the global residual equation [27] where e is the global vector of nodal void ratios, Q is the global fluid flux vector (from the boundary term and first integral in Eq. [25]), K is the global stiffness matrix (from the second integral in Eq. [25]), and M is the global mass matrix (from the third integral in Eq. [25]). Again, for arbitrary residuals δe, the expression in parentheses in Eq. [27] must be zero, which leads to the following update equation

[28] [23] where ΔTj is the temperature correction vector at iteration j, ΨUj is a residual heat flux vector that goes to zero at convergence of a time step, and we compute any weighted vector or matrix Aαj as [24] Note that the global matrices in Eq. [23] depend on qw and e so we achieve coupling by updating them as part of the same iteration loop with the hydromechanical equations. 3.2 Hydromechanical update equations We combine Eqs. [10], [12], and [13] forming the integrated residual hydromechanical equation and apply integration by parts to obtain

where Δej is the void ratio correction vector at iteration j, ΨHMj is a residual hydromechanical vector, and we compute weighted vectors and matrices as in Eq. [24]. 3.3 Implicit time stepping with iterative correction We use an implicit time stepping scheme with iterative correction of material parameters and global matrices at each time step. We improve on the iterative correction algorithm of Yu et al. (2020b) by incorporating the update of material parameters, global coefficient matrices, and the void ratio change due to ice-water phase change within the iterative correction scheme. This can be computationally expensive for large models with thousands of elements, but we find that for the 1D large-strain freeze-thaw problem at field scale, the number of elements required is on the order of hundreds, so the added expense does not substantially increase the simulation time in absolute terms. The implicit time stepping scheme with iterative correction at each time step proceeds as follows: 0. 1.

2. [25] where δe represents residuals or errors in the approximate solution for e, H(T – Tf) is the Heaviside function that is 1 for T > Tf and 0 for T ≤ Tf, and we evaluate the following derivative as [26]

3. 4. 5. 6. 7.

Set initial conditions for t, T, e, e0, and boundary conditions and initialize global vectors for solution variables Tt and et. Initialize λ (Eq. [4]); C (Eq. [5]); Sw and ∂Sw/∂T (Eq. [6]); k and ∂k/∂e (Eq. [8]); qw (Eq. [7] or [9]); σ′, ∂σ′/∂e, and pc′ (Eq. [11]); and σ and ∂σ/∂e (Eq. [16]) based on Tt and et. Integrate to initialize global vectors and matrices Φt, Ht, and Ct (Eq. [21]); and Qt, Kt, and Mt (Eq. [25]). Initialize iteration counter j = 0, update boundary conditions at t + Δt, and integrate to initialize Φt+Δtj and Qt+Δtj. Initialize Tt+Δtj = Tt, et+Δtj = et, Ht+Δtj = Ht, Ct+Δtj = Ct, Kt+Δtj = Kt, and Mt+Δtj = Mt. Initialize weighted vectors and matrices Φαj, Qαj, Hαj, Cαj, Kαj, and Mαj (Eq. [24]). Compute Ψuj and solve for ΔTj (Eq. [23]), then update Tt+Δtj+1 = Tt+Δtj + ΔTj. Compute ΨHMj and solve for Δej (Eq. [25]), then update et+Δtj+1 = et+Δtj + Δej.

8.

Update λ, C, Sw, ∂Sw/∂T, k, ∂k/∂e, qw, σ′, ∂σ′/∂e, pc′, σ, and ∂σ/∂e based on Tt+Δtj+1 and et+Δtj+1, integrate to update Φt+Δtj, Qt+Δtj, Ht+Δtj, Ct+Δtj, Kt+Δtj, and Mt+Δtj, and update Hαj, Cαj, Kαj, and Mαj. 9. Check for convergence of relative error norms ||ΔTj|| / ||Tt+Δtj+1|| and ||Δej|| / ||et+Δtj+1||. If not converged, set j = j + 1 and go to to Step 6. 10. Compute additional void ratio change Δet+Δt due to total stress variation (Eq. [14]). 11. Set Φt = Φt+Δt, Qt = Qt+Δt, Ht = Ht+Δt, Ct = Ct+Δt, Kt = Kt+Δt, Mt = Mt+Δt, and t = t + Δt. 12. Check if final time tf reached. If not, go to Step 3. 4

(a)

PRELIMINARY RESULTS

Current work on fully coupled THM modelling of thaw consolidation (Dumais and Konrad 2018) and freeze-thaw heave and consolidation (Yu et al. 2020a,b) demonstrate results and performance of models for relatively thin surface layers 30°

30°-20°

20°-10°

10°-5°

10° 7. This may be attributed to the variation of soil properties with respect to the range of Ip. The fluctuation of matric suction in soils with Ip ≤ 7 is primarily influenced by the particle size distribution (PSD). Conversely, in soils with Ip > 7, the presence of finer particles and mineral composition not only contribute to but also complicate the variability in suction. 4

FRAMEWORK FOR ESTIMATING MATRIC SUCTION USING TWO AI TECHNIQUES

In this section, a novel framework is proposed, combining the strengths of the SVR and MARS models to provide a more efficient and reliable estimation of matric suction. First, potential inputs characterizing the features of studied

Testing set 0.94 93.79 0.05 -3.97

MARS Training set 0.96 96.36 0.04 0.68

Testing set 0.89 88.59 0.07 7.61

soils were selected. Second, the MARS algorithm was used as an efficient technique for selecting essential inputs. Finally, the SVR model was used to provide accurate estimation using the inputs provided by the MARS model. 4.1

Data reprocessing for soils with different plasticity

The estimation results of matric suction show a difference in the accuracy between soils with different plasticity (Fig. 3). Since the complexity and quality of the dataset have a substantial impact on the upper bound of estimation accuracy, the dataset is divided into two groups (i.e., soils with Ip ≤ 7 and soils with Ip > 7). 4.2

Identify essential inputs using the MARS model

The MARS model can be simplified with fewer BFs to conduct high-efficiency sensitivity analysis. As indicated in Table 4, the MARS model selects different essential inputs for different cases. For soil with Ip ≤ 7, sand fraction, which is useful in distinguishing the water-holding capacity, for soil with various pore size distributions is required. Soils with Ip > 7 require dry unit weight and initial void ratio, which is strongly correlated to the soil structure relating to finegrained soils. Furthermore, the degree of saturation, silt fraction, specific gravity, and plasticity index are required by all the models for suction estimation. Table 4. Identify the essential input using the MARS model

Soils

Essential inputs

Ip ≤ 7 Ip > 7

x10, x2, x8, x4, x1, x6 x2, x3, x4, x10, x8, x9, x5

VAF Training Testing 99.85 99.15 92.04 82.37

𝑛

(𝑢𝑎 − 𝑢𝑤 ) = max (0, 𝛼0 + ∑ 𝛼𝑖 × 𝐵𝐹𝑖 )

[5]

𝑖=1

Table 5. BFs and the coefficient of Eq.5 Since the MARS model performs better in estimating the matric suction of soils with Ip ≤ 7, an empirical equation considering essential inputs was proposed, as shown in Eq. 4. However, the estimation results may occasionally be inconsistent with theoretical and practical expectations. For instance, when the soil is fully saturated, the suction value is zero (i.e., the lower limit of the dataset). Due to potential fluctuations in the estimated value relative to the measured value, there exists a possibility of negative estimated suction values. Thus, it is necessary to enhance Eq. 4 by incorporating appropriate constraints, leading to an improved equation, Eq. 5. This modification ensures the non-negativity of the estimated suction value (The BFs and coefficient of Eq. 5 are presented in Table 5). 4.3

Precise estimation and analysis using SVR

As shown in Fig. 4, the SVR model provides a reasonably accurate estimation of matric suction for soils with Ip ≤ 7.

i 1 2 3 4 5 6 7 8 9 10 11 12 13 -

Basis function max(0, x10 -45.9) max(0, x2 -14.4) max(0, x2-14.4)·max(0, 72.2-x10) max(0, x2-14.4)·max(0, 38.2-x1) max(0, x2-14.4)·max(0, x10-72.2) ·max(0, 38.2-x1) max(0, x2-14.4)·max(0, 72.2-x10) ·max(0, x4-2.71) max(0, x2-14.4)·max(0, 72.2-x10) ·max(0, 2.71-x4) max(0,x2-14.4)·max(0, 72.2-x10) ·max(0,x4-2.71)·max(0,19.3-x1) max(0, x2 -14.4)·max(0, 38.2 -x1) ·max(0, x10 -59.4) max(0, x2-14.4)·max(0, 72.2-x10) ·max(0, x4 -2.71)·max(0, x1 -21.9) max(0, x2 -14.4)·max(0, 72.2 -x10) ·max(0, x4 -2.71)·max(0, 21.9-x1) max(0, x4-2.66) max(0, 2.66-x4) -

Coefficient α1 α2 α3 α4

Value -3.03 2.46 1.54 -0.34

α5

-0.02

α6

-17.14

α7

-20.79

α8

-47.15

α9

0.02

α10

0.14

α11

11.84

α12 α13 α0

505.48 3070.79 -3.46

Figure 4 Estimation of matric suction based on SVR using essential inputs Therefore, the proposed framework achieved efficient sensitivity analysis and precise estimation of matric suction for the soil with Ip ≤ 7 by combining the strengths of the SVR and MARS models. 5

SUMMARY

This study focuses on the development of AI models utilizing the SVR and MARS algorithms for reliable estimation of matric suction in unsaturated soils. A novel framework is proposed, which leverages the strengths of both AI algorithms to estimate matric suction. Subsequently, the suggested framework is employed to

estimate the matric suction of soils with Ip ≤ 7. Based on the findings, the following key conclusions can be drawn: (i) The two proposed AI models provide good results in estimating matric suction using fundamental soil properties. The SVR model can reliably estimate matric suction for various soils with high accuracy. Considering only the essential inputs, the MARS model is a simple tool for estimating the matric suction rapidly for use in engineering practice applications. (ii) Based on the MARS model, a practical and simple empirical equation was proposed to estimate the matric suction of soils with Ip ≤ 7 by utilizing essential inputs. (iii) By employing the proposed framework that combines the strengths of SVR and MARS models, it is

possible to estimate the matric suction of soils efficiently and accurately with Ip ≤ 7. The proposed model has some limitations that can be summarized as follows: First, the proposed model is inherently constrained by dataset limitations, such as difficulties in estimating soil properties for soils with Ip > 7 in comparison to soils with Ip ≤ 7. Second, estimation results can occasionally fail to align either with theoretical or practical considerations that may occur at the boundaries of the dataset range. It is crucial to incorporate some restrictions into the model that are consistent with the state-of-the-art understanding of unsaturated soil mechanics. 6

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Singh, D., & Kuriyan, S. J. (2003). Estimation of unsaturated hydraulic conductivity using soil suction measurements obtained by an insertion tensiometer. Canadian Geotechnical Journal, 40(2), 476-483. Sreedeep, S., & Singh, D. N. (2011). Critical review of the methodologies employed for soil suction measurement. International Journal of Geomechanics, 11(2), 99-104. Sun, D.,Sheng, D.,Cui, H., & Sloan, S. (2007). A density‐ dependent elastoplastic hydro‐mechanical model for unsaturated compacted soils. International Journal for Numerical and Analytical Methods in Geomechanics, 31(11), 1257-1279. Suwal, L. P., & Kuwano, R. (2019). Matric suction and stiffness measurement on soil containing fines at low stress state. Japanese Geotechnical Society Special Publication, 7(2), 297-304. Tao, G.,Li, Z.,Liu, L.,Chen, Y., & Gu, K. (2021). Effects of Contact Angle on the Hysteresis Effect of Soil-Water Characteristic Curves during Dry-Wet Cycles. Advances in civil engineering, 2021. Tripathy, S.,Al-Khyat, S., & Cleall, P. J. (2021). Impact of single and multiple specimen suction control oedometer testing on the measurement of the soil– water characteristic curve. Geotechnical Testing Journal, 44(6). Vanapalli, S.,Fredlund, D.,Pufahl, D., & Clifton, A. (1996). Model for the prediction of shear strength with respect to soil suction. Canadian Geotechnical Journal, 33(3), 379-392. Vanapalli, S. K.,Nicotera, M., & Sharma, R. S. (2008). Axis translation and negative water column techniques for suction control. In Laboratory and field testing of unsaturated soils (pp. 33-48). Springer. Vapnik, V., & Chapelle, O. (2000). Bounds on error expectation for support vector machines. Neural computation, 12(9), 2013-2036. Ye, Y.-X.,Zou, W.-L.,Han, Z., & Liu, X.-W. (2019). Predicting the entire soil-water characteristic curve using measurements within low suction range. Journal of Mountain Science, 16(5), 1198-1214. Zhang, C.,Hu, S.,Qiu, Z., & Lu, N. (2022). A poroelasticity theory for soil incorporating adsorption and capillarity. Géotechnique, 1-57. Zhang, J.,Zhang, S., & Xu, T. (2020). Individual effect of matric suction on soil microstructure and hydraulic conductivity. KSCE Journal of Civil Engineering, 24(5), 1448-1459. Zhou, A.-N.,Sheng, D.,Sloan, S. W., & Gens, A. (2012). Interpretation of unsaturated soil behaviour in the stress–saturation space, I: volume change and water retention behaviour. Computers and Geotechnics, 43, 178-187.

Wednesday, October 4, 2023

ADVANCED TESTING II

Effect of the degree of saturation and mineralogy on acid generation critical time Chevalier Corentin, Pabst Thomas Research Institute of Mines and Environment (RIME), Canada Department of Civil, Geological, and Mining Engineering – Polytechnique Montréal, Montréal, Québec, Canada ABSTRACT Filtered tailings allow to increase geotechnical stability and minimize footprint of tailings storage facilities. Despite their numerous advantages, they can also contribute to increase the risk for acid mine drainage (AMD) generation. In this research, multicomponent reactive transport simulations were conducted to assess the critical time tailings can remain exposed before AMD generation would start. Laboratory kinetics tests were performed on reactive mine tailings and the evolution of pore water geochemistry was monitored with time. Numerical simulations were calibrated on laboratory tests and then extrapolated for other mineralogies. Results showed that critical time could be predicted, as a function of tailings mineralogy and degree of saturation. A predictive equation to estimate critical time is proposed and discussed in this paper. Predicting critical time could be useful to optimize tailings management and improve the geochemical stability of filtered tailings storage facilities. RÉSUMÉ Les résidus filtrés permettent d’améliorer la stabilité géotechnique et de minimiser l’empreinte des parcs à résidus miniers. Malgré leurs nombreux avantages, la filtration des résidus peut aussi contribuer à augmenter le risque de génération de drainage minier acide (DMA). Dans cette étude, des simulations de transport réactif multicomposants ont été effectuées afin d'évaluer le temps critique avant que les résidus exposés génèrent du DMA. Des tests en laboratoires ont été réalisés sur des résidus réactifs et l’évolution de la qualité des eaux interstitielle au cours du temps a été mesurée. Des simulations numériques ont été calibrés sur les résultats de laboratoire, puis extrapolés pour d’autres minéralogies. Les résultats montrent que le temps critique peut être prédit en fonction de la minéralogie et du dégrée de saturation. Une équation de prédiction est proposée et discutée dans cet article. Prédire le temps critique pourrait permettre d'optimiser la gestion des résidus et d'améliorer la stabilité géochimique des parcs à résidus filtrés.

1

INTRODUCTION

The mining industry produces large amounts of wastes (Aubertin et al. 2011), including mine tailings, which are usually transported as a slurry (Simms 2017) and deposited in tailings storage facilities (TSF) surrounded by containment structures (Oldecop and Rodari 2017). Tailings dams are susceptible to geotechnical failure risks (Bowker and Chambers 2017; Chambers and Higman 2011; Rico et al. 2008), primarily caused by the unconsolidated and low geotechnical properties of embankment materials (typically mine waste), and the high water pressure in the TSF (Azam and Li 2010; Rico et al. 2008; Strachan and Goodwin 2015). Tailings dam failures remain frequent (Lyu et al. 2019; Williams 2021) and regularly lead to loss of life and considerable environmental damages (e.g., Brumadinho, Brazil in 2019 ; Thompson et al. (2020)). The industry is therefore considering innovative management approaches to ensure TSF short- and longterm stability (Williams 2021). Dewatering technologies, and particularly filtration, are an alternative to conventional slurry tailings management (Davies 2011; McPhail et al. 2019; Moreno et al. 2018). Decreasing water content indeed contributes to improve tailings physical stability, minimizes TSF footprint and maximizes water recirculation in ore processing (Gutierrez and Oldecop 2021; Lupo and Hall 2010; Schoenbrunn 2011; Ulrich and Coffin 2013).

Filtered tailings are typically characterized by a high solid content (Pm > 85%; Ulrich (2019)) and are disposed of using trucks or conveyors at a degree of a saturation between 60% and 80% (Davies 2011; Zorzal et al. 2020). Filtered tailings have a significantly stronger properties, are less prone to liquefaction, and thus typically do not require confinement (Williams 2021). However, exposed mine waste may negatively impact the environment, due to their mineralogy (Hoffmann et al. 1981; Nordstrom et al. 2015; Price 2009). Sulfide minerals (e.g., pyrite or pyrrhotite) are, indeed, common in tailings and their exposure to atmospheric condition may favor their oxidation (Lindsay et al. 2015) and the generation of acid mine drainage (AMD) (Blowes et al. 2014). Dissolution of neutralizing minerals, such as carbonates (e.g., calcite and dolomite) can, however, temporarily neutralize the acid and postpone the generation of AMD (Bouzahzah et al. 2014; Dold 2005; Lottermoser 2010). Prediction of tailings acidification and neutralization potential is therefore crucial for mining projects (Elghali et al. 2023). Static tests aim to predict the neutralization potential (NP) and the acidification potential (AP) (Bouzahzah et al. 2015; Elghali et al. 2018; Sobek et al. 1978), but give no indication of the delay before AMD would appear. Kinetic tests can typically be performed in the laboratory or in the field to quantify leaching parameters, reactivity and chemical species leaching rates (Benzaazoua et al. 2004; Chopard et al.

2015), but these are typically very time consuming (from several months to several years). This research therefore aimed to propose an approach to evaluate filtered tailings critical time (i.e., the time reactive filtered tailings can be left exposed before AMD generation starts) as a function of their mineralogy and degree of saturation. Laboratory kinetic cells were performed and numerical simulations were calibrated on experimental results. An analytical formula was derived from numerical models to propose a simple approach to predict critical time from tailings carbonate and pyrite contents. 2 2.1

by gas pycnometer (ASTM-D5550 2014) and was around Gs = 3.04. 2.2

Laboratory kinetic tests

Tailings geochemical behaviour was evaluated using kinetic tests. The objective was to monitor the evolution of leachate quality and determine the critical time, i.e., the time before pH became less than 6.0. Tailings were compacted in layers of approximatively 5.4 ± 0.2 cm thick and at a target porosity of around 0.44 ± 0.2 in small plexiglass cells (15 cm in height and 8 cm in internal diameter ; Figure 1).

MATERIALS AND METHODS Tailings properties

Acid generating mine tailings AGT were sampled from the concentrator of a polymetallic mine, located in Quebec Province, Canada. Tailings were kept under water to avoid oxidation during transport and were then homogenized and characterized in the laboratory. Acid generation potential was evaluated by tailings total sulfur and carbon contents measured on 3 duplicates using induction furnaces (LECO CS744 Analyzer). The AP was calculated by assuming that the total S content was originating only from sulfides (pyrite, according to XRD analysis; AP = %Stotal x 31.25) and sulfate concentration was deemed neglectable. NP was estimated based on the measured C content (NP = %C x 83.3), assuming that all the carbon in the sample originated from calcite and ankerite (Lawrence and Wang 1997). Mineralogical phases were identified by X-ray diffraction (XRD ; Bruker AXS D8 Advance diffractometer, precision 0.5-2%). Tailings presented a high acid potential (AP = 382 kgCaCO3 eq/t), with pyrite being the main sulfide (FeS2 ; 17.6 wt%) and some trace of chalcopyrite (0.2 wt%). The buffering capacity was low (NP = 14 kgCaCO3 eq/t) due to the limited content of carbonates (0.6 wt% of CaCO3 and 0.5 wt% of CaFe(CO3)2). Tailings also contained quartz (48 wt%), muscovite (17 wt%), albite (5 wt%), anorthite (4 wt%), chlorite (4 wt%), paragonite (3 wt%), orthoclase (1 wt%) and bassanite (1 wt%), but these did not contribute significantly to acid neutralization. Net neutralization potential (NNP = NP - AP) was very low with NNP = -368 kgCaCO3 eq/t, thus indicating that tailings were acid generating (NNP < - 20 kgCaCO3 eq/t ; Morin and Hutt (2001)). Tailings particle size distribution (PSD ; D10 = 0.0046 mm, D60 = 0.038mm) was evaluated on 3 duplicates (ASTM-D7928 2021). Tailings were typical of hard rock mine tailings (Bussière 2007; Qiu and Sego 2001) and were classified as non-plastic silts (ML ; ASTM-D2487 (2017)). Saturated hydraulic conductivity was estimated using KCM model (Kozeny-Carman Modified; Mbonimpa et al. (2002)), with a saturated hydraulic conductivity ksat = 5×10-7 m/s. Water retention curve was measured using a pressure plate extractor (ASTM-D6836 2016) and results were adjusted using van Genuchten (1980) mode (αvg = 0.002 cm-1, nvg = 1.55). The specific gravity was evaluated

Figure 1. Kinetic cell setup. A total of 5 cells were used to evaluate the effect of the degree of saturation on the critical time. Flushes were regularly applied (every two weeks or so) on top of the specimens with around 125 ml of distilled water (i.e., 1.25 times the tailings pore volume). Leachate was collected at the bottom of the cells after drainage through tailings pores and characterized pH, EC, sulfates (SO4), Ca and Mg concentrations. pH and EC were measured using Hanna HI5522 (pH and EC meter, accuracy of ±0.01 for pH and 1% for EC), SO4 concentration was determined using a spectrophotometer (HACH DR 3900, photometric accuracy 1%) and Ca and Mg concentrations using atomic absorption (PinAAcle 900 F Perkin Elmer, relative error 1.5-2%). Analyses were performed immediately after leachate had been collected. Both drainage and evaporation contributed to rapidly decrease the degree of saturation which was then maintained at the target value as described above. After the end of the experiments, cells were dismantled, and total carbon and sulfur were measured using the same approaches before the tests. 2.3

Numerical simulation approach

Numerical simulations were conducted using MIN3P, a multicomponent reactive transport model (Mayer 1999), which can efficiently simulate the hydrogeochemical behaviour of reactive tailings (Jurjovec et al. 2004; Kalonji-

Kabambi et al. 2020; Molson et al. 2008; Ouangrawa et al. 2009; Pabst et al. 2017). MIN3P simulates the effect of time on aqueous complexation, redox reactions and minerals (Mayer et al. 2012). Laboratory kinetic cells were simulated as onedimensional (1D) models in MIN3P. The length of the domain was 5.4 cm ± 0.2, and was discretized with 30 control volumes (i.e., a mesh size of 1.8 ± 0.1 mm). The surface of the exposed tailings was simulated as second type boundary (Neumann) for inflow (flushes) and mixed (Dirichlet/Cauchy) for reactive transport (e.g., O2 supply, relative humidity and air temperature, 25°C). Constant degree of saturation in the tailings was controlled by the bottom boundary conditions with a negative pressure head. The reactive transport bottom boundary allowed free advective mass outflux for aqueous phase (e.g., leachate drainage). Tailings hydrogeological parameters corresponded to laboratory characterization results. Initial pore water chemical composition corresponded to the first flush. Mineral volume fractions were based on XRD analyses and mineral dissolution-precipitation were kinetically controlled. Activity and equilibrium constants were obtained from MIN3P database and models included 12 primary and 3 secondary minerals, 2 gases (O2 / CO2) and 2 redox pairs (Fe2+/Fe3+ and HS-/SO42-) (Table 1). Table 1. Mineral reactions and equilibrium constants (Kim), from MIN3P databases. (am): amorphous; (aq): aqueous. Minerals

Reactions

Log Kim

Pyrite

FeS2 + 7/2 O2 + H2O ↔ Fe + 2H+

Calcite

CaCO3 ↔ Ca +

Ankerite

CaFe(CO3)2 ↔ Ca2+ + Fe2+ + 2CO32-

Chlorite

(Fe(Mg,Mn)5Al)(Si3Al)O10(OH)8 + 28H+ + 3O2(aq) → 5Mg2+ + Fe2+ + 5Mn2+ + 2Al3+ -2 + 3H4SiO4 +12H2O

Quartz

SiO2(am) + 2H2O ↔ H4SiO4

Muscovite

KAl2(AlSi3O10)(OH)2 +10H+ ↔ K+ + 3Al3+ -13.0 + 3H4SiO4(aq)

Anorthite

CaAl2Si2O8 + 8H+ → Ca2+ + 2Al3+ + 2H4SiO4(aq)

Albite

(NaAl)Si3O8 + 4H+ + 4H2O → Na2+ + Al3+ 2 + 3H4SiO4(aq)

Paragonite

NaAl2 (AlSi3O10)(OH)2 + 10H+ ↔ Na+ + 3Al3+ + 3SiO2 + 6H2O

Orthoclase

(KAl)Si3O8 + 4H + 4H2O → K + Al + 3H4SiO4(aq)

2+

2+

+

2SO42-

+

-215.3 8.5

CO32-

17.1

4.0

-2

+

17.5 3+

-2

Chalcopyrite CuFeS2 + 4O2(aq) ↔ Cu2+ +Fe2+ + 2SO42-

35.3

Bassanite

CaSO4 ↔ Ca2+ + SO42-

4.4

Gypsum1

CaSO4•2H2O ↔ Ca2+ + SO42− + 2H2O

4.6

Ferrihydrite1 Fe(OH)3 + 3H+ ↔ Fe3+ + 3H2O Jarositeh1 1 2

(H3O)Fe3(SO4)2(OH)6 + 5H+ ↔ 3Fe3+ + 7H2O + 2SO42−

Secondary mineral Irreversible dissolution

-4.9 5.4

A two-third power relationship was used for primary minerals effective rate constant (keff ; mol L-3bulk S-1), allowing to update the mineral reactive surface area with time (Lichtner 1996), while secondary mineral reactivity was assumed to be constant. 2.4

Calibration

Simulated hydraulic parameters were first calibrated to match leachate fluxes and degree of saturation observed in the laboratory. Then, geochemical system was calibrated with a particular focus on pH. Pyrite was responsible of the increase of sulfate concentration and the decrease of pH. Calcite and ankerite were identified as the neutralizing minerals and their depletion were linked to the continuous acidification conditions. The relatively high reaction rate of carbonates allowed to maintain pH near neutrality, until their depletion. Sulfates, Ca and Mg cumulative concentrations and carbonate depletion (not shown here) were also used to calibrate simulations and ensure models did reproduce well tailings geochemical behaviour. The effective rate constant of each mineral was iteratively adjusted until a good agreement was observed between simulations and experimental results. Calibrated simulations reproduced well measurements with time. The calibrated reaction rates were usually in the same order of magnitude as reported in other studies (KalonjiKabambi et al. 2020; Mayer et al. 2012). Pyrite reaction rate was 6.0 x 10-9 mol L-3bulk S-1 and 2.0 x 10-8 and 2.0 x 10-9 mol L-3bulk S-1 for calcite and ankerite respectively. Calcite reaction rate was higher than that of ankerite by one order of magnitude, which is common in mine waste (Amos et al. 2015; Mayer et al. 2012). Other reaction rates were 5.5 x 10-11 mol L-3bulk S-1 for chlorite, 5.0 x 10-7 mol L-3bulk S-1 for gypsum, 1.0 x 10-9 mol L-3bulk S-1 for ferrihydrite and 1.0 x 10-11 mol L-3bulk S-1 for other minerals. 3

TAILINGS OXIDATION AND CRITICAL TIME

Calibrated simulations (Figure 2 ; lines) reproduced well pH variation with time for all cells (Figure 2 ; dots). Initial pHs were between 7 and 8 for all cells and remained around neutrality for 58 to 77 days. The time when pH started to decrease depended on the degree of saturation. For example, when Sr = 93% and Sr = 95%, pH remained around neutrality value over 155 days, but when Sr = 79%, pH started to decrease after 58 days. Then pH rapidly decreased and reached within a few days values less than 4.0. After pH reached a value around 4, the decrease was slower and more progressive, until the end of the tests. Final pH for all cells was relatively similar and around 3.0. The critical time (i.e., the time required for pH to decrease below 6.0) was reached between day 72 and 95. Simulated and measured critical times were very close and usually less than 10 days different. For example, critical time at Sr = 83% (duplicate) was measured around 93 days (pH=6.08) and simulated at day 95.

time was 121 days for AP = 21 kg CaCO3 eq/t (NP/AP = 0.5) and 235 days for 11 kg CaCO3 eq/t (NP/AP = 1.0). The third step consisted of modifying the tailings NP. Results indicated that critical time was strongly delayed when NP increased. Critical time doubled when NP doubled (Figure 3). For example, critical time was 40 days for NP = 5 kg CaCO3 eq/t and 81 days for NP = 11 kg CaCO3 eq/t.

Figure 2. Measured (dots) and simulated (lines) pH as a function of time and degree of saturation. Critical time was determined as the moment when pH decreased below 6.0. Leachate remained around neutrality when Sr > 90%. Critical time corresponded to the moment carbonates in the tailings were depleted. For example, carbon content at the end of the test (i.e., after 155 days) had decreased by 71 wt%, 82 wt% and more than 95 wt% when degree of saturation was 83%-1, 83%-2 and 79% respectively. When Sr > 90%, the carbon content had decreased only by 23 wt%, indicating that the remaining content of carbonates was significant and sufficient to maintain pH around 7.0. 4

CRITICAL TIME DETERMINATION

Calibrated simulations were then used to evaluate the critical time at a larger scale and for various mineralogies. The effect of mineralogy was assessed by evaluating the geochemical behavior of tailings with various AP and NP. The volumetric fractions of sulfide and carbonates minerals were adjusted to maintain a constant simulated mass. The first step was to modify the degree of saturation, using the initial simulated values AP (339 kgCaCO3 eq/t) and NP (11 kgCaCO3 eq/t). Critical time was strongly influenced by the degree of saturation. Critical time was over 1,000 days when Sr = 100%, 585 days when Sr = 95% and 81 days when Sr = 88%. The critical time was almost independent of the degree of saturation when Sr < 88% and around 80 days. Critical time could therefore be significantly delayed if Sr > 90%. The second step aimed to evaluate the critical time evolutions with the AP variations. Critical time was only slightly influenced by AP. For example, critical time was 96 days for AP = 61 kg CaCO3 eq/t and 88 days for AP = 761 kg CaCO3 eq/t. Critical time increased only when the NP/AP ratio became greater than 0.5. For example, critical

Figure 3. Simulated critical time as a function of NP and degree of saturation. Critical time increased linearly with the content of neutralizing minerals in the tailings (Figure 4a and Figure 4c). NNP influence on the critical time was particularly significant for elevated neutralizing potential (NP > 11 kg CaCO3 eq/t). For example, critical time was around 350 days for NNP = -328 kg CaCO3 eq/t (Figure 4b), and 650 days at NNP = -10 kg CaCO3 eq/t (Figure 4d) when NP = 43 kg CaCO3 eq/t while the critical time was 41 days for NNP = -328 kg CaCO3 eq/t and 49 days NNP = -10 kg CaCO3 eq/t, when NP = 5 kg CaCO3 eq/t. Critical time may be described from NP and NNP values. A simple model to predict critical time Tc (when Sr < 90%) for tailings classified as acid generating tailings (NNP < -20 kg CaCO3 eq/t) was determined (Eq. 1):

Tc = NP (7.67 −

8.40

) + NP 2 (0.0065 −

NNP

2.11

)

NNP

[1]

The differences between the calculated and simulated critical time never exceeded 21 days, with a median of 2 days. Calculations were consistent with the experimental critical time which was observed in a period of 72 to 95 days.

Figure 4. Critical time as a function of NP and Sr, with NNP = -328 kg CaCO3 eq/t, and NNP = -10 kg CaCO3 eq/t) 5

DISCUSSION

Results showed that the degree of saturation delayed the critical time when Sr > 90%. In practice, increasing (filtered tailings are disposed of at Sr = 80% ; Amoah et al. (2018)) and maintaining a high degree of saturation may be complex in the field (Oldecop et al. 2017) and may have negative impacts on TSF geotechnical stability. Ensuring the regular deposition of a new tailings layers or starting reclamation before critical time is reached is therefore recommended. Extrapolations were performed to evaluate the critical time with different AP and NP, based on tailings total inorganic carbon and total sulfur contents. The critical time increased linearly with the increase of the NP. At the same time the critical time was also delayed by the increase of the NNP. The prediction of the critical time using the proposed model therefore requires a precise determination of the NP and AP in the field. However, critical time in a large TSF will be fluctuating, due to their natural variability. Variable amount of sulfide and carbonate minerals

generate a variable amount of acidity and contamination (Anawar 2015). The model was developed, using experimental and numerical simulations results and has therefore been validated only for mineralogies similar to that of the tested tailings (i.e., where calcite and ankerite are the main neutralizing minerals and pyrite the only sulfide). However, the models could be adapted by using additional tests for other mineralogies to take in consideration the presence of minerals other than carbonates or the presence of pyrrhotite which produces more acidity (Benzaazoua et al. 2001). 6

CONCLUSION

Filtered tailings are an alternative to conventional slurry tailings management which can contribute to increase geotechnical stability in TSF. Their low degree of saturation, however, expose them to the risk of AMD generation. The objective of this study was therefore to evaluate filtered tailings critical time, i.e., the time tailings

can remain exposed to atmospheric condition before they start producing acid (pH < 6). Results from laboratory kinetic tests were simulated using MIN3P reactive transport code and model extrapolations allowed to identify the key parameters influencing the critical time : • Critical time is mainly governed by the neutralization potential, and especially the carbonate contents. Critical time increase linearly with the NP, and greater proportion of carbonates will contribute to delay the critical time. • Critical time is also influenced by the net neutralization potential, especially when the difference between the NP and the AP tend to the uncertainty zone and the ratio NP/AP is greater than 0.5. • Critical time is also influenced by the degree of saturation. However, laboratory results and simulations both showed that critical time was significantly delayed only when Sr > 90%. Results presented in this study show that critical time is predictable and an adapted management in a filtered tailings TSF will ensure a geochemical stability in the long term. Further studies need to evaluate the evolution of the critical time for other mineralogies and the influence of wetting-drying cycles, water movement and frequency of fresh layer drop off. 7

REFERENCES

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Ulrich, B. 2019. Practical thoughts regarding filtered tailings. PASTE 2019: Proceedings of the 22nd International Conference on Paste, Thickened and Filtered Tailings. Australian Centre for Geomechanics, Perth, Australia. pp. 71-79. Ulrich, B., and Coffin, J. 2013. Considerations for tailings facility design and operation using filtered tailings. PASTE 2013: Proceedings of the 16th International Seminar on Paste and Thickened Tailings. Australian Centre for Geomechanics, Perth, Australia. pp. 201210. van Genuchten, M.T. 1980. A closed‐form equation for predicting the hydraulic conductivity of unsaturated soils. Soil science society of America journal, 44(5): 892-898. Williams, D.J. 2021. Lessons from Tailings Dam Failures— Where to Go from Here? Minerals, 11(8): 853. Zorzal, R., Mansur Gomes, M., and Pinheiro, J. 2020. Tailings Geotechnical Characterization from Cuiabá Mine to Support a Dry Stacking Disposal Design in Cuiabá Dam. PASTE 2020: 23rd International Conference on Paste, Thickened and Filtered Tailings. Gecamin Publications, Perth, Australia.

Assessing the Creep Properties of Filtered Tailings in Continuous Permafrost Regions: Methodological Developments and Preliminary Results Weber Anselmo dos Ramos Souza, Vincent Boulanger-Martel, Bruno Bussiere, Mamert Mbonimpa & Mutaz Nujaim Université du Québec en Abitibi-Témiscamingue - Research Institute on Mines and Environment (RIME-UQAT), 445 University Blvd, Rouyn-Noranda, QC, J9X 5E4, Canada ABSTRACT Filtered tailings are popular in the mining industry because they can reduce the risks associated with the physical stability of filtered tailings storage facilities (FTSFs), especially for mine sites located in remote and Arctic regions. Understanding the creep, long-term strength, and compressibility characteristics of frozen filtered tailings is essential in properly designing FTSFs and reclamation covers in continuous permafrost regions. This article first aims to present the experimental approach that was developed to assess the short-term creep properties of frozen filtered tailings using temperaturecontrolled uniaxial constant-load tests. Preliminary results indicated that unsaturated specimens experienced more significant displacement than saturated samples. The creep rate changed according to the stationary and non-stationary creeps. This study also provides insights on the ability of existing creep constitutive models to describe and predict the creep of frozen filtered tailings. RÉSUMÉ Les résidus filtrés sont populaires dans l'industrie minière car ils peuvent réduire les risques associés à la stabilité physique des parcs à résidus miniers (PAR), en particulier pour les sites miniers situés dans des régions éloignées et arctiques. La compréhension des caractéristiques de fluage, de résistance à long terme et de compressibilité des résidus filtrés gelés est essentielle pour une conception appropriée de PAR situés dans des régions de pergélisol continu et de recouvrements de restauration. Cet article vise d'abord à présenter l'approche expérimentale qui a été développée pour évaluer les propriétés de fluage à court terme de résidus miniers filtrés gelés en utilisant des essais à uniaxiaux à charge constante et température contrôlée. Les résultats préliminaires indiquent des déplacements plus importants pour les échantillons non saturés que saturés. Le taux de fluage varie selon les régimes stationnaire et non stationnaire. Cette étude vise également à évaluer la capacité des modèles constitutifs de fluage à décrire et à prédire le fluage des résidus filtrés gelés. 1

INTRODUCTION

Mining plays a vital role in contributing to the Canadian economy, producing around 60 minerals in 200 mines and acting as one of the global leaders in the production of potash, cadmium, cobalt, diamonds, and gold (NRC, 2022). Despite its contribution to the economy of around CAN$48.2 billion in 2019 (NRC, 2022), the industry faces multiple operational, geo-environmental, and social challenges. One of these challenges concerns the safe storage of mine tailings. Filtered tailings, which have a high solids content (≈ 80–85% relative to the total mass), differ considerably from conventional pulped tailings, which contain between 25–40% solids (Davies, 2011). Many mining companies have shifted to this tailings management method, which has increased the physical stability of filtered tailings storage facilities (FTSFs) compared with conventional tailings, especially for remote and Arctic mine sites (Bussière, 2007; Davies, 2011). Filtered tailings contribute to reduce the consumption of fresh water and the footprint of storage areas, and eliminate the need for

tailings dams (Lupo & Hall, 2010). FTSFs also provide the advantage of facilitating progressive reclamation. However, this method has several challenges related to the hydro-geotechnical behavior of the filtered tailings, one of which is compaction. Nujaim et al. (2022) pointed out that material densification could be challenging because of the freezing of tailings or the presence of excessive snow/water during compaction. Furthermore, ice lenses may be found in permafrost foundations, which can lead to complex mechanical, thermal, and hydrogeological processes that can make the behavior more complex, threatening long-term stability (Rykaart et al., 2018). For Arctic sites, these complexities could result in challenging conditions for mechanisms such as thaw consolidation, thaw settlement, and creep, which depend on soil type, structure, and density; amount of ice; temperature; and stress (Sayles and Haines, 1974). Understanding consolidation and creep and their impact on the thermal and hydrological behavior of FTSFs is critical for designing such structures in Arctic regions. It also is now well established that creep depends on several

key factors: not only temperature and magnitude of the applied stress but also soil type and structure, amount of ice, mineral type, strain rate, solute, density, and type and magnitude of loading (Sayles & Haines, 1974). French (2017) pointed out that the frozen ground could deform under gravity and stated that this deformation is mainly associated with the creep of pore ice and the migration of unfrozen pore water. The deformation of frozen soil can be divided into instantaneous initial deformation and deformation over a period of time (Equation 1). The first stage of deformation (𝜀0 ) represents elastic and plastic deformation, and the second (𝜀(𝑡) ) is associated with viscous deformation. Viscous deformation can also be divided into stationary and non-stationary deformations (Figure 1-a). 𝜀𝑡 = 𝜀0 + 𝜀(𝑡)

[1]

where 𝜀𝑡 is the total strain, 𝜀0 is the plastic and elastic strain, and 𝜀(𝑡) is the viscous strain (creep). Stationary creep is a type of creep in which the strain rate approaches zero asymptotically, as shown in Figure 1b. This means that the material continues to deform over time, but the rate of creep slows down until it reaches zero. Non-stationary creep is a type of creep in which the strain rate (rate of deformation) does not approach zero asymptotically but instead exhibits an S-like shape, as shown in Figure 1-c. During primary creep, the creep rate decreases over time. In secondary creep, the creep rate remains constant. Finally, in tertiary creep, the creep rate increases until the failure of the material.

Figure 1. Types of creep and long-term strength curve function of temperature (T) and stress (σ) (a), stationary creep curve (b), and non-stationary creep curve (c) divided into primary (1), secondary (2), and tertiary (3) creeps. The two types of creep behavior differ in terms of their longterm strength. The long-term strength is the stress at and below which the deformation rate decreases to zero over time and no failure occurs within any practical period of load application (Vialov, 1986a). Vialov (1986a) observed that the long-term strength of frozen soil is usually between 1/5 and 1/15 of the uniaxial compression strength (UCS). Figure 1-a illustrates that long-term strength could be shown as a line, but it could also be represented as a single value for a fixed temperature.

For uniaxial creep laboratory tests, there are two approaches to load a sample: single-load and multi-load. A single-load test is the most common method for assessing the creep of rocks, but this method requires several homogeneous samples. The behavior of the material is obtained via several tests performed on different samples at selected loads. The multi-step loading approach is adapted to reduce the number of test specimens needed by utilizing the same sample for different stress levels and initial strain levels. This approach is used widely nowadays to assess the creep properties of geomaterials (Yang et al., 2018), especially through the use of standard creep tests like ASTM D5520 (ASTM, 2018) and GOST-12248 (GOST, 2010). The major downfall of laboratory creep tests is that they are time-consuming and costly. Therefore, constitutive models are often used to predict the creep properties of geomaterials (Yang et al., 2012) . This study first aims to present the experimental approach that was developed to assess the short-term creep properties of frozen filtered tailings using a temperature-controlled uniaxial constant load testing apparatus and a multi-step-loading approach. Another objective of this study was to investigate the ability of existing creep models to describe the creep behavior of the tested samples. 2

BACKGROUND ON THE CREEP OF FROZEN GEOMATERIALS

Constitutive models are a mathematical representation of the behavior of the material (Briaud, 2013). According to Betten (2008), models can be analyzed in a microscopic or macroscopic view. Microscopic models focus on describing creep based on the laws of physics and established equations (Ladanyi, 1972). On the other hand, the phenomenological theory of creep (macroscopic models) can be considered as a collection of laws found, by experience, to adequately describe the observed manifestations of creep in simple mathematical terms, keeping the number of material parameters as small as possible (Ladanyi, 1972). Microscopic models are typically used to interpret material properties and macroscopic models are rather used for the calculation of stress and displacement of the same material (Scott, 1963). Several creep models have been developed to simulate experimental results of creep tests. These models include visco-plastic (Foriero et al., 1998), viscoelastic (Perzyna, 1963, 1966), damage creep (Miao et al., 1995), hypoplastic (Xu et al., 2018), and endochronic models (Gopal, 1985). The following sections will introduce the Vialov (1969, 1986b) model and the primary creep model, which have the potential to accurately compute the creep of frozen filtered tailings. The Vialov model is straightforward to fit and has established reference values, making it a valuable tool for this application. 2.1

Primary creep model

One of the first models for soil is the primary creep model (Vialov, 1969). This model combines the Voigt-Kelvin unit in series with a Maxwell unit and a blocking device, making it an element assembly model. In other words, this model has a physical background based on the Voigt and Newton

models for viscous materials. This creep model was initially developed to describe experimental data obtained from single-load creep tests, as observed in Vialov (1969). Equation 2 presents a primary creep model expression for single-load tests. The Yang et al. (2018) methodology to convert single-load models to multi-load models was applied for the primary creep model. The Vialov model is presented in Equation 3. This equation describes the creep response to an incremental increase in load over time. The model is useful for step-load tests because it can significantly reduce the number of tests needed to obtain reliable results. Table 1 provides typical model parameters for selected materials. [2]

1/𝑚

𝜀𝑡 = [

εt = [ +[

𝜎𝑡 𝜆 ] 𝜔(𝜃 + 1)𝑘

σb (t 1 )λ ] ω(θ+1)k

𝜎𝑏+1 (𝑡1 −𝑡)𝜆 𝜔(𝜃+1)𝑘

Modified Vialov model

Vialov (1986b) stated that the main characteristic of frozen soil deformation properties is the relationship between stress and total deformation, which is determined based on a time value. They also expressed that stress is directly related to time and deformation (Equation 4). Vialov (1986b) adapted the formulation of Equation 4 for step-load tests, which reduced the residual between measured and modelled data. Equations 5 and 6 present these modifications in detail. As opposed to the primary creep model, an estimation of the initial displacement is not necessary for this model (Yang et al., 2018). In Equations 4 to 6, A and 𝑚 are empirical parameters.

+ 𝜀0

[4]

σ= A (t). 𝜀𝑚

1/m

+ε0

1/𝑚

]

2.2

+ 𝜀1

𝜀𝑡 = [3]

𝜀𝑡 =

where: σb is the applied constant stress (kg/cm²), σb+1 is the applied constant stress in the following phase, t is the time (h) 𝑡1 is the time when a load was increased (h) θ is the temperature below the freezing point (water) (°C) ε0 is the instantaneous strain (m/m) ε1 is an instantaneous strain in the following phase (m/m) λ, m, ω and k are constants that are characteristic of the material.

3

𝜎𝑏 𝐴.𝑡 𝑚 𝜎𝑏+1 𝐴.𝑡 𝑚

+𝜀0 +𝜀0

[5] [6]

MATERIALS AND METHODS

This study used filtered tailings from the Raglan mine after being prepared according to standardized procedures. Step-load tests were conducted according to ASTM D5520 (ASTM, 2018) and GOST-12248 (GOST, 2010), and the test results were analyzed to determine the long-term strength. The ability of the Vialov (1969, 1986) models to fit experimental results was investigated. 3.1

Test material

The main advantage of this model is that it provides model parameters for several types of materials (Table 1). However, the practical use of the primary model (Equation 3) can be challenging because it requires determination of a new set of empirical parameters for each geomaterial (Andersland & Ladanyi, 2004). This model also often underestimates strain during the tertiary creep phase (Zhu & Carbee, 1987), which is unsuitable when important variations in creep stress occur (Xu et al., 2016).

Remolded filtered tailings from the Raglan mine were used in this study. The Raglan mine is a nickel-copper operation located in the Nunavik region, Northern Quebec (61°N; 73°W) that started production in 1997. The ground thermal regime at the mine site is characterized by continuous permafrost. The basic geotechnical properties and field properties of the Raglan filtered tailings were characterized by several authors (i.e., Coulombe, 2012; SNC, 2013; Nujaim et al., 2022). The basic geotechnical properties of the tailings are presented in Table 2.

Table 1. Primary creep model parameters for selected materials (Sayles, 1968; Sayles & Haines, 1974).

Table 2. Basic geotechnical properties of the Raglan mine tailings (from Coulombe (2012) and Nujaim et al. (2022)). Parameter Specific gravity (-) Optimum water content Maximum dry density Field dry density (kg/m3) Degree of compaction in situ Average water content in situ (-) Coefficient of uniformity CU Porosity in situ

Value 2.93 17.3% 1,832 kg/m3 1,650–1,700 90–93% 20.5% ± 3.8% 16.7 42%

3.2

Preparation of specimens

Samples were mixed at a water content of approximately 20% by weight. This represents the average in situ water content at Raglan Mine. All samples were compacted to the desired density. Saturated samples were subjected to a vacuum to obtain a high degree of saturation. After this phase, the specimens were placed in a freezing cabinet with a membrane to prevent surface evaporation. They were then quickly frozen at –27 ºC from top to bottom to avoid ice lens formation. After freezing, the mold was cut to facilitate the ejection the sample. The sample was trimmed to ensure its flatness and parallel alignment with the frozen plate. Each specimen had the nominal dimensions of 2 in in diameter and 4 in in length. Before the test, each sample was placed in the creep apparatus for 24 h. Samples were left without any applied load to allow the sample to reach thermal equilibrium with the desired test temperature. Each test was assigned a number: unsaturated samples from 1–3 and saturated samples from 4–5. Figure 2 shows the equipment used for step-load tests in frozen filtered tailings.

were placed at the surface of each sample and in the sample housing to monitor temperature fluctuations: the first was fixed in the sample, and the second was fixed on the wooden box. Creep tests are commonly conducted by applying a single constant load to a specimen, as shown in Figure 3-a. To reduce the number of samples required, a different approach was used to obtain results regarding the creep behavior of specific materials. One of these approaches was a step-load test, in which the applied load was incrementally increased several times for the same specimen, as shown in Figure 3-b. Creep tests were performed using ten loading stages, ranging from 300 kPa to 1.8 MPa (Figure 4). Each loading step was applied for 24 h. As required by ASTM (2018) and GOST (2010), each load increment was increased for no more than 30 seconds. This study considered a creep strength failure criterion of 20% strain (GOST, 2010). Any test reaching the failure criterion before the end of the loading sequence would be terminated. The termination of tests was also considered if a brittle failure occurred, or if the 10th loading stage was achieved. Surface area corrections (to adjust calculated stress) were also conducted for samples exhibiting changes in diameter exceeding 5% of the initial value. a

b

Figure 3. Load during single-load test (a) and steploaded test (b).

Figure 2. Apparatus used for creep tests (Nujaim et al., 2022). 3.3 Test method Specimens were tested using the temperature-controlled uniaxial creep apparatus described in Nujaim et al. (2022). All tests were performed in a controlled atmosphere cold chamber in which the set-up and samples were stabilized at –6 ºC using an insulated box and a circulating bath (Nujaim et al. 2022). The samples’ axial displacements were measured using a linear variable displacement transducer (LVDT) within a 50 mm range. Thermocouples

Figure 4. Load used for creep test. 3.4

Calculation of long-term strength

According to Vialov (1969), the creep rate stabilizes when the creep rate for long-term strength is less than 0.0001/h. This occurs after different time intervals depending on the type of soil: 6 h for sand, 12 h for sandy loam, and 24 h for loam and clay. This indicates that the creep has reached a stationary phase, and a long-term strength value can be determined.

GOST (2010) and Aksenov et al. (2016) describe a procedure to determine the long-term strength using shortterm tests (Equation 7). The time factor Kt is determined by considering the duration of each phase and the stress when the tertiary creep begins. The time factor for the 24 h phase is 0.6, which is used to estimate the long-term strength using Equation 7. σlt =Kt σm

[7]

where: σlt is the long-term stress, Kt is the time factor; and σm is the stress when minimum strain rate occurs. 3.5

Figure 3. Creep curves for all tested samples.

Model fitting

To evaluate the fitting process, Equation 8 was used, providing the possibility to observe the effect of parameter changes. Lower R values suggest a better fit between measured and computed data. For the primary creep model, the initial parameters used for fitting were based on Table 1. Vialov’s (1986b) model empirical parameters were estimated based on the Solver solution from Excel, minimizing the residual value.

R = Σ(𝑑1 − 𝑀)2

pore ice strength and weaker mechanical interaction between the ice and grains (Ting, 1981).

Figure 6 presents the creep rate from the last 6 h of each loading step as a function of the applied stress in the sample. Unsaturated samples presented a higher variability in creep rate than saturated samples. Results also show that creep rates above 0.001 h-1 (ten times more than the stabilization criteria of Vialov (1969)) resulted in sample failure. These results suggest that all specimens, either saturated or unsaturated, had at least 600 kPa of long-term strength at –6 °C.

[8]

where R is the sum of difference square between values of the model (𝑑1 ) for the time t and a point in a data (𝑀) for the time t. 4

RESULTS AND DISCUSSION

The uniaxial stepwise load test was performed on five frozen filtered tailings samples; the axial load and axial displacement were recorded. Figure 5 shows strain curves for unsaturated (samples 1–3) and saturated (samples 4– 5) filtered tailings. The results show that at any given load stage, the strain observed for unsaturated tailings was higher than for saturated tailings. Both saturated samples (4 & 5) showed similar strain until the 5th loading stage (120 h). Such behavior was observed for unsaturated samples (1 and 3) but only up to the 4th loading phase. Unsaturated sample failure occurred within a range of applied stress between 1,170 kPa (6th) and 1,480 kPa (8th). However, no saturated samples reached the 20% strain failure criterion, indicating that 1.8 MPa was insufficient to induce failure. The short-term creep results suggest that the frozen saturated samples supported at least 22% more weight than the unsaturated samples when compared to the maximum stress in those tests. Based on these results, Equation 7 was used to estimate the long-term strength, which varied between 750–960 kPa for the unsaturated samples and reached more than 1,080 kPa for the saturated samples. This suggests that the long-term strength of the frozen saturated tailings was greater than that of the unsaturated tailings. The difference in behavior between the saturated and unsaturated samples can mostly be attributed to the fact that geomaterials containing less ice usually have weaker

Figure 4. Creep rate of the last 6 h of each phase versus applied stress in unsaturated and saturated samples. 4.1 4.1.1

Model fitting Primary creep model

Table 3 shows the parameters that were used to fit the primary creep model to the experimental data. Figure 7 compares the results of the primary creep model to the experimental data for some selected samples (samples 3 (7-a), 4 (7-b), and 5 (7-c)). First, the model requires four empirical variables, alongside an initial deformation parameter that increments with each subsequent load augmentation. This implies that it is challenging to use methods like Solver from Excel to reach the desired results. Some parameters are related, so increasing one variable requires a decrease or increase in the others. Even with the procedure from Yang et al. (2018) for transforming single-load models into multi-load models, this model did not fit, especially for test 4, the creep rate has multiple changes due to the introduction of additional load phases. (Figure 6-b). However, Fig. 7-c shows the excellent fit for the saturated models when no changes in creep rate occurred in the sample. Table 3 lists the values of ω for the unsaturated and saturated samples. The values of ω can

be linked to the strain behavior during the test: a lower value of ω indicates that the model experienced more strain, as can be seen by comparing unsaturated and saturated samples.

a

4.1.2

Modifed Vialov creep model

Generally, the modified Vialov model (1986b) showed a good fit with experimental data (Figure 8). With only two parameters to change, this model is easier to fit than the primary creep model and presents good results. Because this model cannot simulate the tertiary creep (Zhu & Carbee, 1987), the last load step (the rupture) for curves of unsaturated samples were not used for fitting using residuals. Table 4 summarizes the values for A and m in this model. The Vialov model provided a better fit for sample 4 than the primary creep model; however, sample 5 exhibited a better fit with the primary creep model than the modified Vialov model at early times in the experiment.

a

b

b

c

c

Figure 5. Primary creep model and experimental data for some selected experimental samples: 3 (a), 4 (b), and 5 (c). Table 3. Variables used in the primary creep model. Unsaturated 𝑁𝑜 m

1

2

Saturated 3

4

Figure 6. Modified Vialov model (1986b) and experimental data for some selected samples: 3 (a), 4 (b), and 5 (c). Table 4. Variables used in the modified Vialov model (1986b).

5

Unsaturated

0.64 0.64 0.64 0.64 0.64

𝜆

0.3

0.3

0.3

0.3

0.3

ω

33

30

25

50

50

k

1

1

1

1

1

𝑁𝑜

1

m

0.529

A

2

3

0.539 8

1.91 𝑥 10

Saturated 4

0.491 8

1.02 𝑥 10

5

0.691 8

3.71 𝑥 10

0.742 7

4.43 𝑥 10

4.27 𝑥 107

A comparison of the residuals between the models and experimental data suggests that the modified Vialov model computes creep better than the primary creep model. Although samples 3 and 5 yielded similar residuals, a huge discrepancy was observed between the primary creep model and the modified Vialov model. This may have resulted from difficulty fitting the parameters: the primary creep model has four parameters, making it difficult to find a unique solution. Table 5. Residuals of the primary creep model and modified Vialov model.

5

Test

Primary creep

Modified Vialov

1

7.883

0.177

2

1.008

0.670

3

0.090

0.075

4

2.501

1.478

5

0.069

0.082

CONCLUSION

This study introduced creep behavior in frozen filtered tailings, estimated the long-term strength of these materials, and discussed some models for predicting their behavior. The results of this paper demonstrated that unsaturated samples exhibited failure for stress between 1,170 kPa and 1,480 kPa. On the other hand, saturated samples did not show a strain of more than 20% for 1.8 MPa. A procedure to determine the long-term strength values based on the short-term tests was presented. The tested stresses were not high enough to determine the long-term strength of the saturated samples. Two different models were tested to fit the experimental creep curves. The number of fitting parameters required for each model influenced the ability to achieve a good fit to the experimental data. The modified Vialov model appeared to better represent creep than the primary creep model. This work is part of an ongoing investigation on the creep of frozen filtered tailings. Additional experimental data are required to understand the complex mechanisms governing the creep of frozen filtered tailings. Tests with different temperatures, degrees of saturation, and stress states will contribute to further knowledge. The suitability of constitutive models to describe and predict the creep of filtered tailings should also be assessed. 6

ACKNOWLEDGMENTS

This study was funded by the FRQNT as a part of the Développement durable du secteur minier program and by the Research Institute on Mines and the Environment (RIME UQAT-Polytechnique; http://www.irme.ca). The personnel from the Raglan mine’s environment department is thanked for their collaboration.

7

REFERENCE

Aksenov, V., Iospa, A., Krivov, D., Ozeritskii, K., & Doroshin, V. 2016. Comparison of test results for uniaxial compression of frozen soil in response to constant load and to incremental loading. Soil Mechanics and Foundation Engineering, 53(2), 91-98. Andersland, O. B., & Ladanyi, B. 2004. Frozen ground engineering (2nd ed. ed.). Wiley. American Society of Civil Engineers.ASTM. 2018. ASTM D5520, Standard Test Method for Laboratory Determination of Creep Properties of Frozen Soil Samples by Uniaxial Compression. Betten, J. 2008. Creep mechanics (3rd ed. ed.). Springer. https://doi.org/10.1007/978-3-540-85051-9. Briaud, J.-L. 2013. Geotechnical engineering: unsaturated and saturated soils. John Wiley & Sons. Bussière, B. 2007. Colloquium 2004: Hydrogeotechnical properties of hard rock tailings from metal mines and emerging geoenvironmental disposal approaches. Canadian Geotechnical Journal, 44(9), 1019-1052. Davies. 2011. Filtered dry stacked tailings–the fundamentals. Proceedings tailings and mine waste (pp. 1-9). Foriero, A., Ladanyi, B., Dallimore, S. R., Egginton, P. A., & Nixon, F. M. 1998. Modelling of deep seated hill slope creep in permafrost. Canadian Geotechnical Journal, 35(4), 560-578. French, H. M. 2017. The periglacial environment. John Wiley & Sons. Gopal, R. K. 1985. Endochronic constitutive modeling of marine fiber reinforced concrete and frozen soil]. WorldCat.org. http://gateway.proquest.com/openurl?url_ver=Z3 9.882004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertatio n&res_dat=xri:pqm&rft_dat=xri:pqdiss:8606713 http://ufdc.ufl.edu/AA00047361 GOST. 2010. GOST-12248: Laboratory Methods for Determining Strength and Deformation Characteristics. Ladanyi, B. 1972. An Engineering Theory of Creep of Frozen Soils. Canadian Geotechnical Journal, 9(1), 63-80. https://doi.org/10.1139/t72-005 Lupo, J. F., & Hall, J. E. 2010. Dry stack tailings – design considerations. Miao, T., Wei, X., & Zhang, C. 1995. Creep of frozen soil by damage mechanics. Science in China (Scienctia Sinica) Series B, 8(38), 996-1002. NRC. 2022. Minerals and the economy. Government of Canada. https://www.nrcan.gc.ca/our-naturalresources/minerals-mining/minerals-metalsfacts/minerals-and-the-economy/20529 Nujaim, M., Boulanger-Martel, V., Bussière, B., Anselmo, W., & Pabst, T. 2022. Creep properties of filtered tailings: design of a temperature-controlled uniaxial constant load testing apparatus and preliminary results.

Perzyna, P. 1963. The constitutive equations for rate sensitive plastic materials. Quarterly of applied mathematics, 20(4), 321-332. Perzyna, P. 1966. Fundamental problems in viscoplasticity. Advances in applied mechanics, 9, 243-377. Rykaart, M., Barrero, A., & Lizcano, A. 2018. Frozen Core Tailings Dam: Part 2, Long-Term Creep Deformation. Proceedings of Tailings and Mine Waste. Sayles, F. 1968. Creep of Frozen Sands. Sayles, F., & Haines, D. 1974. Creep of frozen silt and clay. Scott, R. F. 1963. Principles of soil mechanics. AddisonWesley Pub. Co. Ting, J. 1981. The creep of frozen sands: Qualitative and quantitative models, part 2. Massachusetts Inst. of Tech. Report. Vialov, S. S. 1969. Methods of determining creep, longterm strength and compressibility characteristics of frozen soils. National Research Council of Canada. Vialov, S. S. 1986a. Rheological fundamentals of soil mechanics. Elsevier ;Distributors for the United States and Canada, Elsevier Science Pub. Co. Vialov, S. S. 1986b. Rheological fundamentals of soil mechanics. Elsevier ;Distributors for the United States and Canada, Elsevier Science Pub. Co. Xu, G., Wu, W., & Qi, J. 2016. Modeling the viscous behavior of frozen soil with hypoplasticity. International Journal for Numerical and Analytical Methods in Geomechanics, 40(15), 2061-2075. https://doi.org/10.1002/nag.2516 Yang, Q., Zhang, J.-M., Zheng, H., & Yao, Y. 2012. Constitutive modeling of geomaterials: advances and new applications. Springer Science & Business Media. Yang, W., Gamage, R. P., Huang, C., Luo, G., Guo, J., & Wang, S. 2018. Loading history effect on creep deformation of rock. Energies, 11(6), 1462. Zhu, Y., & Carbee, D. L. 1987. Creep and strength behavior of frozen silt in uniaxial compression.

Establishment of the mechanical and hydraulic apertures of concrete fractures using 3D scans and hydraulic tests Khalil El Mekari1, François Duhaime1 1École de technologie supérieure (ÉTS), Montréal, Québec, Canada ABSTRACT Water infiltration through concrete fractures in tunnels causes deteriorations such as rebar corrosion. Chemical grout can be injected to seal concrete fractures. The fracture aperture needs to be considered when planning the injection because it affects the hydraulic characteristics of fracture such as the hydraulic conductivity. Two types of apertures can be defined: mechanical and hydraulic apertures. The mechanical aperture is the average distance between the two bounding surfaces of the fracture. The hydraulic aperture governs the liquid propagation and the head loss during the injection. This article presents four methods to establish both apertures. For the mechanical aperture, the physical model of a concrete fracture was built and scanned using the Polyworks software. The second method consisted of X-ray computed tomography (CT scans) on core samples taken from tunnels of the Montréal Métro. For the hydraulic aperture, an in-situ test was developed based on the injection of water-glycerol mixtures. A variable-head permeability test was also developed to establish the hydraulic aperture. RÉSUMÉ Les infiltrations d’eau par les fissures du béton en tunnel provoquent des détériorations telles que la corrosion des armatures. L’injection de résine est une technique employée pour sceller les fissures de béton. L’ouverture de la fissure est un élément à considérer, car elle affecte les caractéristiques hydrauliques de la fissure telles que la conductivité hydraulique. Deux types d’ouverture peuvent être définies: mécanique et hydraulique. L’ouverture mécanique est la distance moyenne entre les deux surfaces qui délimitent la fissure. L’ouverture hydraulique régie la propagation du liquide et les pertes de charge durant l’injection. Cette article présente quatre méthodes pour établir ces deux ouvertures. L’ouverture mécanique a pu être établie à partir de modèle physique de fissure de béton et de scans avec le logiciel Polyworks. La deuxième méthode consiste à effectuer des microtomographies aux rayons X (CT scans) sur des échantillons provenant de tunnels du Métro de Montréal. Concernant l’ouverture hydraulique, un essai in-situ a été développé à l’aide d’injection de mélanges eau-glycérol. Un essai à charge variable a également été développé pour établir l’ouverture hydraulique. 1

INTRODUCTION

Water infiltrations through concrete fractures in tunnels can be controlled or stopped to prevent important damages such as rebar corrosion by collecting the water with gutters or by performing a chemical grout injection to seal the fractures. Chemical grout injection is a common method that is performed by pumping a liquid grout into the fracture. Thermosetting polymers such as polyurethane are often used (Panasyuk et al., 2013; von Fay, 2015). The fracture aperture is an important parameter to consider when an injection is performed in fractured porous media such as rock or concrete. It influences the fracture hydraulic conductivity, the liquid propagation, and the pressure distribution in time (Gao et al., 2023; Hao et al., 2018; Zhao et al., 2014). Two types of aperture definitions can be found in the literature: mechanical and hydraulic apertures (Berre et al., 2019; Gao et al., 2023; Huang et al., 2021; Huo et al., 2014). The mechanical aperture is the average distance between the two bounding surfaces of a fracture. It is obtained after establishing the aperture distribution in space. It is used to estimate the injection volume, to

establish the injection port drilling parameters and to simulate the flow with numerical models. In rock fractures, many authors showed that the aperture follows a normal or lognormal distribution (e.g. Berre et al., 2019; Huang et al, 2019; Li et al., 2019; Ye et al., 2015; Zhao et al., 2014). For example, the numerical model results presented by Huang et al. (2019) showed that the mean and standard deviation of a normally distributed aperture influenced the flow canalisation during liquid propagation in the fracture. The mechanical aperture of fractured rock samples can be established from X-ray computed tomography (CT scans) (Caulk et al., 2016; Crandall et al., 2017; Ramandi et al., 2017; Wanniarachchi et al., 2018). CT scans can be used to reconstruct a 3D model of the fracture and can be used to establish the flow characterisation such as the pressure distribution. For example, Wanniarachchi et al. (2018) built a numerical model to simulate the flow using a rock fracture reconstructed from CT scan results Such models have not been presented in the literature for concrete fractures to our best knowledge. The hydraulic aperture governs the liquid propagation and the head loss during the injection. In a fractured porous media, it can be obtained from the cubic law and Darcy’s

law for laminar flow in porous media after performing a hydraulic in-situ test (Cao et al, 2016; Maldaner, 2018; Quinn et al., 2011). For example, Cao et al. (2016) used a variable-head in-situ test to establish the hydraulic aperture and hydraulic conductivity of a rock fractured network. Both types of apertures can be significantly different. Sun et al. (2020) did a review of the mathematical equations available in the literature to establish the hydraulic aperture from the mechanical aperture and other parameters. Some authors such as Olsson and Barton (2001) and Cao et al. (2019) determined the relation between both apertures from hydromechanical shear tests. In rock fractures, it is possible to establish that the mechanical aperture is higher than the hydraulic aperture (Cao et al., 2016; Cao et al., 2019; Olsson and Barton, 2001; Sun et al., 2020; Zhao et al., 2014). To our best knowledge, there is no such relation for concrete fractures. This article presents four methods to establish both type of apertures. For the mechanical aperture, a 3D scan of the physical model of a concrete fracture was performed and analyzed with the Polyworks software. CT scans were done on two core samples from two different concrete fractures in a tunnel to establish their aperture distribution in space. Two hydraulic tests were developed to establish the hydraulic aperture. An in-situ test in a tunnel construction joint was performed with water-glycerol mixtures. Pressure reading ports allowed the pressure distribution in time to be recorded and the hydraulic aperture to be determined. The second test consisted of a variable-head permeability test on one of the core samples used for CT scans. Both methods established the hydraulic aperture from the cubic law and Darcy’s law. The objective of this article is to propose multiple methods to establish both types of apertures. More information on fracture aperture will allow for a more rational definition of injection parameters (e.g. pressure, volume, spacing of injection ports). 2 2.1 2.1.1

METHODOLOGY Mechanical aperture Physical model 3D scan

The physical model is composed of two concrete slabs cast on top of each other. The fracture plan is a square with sides of 61 cm. The model has a thickness of 12.7 cm. Allpurpose concrete mix (Bomix brand) was used. A plastic film was inserted between the two slabs to be able to separate them after the concrete curing. The 3D scan was performed in the Metrology Laboratory of École de technologie supérieure. It consisted in three steps. The first step was to scan the model with the two concrete slabs on top of each other. The second step was to separate the slabs and scan them separately. The last step was to compare the scans of both slabs joined together with the scan of the entire model using Polyworks. Figure 1 shows a photograph taken during the scan of the entire model. A total of 347,174 points were used for the scan.

Scanning device Physical model

Figure 1. 3D scan of the entire physical model 2.1.2

CT scans on core sample

Two core samples were taken from two different tunnels of the Montréal Métro. The first sample was taken from a concrete slab that was fractured on approximatively 120 m and had a thickness of approximatively 40 cm. The sample length and diameter were approximately 20 cm and 5 cm, respectively. The second core sample was taken from a construction joint in a mechanical ventilation station. The sample length and diameter were approximately 15 cm and 7.5 cm, respectively. The CT scans were performed by the Shape Memory Alloys and Intelligent Systems Laboratory of École de technologie supérieure. A Nikon XT-H-225 microfocus Xray source was used. The samples were scanned using a reflection scan source. The CT scan parameters were as follows: • • • • • • •

214 kV scan voltage. Effective pixel size of 51.9 µm. Copper filter of 2.5 mm to reduce the effect of beam hardening. 200 μA beam current. Gain of 24.02 dB. exposure time of 1.4 seconds. 2634 projections.

These specifications allowed reconstruct a 3D fracture that has a length of approximatively 60 mm and a resolution of 60 µm. The Pro 3D and Dragonfly software packages were used for the segmentation and reconstruction. The mesh thickness evaluation module in Dragonfly was used to obtain the mechanical aperture (𝑏𝑚 ) and its distribution in space. A total of 15,730,798 mechanical aperture values were calculated. Figure 2 shows one of the samples during CT scan preparation.

Reflection target

X-ray source

Table 1. Injection and pressure reading ports parameters Parameters

P3

P4

P7

P11

Distance from injection port (mm)

1632

1238

900

0

Drilling distance (mm)

525

472

525

530

Drilling angle (°)

39

53

39

37

Core sample

Figure 2. Core sample preparation for CT scans 2.2

Hydraulic aperture

2.2.1

In-situ test

An in-situ test was developed in the same construction joint used for CT scan sampling. It was performed before taking the sample. Surface capping was installed to seal the joint. Acid and water injections were done to clean the joint before the in-situ test. Multiple injection and pressure reading ports were installed in the construction joint but only four were used. The P11 port was the injection port and the P3, P4 and P7 ports were the pressure reading ports. Figure 3 shows the construction joint before the insitu test. Table 1 presents the parameters of the injection and pressure reading ports. The injected liquid were mixtures of water and glycerol. Using Cheng’s (2008) equations, it was possible to produce three types of mixtures with different dynamic viscosity and density. The injections were performed with a P300 hydracell pump that used a constant injection flow rate. The injection flow rate was between 2.61 and 3.91 mL/s. The injection duration was between 225 and 560 s. Both injection parameters depended on the injected liquid. The injection pressure varied during the injection. Table 2 presents the injected mixtures and their properties. The water and glycerol proportions are mass proportions.

Table 2. Properties of the injected mixtures Mixtures

Water (%)

Glycerol (%)

A

18

82

7.510-2

1220

B

10

90

2.210-1

1225

C

5

95

5.210-1

1250

Equations 1 and 2 present the cubic law and Darcy’s law to obtain the hydraulic conductivity (K) and the flow rate (Q). K is influenced by the hydraulic aperture (𝑏ℎ ), the gravitational acceleration (g), the liquid dynamic viscosity (µ) and the liquid density (ρ). Q is influenced by the hydraulic gradient (i) and the section area (A) that is equal to the joint width (0.242 m) multiplied by bh. i is equal to the head loss (dh) divided by the injection distance (L). ρ and 𝜇 were equal to 998 kg/m3 and 1.01 mPa s. 𝐾=

Injection ports

Figure 3. Capped construction joint before the in-situ test

[1] [2]

The next step was to combine Eq. 1 and 2 to have the fully developed equation of the flow shown in Eq. 3. It is possible to isolate 𝑏ℎ in Eq. 3 as presented in Eq. 4. 𝜌𝑔𝑏ℎ 2 𝑑ℎ 0.242𝑏ℎ 12µ 𝐿

3 49.39𝐿𝑄µ 𝑏ℎ = √ 𝑔𝜌𝑑ℎ

2.2.2. Capped constr. joint

𝜌𝑔𝑏ℎ 2 12µ

𝑄=𝐾𝑖𝐴

𝑄= Pressure reading ports

Dynamic viscosity Density (Pa s) (kg/m3)

[3]

[4]

Variable-head permeability test

A variable-head permeability test was developed with the first core sample used for CT scans. An initial water level was set at 60 cm and the reduction of the water level (𝐻) was monitored through time (𝑡) with transparent plastic tubing. A constant head of 6.5 cm was applied at the sample outlet. Surface capping was applied on the fracture sample to get a unidirectional flow through the fracture length. Figure 4 shows the test setup.

PVC pipe that contains the water

Core sample Constant head Figure 4. Variable-head permeability test setup Equation 5 presents another definition of the flow rate from the cubic law and Darcy’s law. Equation 6 gives the flow rate that can calculated from the rate of change of the water level in a pipe (∆𝐻⁄∆𝑡 ) during a variable-head test. The section of the pipe was 𝑆𝑖𝑛𝑗 = 19.6 cm² in this case. Equation 7 presents the final equations to use when performing a permeability test.

𝑄=

𝜌𝑔𝑏ℎ 3 𝑤𝐻 12𝜇𝐿

[5]

∆𝐻 ∆𝑡

[6]

𝑄 = −𝑆𝑖𝑛𝑗 𝜌𝑔𝑏ℎ 3 𝑤𝐻 12𝜇𝐿

∆𝐻 = −𝑆𝑖𝑛𝑗

∆𝑡

[7]

For this article, the Hvorslev (1951) method was used. The hydraulic aperture (𝑏ℎ ) can be calculated from the slope value (𝑆𝑉) obtained from the Hvorslev graph:

3 12𝑆𝑉𝜇𝐿𝑆𝑖𝑛𝑗 𝑏ℎ = √ 𝜌𝑔𝑤

3 3.1

[8]

RESULTS AND DISCUSSION Mechanical aperture

Figure 5 shows the result of the 3D scan. The mechanical aperture varied from 0 to 1.5 mm. The aperture is higher on the model extremities compare to the middle The aperture in the middle of the model was 0.50 mm according to the map presented in Figure 5. The aperture on the physical model extremities varied between 0.50 and 1.50 mm. A third area can be found on the model corners where the aperture is greater than 1.5 mm. The cubic law equation suggests that the fracture is more permeable when the aperture is higher. Therefore, the hydraulic conductivity should be lower in the middle and higher on the extremities of the physical model.

Figure 5. 3D scan of the physical model The mechanical aperture histogram for the concrete slab and the construction joint samples are presented in Figures 6a and 6b. Only the values between 0 and 2.5 mm are shown for Figure 6a and 0 and 1 mm for Figure 6b. Both fracture distributions in space were lognormal. Figure 6b displays additional information such as the median value. For Figure 6a, the CT scans results established 𝑏𝑚 at 0.62 mm and a standard deviation of 0.35 mm. For Figure 6b, 𝑏𝑚 was established at 0.59 mm with a standard deviation of 0.25 mm. Figures 7a and 7b presents 3D reconstructed fracture of both samples. For the concrete slab sample, the minimum and maximum apertures detected by the CT scans were 0.16 mm and 3.72 mm. For the construction joint sample, the minimum and maximum apertures detected by the CT scans were 0.21 mm and 2.10 mm. The methods presented to establish 𝑏𝑚 can be used for different purposes. The 3D scan can cover a larger fracture surface and produce an entire map of aperture if needed. However, the level of precision is lower due to the number of points per surface scanned. The 3D scan produced approximatively 93 apertures/cm2 and the CT scans produced more than 500,000 apertures/cm2. Due to the small surface that can be scanned, the CT scans are better suited for the study of the aperture at a precise location of the fracture. Additionally, the application of the 3D scan method is slightly different in tunnels for an injection. A concrete fracture in tunnel is not accessible and cannot be separated as it was for the physical model. It is only applicable, for example, on samples that can be separated to perform the 3D scan. For both methods, multiple core samples are needed to establish the mechanical aperture of a concrete fracture with a high fracture surface.

a)

a)

b)

20

Frequency (%)

15

10

5

0

b)

0.5

1

Aperture (mm)

1.5 Aperture (mm)

2

2.5

14

12

Frequency (%)

10

8

6

4

2

0.25

0.5

0.75

1

Aperture (mm)

Figure 6. Mechanical aperture histogram, a) Concrete slab sample, b) Construction joint sample

a)

b)

Figure 7. 3D reconstruction of the fracture from CT scans, a) Concrete slab sample, b) construction joint sample

Hydraulic aperture

Time (s)

The pressure time series for port P7 were used to establish 𝑏ℎ from the in-situ test. Figure 8 shows the pressure time series for the three water-glycerol mixtures. The pressure magnitude is related to the dynamic viscosity of the liquid. Higher dynamic viscosity values are associated with higher pressure values. The pressure stabilized between 50 s and 150 s. Table 3 shows the 𝑏ℎ values calculated from Eq. 4 for each mixture at 50, 100 and 150 s. 𝑏ℎ varied between 0.46 mm and 0.51 mm. 𝑏ℎ was set at 0.50 mm. The hydraulic conductivity obtained from Eq. 1 was different for each injected mixture. It was 0.28, 0.11 and 0.05 cm/s for mixtures #A, #B and #C, respectively. The hydraulic conductivity of the fracture decreases with increasing dynamic viscosity. 2000

A

1800

B

Pressure (kPa)

1600

C

1400 1200 1000 800 600 400 200 0

0

25

50

75

100

125

150

Injection time (s)

Figure 8. Pressure time series for port P7 Table 3. Hydraulic apertures (mm) for different injection duration (s) Mixtures

50

100

150

A

0.49

0.48

0.46

B

0.49

0.50

0.51

C

0.46

0.47

0.46

The velocity and Hvorslev graphs are presented in Figures 9 and 10. The velocity graph permitted to establish the piezometric error and adjust the values to produce the Hvorslev graph. the piezometric error was 0.14 m. SV was obtained from Figure 10 and 𝑏ℎ was calculated according to Eq. 7. 𝑏ℎ and K results were 0.11 mm and 0.93 cm/s for water. 0.6 Hm= -11355(ΔH/Δt) + 0.14

0.5

0.3 0.2

Hm (m)

0.4

0.1 0.0 -4

-3.5

-3

-2.5 -2 -1.5 ΔH/Δt ( x 10-5 m/s)

-1

-0.5

Figure 9. Velocity graph obtained from the permeability test

0

5000

10000

15000

20000

25000

0.00 -0.50

-1.00 Ln(Hr)

3.2

Ln(Hr) = -0.000124s - 0.32

-1.50 -2.00 -2.50 -3.00 -3.50

Figure 10. Hvorslev graph obtained from the permeability test The methods presented to obtain 𝑏ℎ are aimed for different purposes. The values obtained from the variablehead permeability test are only applicable for the studied area that it was taken from. If the fracture surface is important, multiple core samples are needed to establish the proper 𝑏ℎ values. On the other hand, the in-situ test captured the heterogeneity of the whole fracture media by performing the test on the entire fracture surface. Furthermore, the in-situ test took into account the in-situ characteristics of an injection such as the ports volume and volume losses that are not present during a variable-head permeability test. 4

CONCLUSION

This article presented four methods to obtain the mechanical and hydraulic apertures. The aperture needs to be considered because of its effects on the injection results. It is the professional responsible of the injection to determine which method is better suited for his requirements and needs. The mechanical aperture obtained from the 3D scans can be performed on a larger fracture surface but was less precise compared to the CT scans method. Core sampling is needed to perform both methods. The hydraulic aperture obtained from the in-situ test requires more time, preparation and funding but considers the in-situ characteristics of an injection compared to the hydraulic apertures established from the variable-head permeability test. Additionally, the in-situ test permitted to establish the pressure distribution in time and the head loss during the flow in the fracture. 5

ACKNOWLEDGMENTS

The authors want to thank the Ministère des Transports du Québec (MTQ), The National Sciences and Engineering Research Council of Canada (NSERC) and the Société de transport de Montréal (STM) for the funding of this project. The authors would also like to thank Jonathan Auger who helped with the construction of the physical models, Joel Grignon who performed the 3D cans and Sébastien Ménard and Richard Prowt whose helped with the in-situ test.

6

REFERENCES

Berre, I., Doster, F., and Keilegavlen, E. 2019. Flow in fractured porous media: a review of conceptual models and discretization approaches. Transport in Porous Media, 130(1), 215-236. Cao, Y. B., Feng, X. T., Yan, E. C., Chen, G., Lü, F. F., Ji, H. B., and Song, K. Y. 2016. Calculation method and distribution characteristics of fracture hydraulic aperture from field experiments in fractured granite area. Rock Mechanics and Rock Engineering, 49, 1629-1647. Cao, C., Xu, Z., Chai, J., & Li, Y. 2019. Radial fluid flow regime in a single fracture under high hydraulic pressure during shear process. Journal of Hydrology, 579, 124142.Caulk, R. A., Ghazanfari, E., Perdrial, J. N., and Perdrial, N. 2016. Experimental investigation of fracture aperture and permeability change within Enhanced Geothermal Systems. Geothermics, 62, 12-21. Crandall, D., Moore, J., Gill, M., and Stadelman, M. 2017. CT scanning and flow measurements of shale fractures after multiple shearing events. International Journal of Rock Mechanics and Mining Sciences, 100, 177-187. Gao, M., Zhang, C., and Oh, J. 2023. Assessments of the effects of various fracture surface morphology on single fracture flow: A review. International Journal of Mining Science and Technology, 33(1), 1-29. Hao, M., Li, X., Zhong, Y., Zhang, B., Jin, D., and Chen, G. 2018. Numerical simulation of polymer grout diffusion in a single fracture. AIP Advances, 8(10), 105329. Huang, N., Jiang, Y., Liu, R., and Li, B. 2019. Experimental and numerical studies of the hydraulic properties of three-dimensional fracture networks with spatially distributed apertures. Rock Mechanics and Rock Engineering, 52, 4731-4746. Huang, N., Liu, R., Jiang, Y., and Cheng, Y. 2021. Development and application of three-dimensional discrete fracture network modeling approach for fluid flow in fractured rock masses. Journal of Natural Gas Science and Engineering, 91, 103957. Huo, D., Li, B., and Benson, S. M. 2014. Investigating aperture-based stress-dependent permeability and capillary pressure in rock fractures. SPE annual technical conference and exhibition. Netherlands. Hvorslev, M. J. 1951. Time lag and soil permeability in ground-water observations (No. 36). USA. Waterways Experiment Station, Corps of Engineers, US Army. Li, X., Jiang, Z., and Couples, G. G. 2019. A stochastic method for modelling the geometry of a single fracture: spatially controlled distributions of aperture, roughness and anisotropy. Transport in Porous Media, 128(2), 797-819. Olsson, R., & Barton, N. 2001. An improved model for hydromechanical coupling during shearing of rock joints. International Journal of Rock Mechanics and Mining Sciences, 38(3), 317-329. Panasyuk, V. V., Marukha, V. I., and Sylovanyuk, V. P. 2013. Injection technologies for the repair of damaged concrete structures. Ukraine. Springer.

Quinn, P. M., Parker, B. L., and Cherry, J. A. 2011. Using constant head step tests to determine hydraulic apertures in fractured rock. Journal of Contaminant Hydrology, 126(1-2), 85-99. Ramandi, H. L., Mostaghimi, P., and Armstrong, R. T. 2017. Digital rock analysis for accurate prediction of fractured media permeability. Journal of Hydrology, 554, 817-826. Sun, Z., Wang, L., Zhou, J. Q., and Wang, C. 2020. A new method for determining the hydraulic aperture of rough rock fractures using the support vector regression. Engineering Geology, 271, 105618. von Fay, K. F. 2015. Guide to Concrete Repair (2nd edition). U.S. Bureau of Reclamation. USA. Wanniarachchi, W. A. M., Ranjith, P. G., Perera, M. S. A., Rathnaweera, T. D., Zhang, C., and Zhang, D. C. 2018. An integrated approach to simulate fracture permeability and flow characteristics using regenerated rock fracture from 3-D scanning: A numerical study. Journal of Natural Gas Science and Engineering, 53, 249-262. Ye, Z., Liu, H. H., Jiang, Q., and Zhou, C. 2015. Two-phase flow properties of a horizontal fracture: The effect of aperture distribution. Advances in Water Resources, 76, 43-54. Zhao, Z., Li, B., and Jiang, Y. 2014. Effects of fracture surface roughness on macroscopic fluid flow and solute transport in fracture networks. Rock Mechanics and Rock Engineering, 47(6), 2279-2286.

A modified framework to describe stressstrain behavior and volumetric response of hydrate bearing sand Maral Goharzay1*, Jeffrey Priest1, Richard Wan1 Department of Civil Engineering, University of Calgary, Calgary, AB, Canada ABSTRACT Gas hydrate-bearing sands (GHBS) contain enormous reserves of methane gas, making them an attractive energy resource. The mechanical properties of these sands are strongly influenced by the hydrate, with increases in hydrate saturation (𝑆ℎ ) leading to higher strength, strain softening, and dilation. Field-scale tests for methane recovery from GHBS have faced unexpected technical failures, emphasizing the need for numerical simulations to assess long-term feasibility and minimize risks. Previous constitutive soil models modified the Mohr-Coulomb (MC) model by incorporating a relationship between cohesion and hydrate saturation, however the occurrence of cohesion is disputed, and so the actual stress-strain response was not truly captured. Recent researchers have utilized Rowe's stress-dilatancy theory showing that strength increase in GHBS could be attributed to kinematics rather than cohesive influences. However, this assumption may not be correct, especially at the initial stages of shearing. In this paper, a stress-dilatancy model is introduced that captures the unique hydrate characteristics, soil density, and applied confining pressure. This model better represents the geomechanical behavior of GHBS, allowing its implementation in elastoplastic models for realistic numerical simulations and analysis. Les GHBS, riches en méthane, offrent une source d'énergie prometteuse. Leurs propriétés mécaniques sont influencées par les hydrates, augmentant la résistance, réduisant la déformation et provoquant une dilatation avec une saturation accrue (Sh). Les tests de récupération de méthane ont rencontré des échecs techniques, nécessitant des simulations numériques pour évaluer la faisabilité à long terme et réduire les risques. Les modèles antérieurs ont modifié le modèle de Mohr-Coulomb (MC) en intégrant la relation entre cohésion et saturation des hydrates, bien que l'existence de la cohésion soit contestée, limitant la capture précise de la réponse contrainte-déformation. Des chercheurs récents ont proposé la théorie de dilatance de Rowe, suggérant que l'augmentation de résistance dans les GHBS est due à des influences cinématiques plutôt que cohésives. Un modèle de dilatance contrainte, prenant en compte les caractéristiques uniques des hydrates, la densité du sol et la pression de confinement, permet une meilleure représentation du comportement géomécanique des GHBS et son utilisation dans des simulations numériques et des analyses réalistes.

1

INTRODUCTION

Gas hydrates, particularly methane gas hydrates, have garnered international attention as a potential abundant and valuable energy resource. Hydrates are ice-like crystalline structures that form when low-density gases, like methane, combine with water under specific temperature and pressure conditions in deep-water marine sediments and below the permafrost. The introduction of hydrate into the pore space of a sand leads to significant enhancement mechanical response of the sediment in which it resides, such as its strength and stiffness. As such, the extraction of methane from hydrate, which involves the dissociation of hydrate, can lead to complex processes that significantly affect the mechanical behavior of gas hydratebearing sediments (GHBS). These changes lead to sediment instability, leading to potential slope failures and engineering problems associated with wellbore integrity. Although field-scale tests have been conducted to evaluate the feasibility of methane recovery, these are typically of short duration due to the high costs, and in some instances prematurely ended due to production issues related to the instability of the GHBS following hydrate dissociation.

Numerical simulations are required to assess long-term viability and minimize risks of hydrate production (Collett, 2002) that are dependent on geomechanical models that can describe the stress-strain behavior of GHBS. Several geomechanical models have been developed to account for the behavior of GHBS, which typically integrate the Mohr-Coulomb failure criteria with relationships that link a cohesion parameter with hydrate saturation (𝑆ℎ ). However, there is much debate in the literature as to whether hydrate provides cementation (cohesion) to soil particles or can be considered as an additional ‘grain-like’ phase that is loadbearing. In this paper, the development of a conceptual model to better capture the stress-strain response of GHBS is presented. The model was developed using the stressstrain response obtained from laboratory tests conducted on water-saturated GHBS (Abbas, 2018). The improved model incorporates the mobilization and loss of cohesion during shearing as well as evaluating reduction in ‘apparent’ void ratio of the GHBS due to the presence of gas hydrate. This is crucial for numerical simulations that seek to assess the long-term impact of hydrate production.

2 BACKGROUND 2.1 Soil Behavior Shear strength is a fundamental concept in soil mechanics, representing the maximum shear stress that soil can withstand before failure. The triaxial test (Figure 1a) is a widely used method to assess soil strength, which involves subjecting a soil sample to a confining stress while shearing the specimen under an increasing axial load. Deviatoric stress (𝑞 = 𝜎1′ - 𝜎3′ , where 𝜎1′ and 𝜎3′ are maximum and minimum principal effective stresses, respectively), axial strain (𝜀𝑎 ), and volumetric strain (𝜀𝑣 ) are measured after the test. a

b

The Mohr-Coulomb (MC) failure criterion is the most widely used soil model, and represents the linear relationship between the shear stress (𝜏𝑓 ) and the normal stress (𝜎𝑛′ ) at failure acting on a failure plane through a granular soil, given by: [1]

𝜏𝑓 = 𝜎𝑛′ 𝑡𝑎𝑛(𝜑 ′ )

where 𝜑 ′ is the angle of internal friction of the material mobilized at failure. As highlighted above most natural soils exhibit a degree of bonding such that a shear stress can arise at 𝜎𝑛′ = 0. Therefore, to account for this bonding a cohesion term (𝑐’) is applied such that Eq. 1 is typically rewritten as: [2]

𝜏𝑓 = 𝑐 ′ + 𝜎𝑛′ 𝑡𝑎𝑛(𝜑 ′ )

The MC failure criterion is only strictly valid for soils that are sheared to their critical state and cannot capture the peak stresses associated with a dense sand compared to the loose. To address this weakness, Rowe (1962) proposed a stress-dilatancy model for frictional soils, which is applicable to the prepeak stress-strain behavior, as follows: [3]

𝑅 = 𝐾𝐷 σ′1

where R = Figure 1. a) Schematic of triaxial test, b) Typical stressstrain response of drained granular materials during triaxial test. In granular soils the mobilized shear stresses are a function of the frictional resistance at grain contacts that resist sliding of particles. Differences in stress-strain response occurs depending on whether the sand is loose or dense, which relates to the packing of soil grains in a unit volume. As a loose sand is subject to axial load the soil particles are able to rotate and slide into the available void space leading to compaction of the specimen and an increase in shear stress. This compaction process continues until the sand reaches its critical state at which point shear stresses and specimen volume remain constant under increasing axial strain (Figure 1b). In contrast, for dense materials after an initial compaction, soil particles need to ride up over each other to enable shearing. This expansion (dilation) requires increased shear stresses, until the specimen is at its critical state, where shear stresses and volume change are constant. Dense sand therefore exhibits a peak stress due to the dilation of the sand and post peak strain softening behavior as the specimen reaches constant volume conditions (Figure 1b). In nature, most soils inherently possess a degree of cementation that enhances the strength of soil by promoting interparticle bonding and increasing resistance to shear deformation. This leads to higher shear strength and a stiffer response with less compressibility for cemented sand compared to uncemented sand.

𝐷 =1−

σ′3

is the principal stress ratio.

𝜀̇ 𝑣

[4]

𝜀̇ 𝑎 𝜋

𝜑𝑐𝑣

4

2

𝐾 = 𝑡𝑎𝑛2 ( +

[5]

)

𝜀̇𝑣 and 𝜀̇𝑎 are volumetric and axial strain rates, respectively (positive in contraction) and 𝜑𝑐𝑣 is the critical state friction angle. Also, Rowe (1962) proposed a second model to address cohesive materials. This model incorporates the concept of true cohesion, represented by the parameter 𝑐, along with dilatancy and intergranular friction, as follows: σ′1 σ′3

π

φcv

4

2

= [tan2 ( +

)+

2c σ′3

π

φcv

4

2

tan ( +

ε̇

)] (1 − v) ε̇ a

[6]

Further modifications to Eqs. 3-5 have been proposed to better capture observed behavior. Wan and Guo (1998) proposed a modification to account for the differences in the initial void ratio of the specimen (𝑒) relative to the critical void ratio (𝑒𝑐𝑟 ) given by: 𝜎

( 1)

𝜎3 𝑚

with

∗ 𝐷; = 𝐾𝑐𝑣

∗ = 𝐾𝑐𝑣

1+𝑆𝑖𝑛𝜑∗ 1−𝑆𝑖𝑛𝜑∗

[7]

α

Sinφ∗ = (e⁄ecr ) . Sinφcv

in which 𝑎 is a parameter to be determined. The angle 𝜑 ∗ corresponds to the friction angle mobilized along a certain macroscopic plane which evolves during deformation. Zhang & Salgado (2010) introduced a modification to capture the impact of both cohesive and frictional strength components, as follows:

𝜎1′ 𝜎3′

𝜋

𝜑𝑐𝑣

4

2

= 𝑡𝑎𝑛2 ( +

𝜀̇

2𝑐

𝜀̇ 𝑎

𝜎3′

) (1 − 𝑣 ) +

𝜋

𝜑𝑐𝑣

4

2

𝑡𝑎𝑛 ( +

)√1 −

𝜀̇ 𝑣 𝜀̇ 𝑎

[8]

2.2 Mechanical properties of GHBS Extensive research has been conducted to investigate the impact of gas hydrate on the mechanical behavior of GHBS (Ghiassian & Grozic, 2013; Hyodo et al., 2013; Li et al., 2016; Miyazaki et al., 2010; Priest et al., 2014; Winters et al., 2004). Figure 2a illustrates a typical stress-strain behavior of GHBS as a function of hydrate saturation (𝑆ℎ ). It can be seen that as 𝑆ℎ increases, higher shear strength and stiffness are observed, with specimens exhibiting a peak strength at low axial strains, followed by post-peak strain softening. Notably, the behavior of the soil transitions from contractive to dilative as the 𝑆ℎ increases. In contrast, hydrate-free soils typically exhibit dominant strain hardening behavior with maximum shear stresses occurring at higher strains. Figure 2b depicts the stressstrain response of highlighting that increasing the effective stress for a given 𝑆ℎ , leads to an increase in shear strength while dilation is reduced.

Various hydrate morphologies (Figure 3) have been considered to define the interaction between hydrate and the host soil that account for differences in shear strength observed in GHBS: pore filling, load bearing, and cementation. Pore-filling occurs when hydrates nucleate on the boundaries of soil particles and freely grow into the pore space, such that the soil behaves similar to a normal sand (Miyazaki et al., 2011) until 𝑆ℎ values > 25% are encountered (Berge et al., 1999 ; Yun et al., 2005; Yun et al., 2007) at which point the strength and stiffness of the GHBS starts to slowly increase. Load-bearing considers that hydrate bridges between soil particles and supports the soil matrix. This leads to a more rapid increase in strength and stiffness compared to the pore filling morphology. It is suggested that load-bearing, in a practical sense, is an extension of pore filling at higher hydrate saturations (Yun et al., 2005).Cementation is characterized by hydrate formation at particle contacts (Chaouachi et al., 2015) that acts as a bonding agent within the soil matrix, which results in a rapid increase in the strength and stiffness of the soil. Even a small level of hydrate saturation can significantly enhance sediment stiffness and strength in this morphology (Jiang et al., 2014; Li et al., 2016; Priest & Hayley, 2019; Priest et al., 2009).

Figure 3. Main types of hydrate morphology: a) pore filling; b) load bearing; and c) cementation (Gai, 2018) 2.2.1 Numerical modeling of GHBS

Figure 2. Deviatoric stress (solid line) and volumetric strain (dashed line) against axial strain for sand specimens with (𝑎) different hydrate saturations and (𝑏) different effectives stresses (redrawn from Hyodo et al., (2013)) (Priest & Hayley, 2019)

The existing literature on the strength of soils containing hydrates commonly uses the MC framework, with a focus on including cohesion (Eq. 2) that is depend on hydrate saturation 𝑆ℎ (Klar et al., 2010; Miyazaki et al., 2010; Pinkert & Grozic, 2014; Rutqvist & Moridis, 2007). Another perspective (Pinkert, 2017a; 2017b; 2019) considered Rowe’s stress-dilatancy theory (Rowe, 1962) using Eqs. 35 to explain the enhancement in strength that arises in GHBS, suggesting that the impact of hydrate in the pore space is kinematic in nature (function of friction angle of the GHBS) and does not include any cohesion component. This model was supported by experimental data from triaxial tests conducted on water-saturated hydrate-bearing soils (Hyodo et al, 2013, etc). Their conclusion was based on the observation that when using Eqs. 6 and 8 (the modified versions of Rowe’s theory that included cohesion) the modeled results deviated from measured stress-strain behavior observed in the lab tests. However, it is known that in cemented soils cohesion is mobilized at the

beginning of shearing and lost as shear strain increases. Recent studies on water-saturated GHBS (Priest et al., 2021) also observed such behavior. In addition, the stressdilatancy plots presented by Pinkert exhibited significant deviations from a linear line at the early stages of tests on GHBS. It is evident that Rowe’s stress-dilatancy model does not accurately reflect observed behavior, and more effective modifications to Eqs. 3-5 that account for void ratio and variations in cohesion should be considered to better describe the dilatancy behavior of GHBS.

𝑒𝑐𝑟0 , ℎ𝑐𝑟 and 𝑛𝑐𝑟 are 0.88, 75.86 MPa, and 0.3216, respectively, that are found from experimental data.

a

3. MODEL DEVELOPMENT 3.1 Application of Rowe’s theory to GHBS As highlighted in Figure 2 the mechanical properties of a sand are greatly affected by 𝑆ℎ and 𝜎 ′ . In order to investigate the application of Rowe’s theory, the stressstrain responses of a sand with different 𝑆ℎ and confining stresses were selected from laboratory tests conducted at the University of Calgary (Abbas, 2018). Table 1 provides a summary of the relevant details for each specimen, including the initial void ratio, confining stress, and 𝑆ℎ .

b

Table 1. Specimens’ information Specimen

Hydrate saturation (Sh%)

WHS1 WHS2 WHS3

39.6 37.6 13.48

Confining stress (MPa) 0.5 1 0.5

Void ratio (e) 0.54 0.55 0.55

Figure 4. Investigation of the Effect of 𝑆ℎ on the stressstrain-dilatancy behavior of GHBS

Figure 4a highlights the stress-strain response for the three GHBS specimens highlighted in Table 1. It can be observed that increasing 𝑆ℎ and confining stress results in increased peak strength at lower axial strains. The stress dilatancy plots (Figure 4b), derived from using the data presented in Figure 4a, clearly deviate from the theoretical line given by Rowe's theory (yellow line) assuming a constant 𝜑𝑐𝑣 of 31° . The deviation from theory could be related to cohesion being present or the reduction in apparent void ratio of the GHBS specimen. Thus, the consideration of other factors such as void ratio, cohesion, and other relevant parameters is required to better map the theory to lab results. 3.1.1 Wan and Guo (1998) modification To investigate whether the change in void ratio of the specimen due to inclusion of hydrate is a factor, Wan & Guo (1998) void ratio modification (named W&G herein) was considered. In using the W&G model (Eq. 7), the critical state line (CSL) of the host sand is required. Figure 5 shows the stress path followed for sand without hydrate at various confining stress during triaxial shearing (Abbas, 2018; Hyodo et al. 2013), from which a CSL was developed (red line). The values of CSL parameters are as follows:

Figure 5. Changes in void ratio and mean effective stress during shear tests for different sands when tested without hydrate. From this, a critical state line for sand can be derived (red curve) Applying Eq. 7 to the GHBS data, with the appropriate 𝑒𝑐𝑟 value, an appropriate constant alpha value (α = −0.1016), and using an apparent void ratio of GHBS

(eGHBS =

esand(1−𝑆ℎ ) 1+esand×𝑆ℎ

), Figure 6 is obtained. Although the

W&G modification appears to improve the mapping of theory to the lab results, the discrepancy still remains noteworthy. This implies that assuming the influence of hydrate in the pore space as purely kinematic in nature is somewhat invalid. The deviation of the W&G-modified theoretical line from the real data may suggest that cohesion does influence the behavior of GHBS.

Figure 6. Stress-dilatancy plots taking into account the W&G (1998) modification of void ratio

WHS1 and WHS2 for both scenarios are highlighted in Figures 7 and 8, respectively. Table 2 shows the variables for μ and 𝜅 using these different approaches, with the resulting fits shown in Figure 9 and Figure 10. It can be seen that mapping WHS1 to WHS2, or vice versa, results in reasonably good fit, suggesting that cohesion is not influenced by effective stress. The minor discrepancies that can be seen mainly arise due to the different cohesion values mobilized for WHS1 and WHS2 (Figures 7-8).

Figure 7. Cohesion variation and the related fitted curves for two scenarios – PrePeak (WHS1)

3.2 Effect of cohesion on stress-dilatancy behaviour of GHBS behaviour To investigate the effect of cohesion on GHBS behavior, it is necessary to determine a cohesion function that better reflects the changes in cohesion that may occur during a shear test. Two scenarios were considered. In the first scenario, Eq. 8- Zhang and Salgado’s (2010) stressdilatancy theory (named Z&S herein)- was used to calculate the changes in cohesion manifest during a shear test. The resultant cohesion values were then fitted with a function of the form shown in Eq. 9, where the unknown variables μ and 𝜅 are obtained using the trial-and-error method to achieve a suitable fit. This function was consequently used to derive a theoretical curve for WHS2 using Eq. 8. This method was also adopted to fit WHS2 data to WHS1. c(𝜀𝑎 ):

[9]

For 𝜀𝑎 < 𝜀𝑎(cmax) : μTanh(−εa + 2εcmax ) × (slopeorigin to cmax ) × εa

Figure 8. Cohesion variation and the related fitted curves for two scenarios – PrePeak (WHS2) Table 2 Information of the model parameters (𝜇 and 𝜅)

For 𝜀𝑎(cmax) ≤ 𝜀𝑎 ≤ 𝜀stress peak : κTanh(εa − εcmax ) × (slope cmax to cstress peak ) × εa + cmax In the second scenario, the W&G modification was initially applied to WHS1 before subsequently deriving a cohesion function that was then applied to WHS2. This method was also adopted to fit WHS2 data to WHS1. The variation in cohesion and fitted cohesion functions for

Specimen

Parameters

WHS1

μ

WHS2

μ

𝜅 𝜅

First scenario (Z&S without modifications) 2 1.1 2.93 1.6

Second scenario (Z&S with W&G modification and 𝑒ℎ𝑟 ) 2 1.1 2.93 1.6

Figure 9. Stress-dilatancy response of WHS2 using WHS1 cohesion function

Figure 11. Stress-dilatancy response of WHS3 using WHS1 cohesion function. Figure 12 shows the application of this modification to cohesion in mapping the stress-dilatancy to the test results. The better alignment of these curves, compared to those in Figure 11, is apparent suggesting that this modification to 𝑐 is appropriate. This approach was also implemented based on utilizing the WHS2 cohesion function (Figure 13) and subsequently applying the same modification using Eq. 10 where 𝑆ℎ for WHS2 is substituted for WHS1, with Figure 14 showing the successful alignment between the fitted curves and the real data.

Figure 10. Stress-dilatancy response of WHS1 using WHS2 cohesion function Figure 11 shows the stress-dilatancy plots for WHS3 estimated based on applying the WHS1 cohesion function for both scenario 1 and 2. Given that 𝑆ℎ of WHS1 and WHS2 is approximately 2.93 times that of WHS3, the discrepancies observed in the fitted stress-dilatancy responses shown in Figure 11 suggest that cohesion is a function of 𝑆ℎ . To better align the stress-strain responses for WHS3, a modification was considered to scale the cohesion function based on the ratio of 𝑆ℎ between the tests. The proposed modification is: 𝑆ℎ(𝑊𝐻𝑆3)

𝑐𝑊𝐻𝑆3,𝑀𝑜𝑑𝑖𝑓𝑖𝑒𝑑 = 𝑐(𝜀𝑎 )𝑊𝐻𝑆1 × (

𝑆ℎ(𝑊𝐻𝑆1)

𝑝

)

Figure 12. Evaluation of the validity of the modifications to map the fitted curves onto the real data (WHS3 data) based on WHS1 cohesion function

[10]

where 𝑐(𝜀𝑎 )𝑊𝐻𝑆1 is WHS1 cohesion function and 𝑝 is a variable to achieve an appropriate fit, which is determined through a trial-and-error method. The value of 𝑝 is 1.5 for the first scenario and 3 for the second scenario.

4. CONCLUSION The results of laboratory tests conducted on watersaturated GHBS at the University of Calgary (Abbas, 2018) were used to examine the influence of hydrate on the stress-strain behavior of a sand. Utilizing Rowe’s stress-

Figure 13. Stress-dilatancy response of WHS3 using WHS2 cohesion function

Figure 14. Evaluation of the validity of the modifications to map the fitted curves onto the real data (WHS3 data) based on WHS2 cohesion function. dilatancy theory and assuming zero cohesion (𝑐 = 0), it was shown that the fitted model did not map to the stressdilatancy response of water-saturated GHBS. A subsequent stress-dilatancy model was developed considering factors such as void ratio and cohesion in the stress-dilatancy relationship for GHBS. By accounting for changes in void ratio resulting from the inclusion for hydrate in the pore space and applying Wan and Guo (1998) void ratio modification factor, provided a better approximation to the stress-dilatancy response. However, this approach still failed to accurately capture the observed stress-dilatancy behavior, suggesting that hydrate may also be active as a cohesive element during shearing. To assess whether hydrate contributes a cohesive component to the shear strength, cohesion functions were determined for a given test (e.g., WHS1) and then used to model the response of WHS2 (which had similar 𝑆ℎ but different confining stresses). The results provided a suitable fit when the WHS1 cohesion function was applied to estimate the stress-dilatancy behavior of WHS2, and

vice versa, showing that cohesion was a factor in the strength of GHBS and not solely governed by kinematics, with cohesion being strongly related to 𝑆ℎ and not confining stress. The comprehensiveness of the proposed model was evaluated by applying the cohesion functions derived from samples with higher 𝑆ℎ (WHS1 and WHS2) to estimate the stress-dilatancy behavior of WHS3 with lower 𝑆ℎ . To account for the difference in 𝑆ℎ between WHS1/WHS2 and WHS3, a modification (to account for changes in 𝑆ℎ ) was employed to align the fitted curves with the real data, with modeling results confirming the effectiveness of the model and the corresponding modification. However, due to the limited dataset considered, in terms of 𝑆ℎ and confining stresses, further studies are required to determine if the developed model is appropriate for differing test conditions. 6. REFERENCES Abbas, M. (2018). Geomechanical Characteristics of Hydrate-bearing Sands University of Calgary]. Chaouachi, M., Falenty, A., Sell, K., Enzmann, F., Kersten, M., Haberthur, D., & Kuhs, W. F. (2015). Microstructural evolution of gas hydrates in sedimentary matrices observed with synchrotron X-ray computed tomographic microscopy. Geochemistry Geophysics Geosystems, 16(6), 1711-1722. Chu, J., & Wanatowski, D. (2009). Effect of Loading Mode on Strain Softening and Instability Behavior of Sand in Plane-Strain Tests. Journal of Geotechnical and Geoenvironmental Engineering, 135(1), 108-120. Collett, T. S. (2002). Energy resource potential of natural gas hydrates. Aapg Bulletin, 86(11), 1971-1992. Coulomb, C. A. (1776). Essai sur une Application des Rggles de Maximis et Minimis a Quelques ProblSmes de Statique RSatife a 1’Architecture. MSmoires de l’Academie Royale des Sciences 7, 343-382. Gai, X. (2018). Geomechanical modeling of gas hydrate bearing sediments and other complex soils Texas A&M University]. Ghiassian, H., & Grozic, J. L. H. (2013). Strength behavior of methane hydrate bearing sand in undrained triaxial testing. Marine and Petroleum Geology, 43, 310-319. Helgerud, M. B., Dvorkin, J., Nur, A., Sakai, A., & Collett, T. (1999). Elastic-wave velocity in marine sediments with gas hydrates: Effective medium modeling. Geophysical Research Letters, 26(13), 2021-2024. Hyodo, M., Li, Y. H., Yoneda, J., Nakata, Y., Yoshimoto, N., Nishimura, A., & Song, Y. C. (2013). Mechanical behavior of gas-saturated methane hydrate-bearing sediments. Journal of Geophysical Research-Solid Earth, 118(10), 5185-5194. Jiang, M. J., Chen, H., Tapias, M., Arroyo, M., & Fang, R. (2014). Study of mechanical behavior and strain localization of methane hydrate bearing sediments with different saturations by a new DEM model. Computers and Geotechnics, 57, 122-138. Klar, A., Soga, K., & Ng, M. Y. A. (2010). Coupled deformation-flow analysis for methane hydrate extraction. Geotechnique, 60(10), 765-776.

Li, Y. H., Liu, W. G., Zhu, Y. M., Chen, Y. F., Song, Y. C., & Li, Q. P. (2016). Mechanical behaviors of permafrostassociated methane hydrate-bearing sediments under different mining methods. Applied Energy, 162, 16271632. Miyazaki, K., Masui, A., Sakamoto, Y., Aoki, K., Tenma, N., & Yamaguchi, T. (2011). Triaxial compressive properties of artificial methane-hydrate-bearing sediment. Journal of Geophysical Research-Solid Earth, 116, Article B06102. Miyazaki, K., Masui, A., Tenma, N., Ogata, Y., Aoki, K., Yamaguchi, T., & Sakamoto, Y. (2010). Study on Mechanical Behavior for Methane Hydrate Sediment Based on Constant Strain-Rate Test and UnloadingReloading Test Under Triaxial Compression. International Journal of Offshore and Polar Engineering, 20(1), 61-67. Pinkert, S. (2017a). Rowe's Stress-Dilatancy Theory for Hydrate-Bearing Sand. International Journal of Geomechanics, 17(1), Article 06016008. Pinkert, S. (2017b). The lack of true cohesion in hydratebearing sands. Granular Matter, 19(3), Article 57. Pinkert, S. (2019). Dilation Behavior of Gas-Saturated Methane-Hydrate Bearing Sand. Energies, 12(15), Article 2937. Pinkert, S., & Grozic, J. L. H. (2014). Prediction of the mechanical response of hydrate-bearing sands. Journal of Geophysical Research-Solid Earth, 119(6), 4695-4707. Priest, J. A., Best, A. I., & Clayton, C. R. I. (2005). A laboratory investigation into the seismic velocities of methane gas hydrate-bearing sand. Journal of Geophysical Research-Solid Earth, 110(B4), Article B04102. Priest, J. A., & Hayley, J. L. (2019). Strength of Laboratory Synthesized Hydrate-Bearing Sands and Their Relationship to Natural Hydrate-Bearing Sediments. Journal of Geophysical Research-Solid Earth, 124(12), 12556-12575. Priest, J. A., Rees, E. V. L., & Clayton, C. R. I. (2009). Influence of gas hydrate morphology on the seismic velocities of sands. Journal of Geophysical ResearchSolid Earth, 114, Article B11205. Rowe, P. W. (1962). The Stress-Dilatancy Relation for Static Equilibrium of an Assembly of Particles in Contact Proc. of the Royal Society of London, Rutqvist, J., & Moridis, G. J. (2007). Numerical studies on the geomechanical stability of hydrate-bearing sediments Offshore Technology Conference, Song, Y. C., Zhu, Y. M., Liu, W. G., Li, Y. H., Lu, Y., & Shen, Z. T. (2016). The effects of methane hydrate dissociation at different temperatures on the stability of porous sediments. Journal of Petroleum Science and Engineering, 147, 77-86. Wan, R. G., & Guo, P. J. (1998). A simple constitutive model for granular soils: Modified stress-dilatancy approach. Computers and Geotechnics, 22(2), 109133. Winters, W. J., Pecher, I. A., Waite, W. F., & Mason, D. H. (2004). Physical properties and rock physics models of sediment containing natural and laboratory-formed

methane gas hydrate. American Mineralogist, 89(8-9), 1221-1227. Yun, T. S., Francisca, F. M., Santamarina, J. C., & Ruppel, C. (2005). Compressional and shear wave velocities in uncemented sediment containing gas hydrate. Geophysical Research Letters, 32(10), Article L10609. Zhang, J., & Salgado, R. (2010). Stress-dilatancy relation for Mohr-Coulomb soils following a non-associated flow rule. Geotechnique, 60(3), 223-226.

Evaluation of the Relative Accuracies of Two- and Four-Parameter Models for Predicting Maximum and Minimum Void Ratios for Sand-Silt Mixtures Carmine P. Polito Valparaiso University, Valparaiso, Indiana, USA

ABSTRACT The maximum and minimum index void ratios of sand-silt mixtures can be predicted using the filling coefficient, a, and embedment coefficient, b with an appropriate model. There are three implementations of these models: The four-parameter model, the emintwo-parameter model and the emax-two parameter model. The four-parameter model uses two values of the filling coefficient, a, and two values of the embedment coefficient, b. Conversely, the twoparameter models use one pairing of the filling coefficient, a, and the embedment coefficient, b. A study was performed to evaluate the relative accuracies of two-parameter models and fourparameter model in predicting the maximum and minimum index void ratios of sand-silt mixtures. It was found that the emax-two parameter model was the least accurate model. Both the emin-twoparameter model and the four-parameter model produced very accurate estimates. Additionally, recommendations are provided for choosing which model to use based on several possible scenarios. RÉSUMÉ Les taux de vide d'indice maximum et minimum des mélanges sable-limon peuvent être prédits en utilisant le coefficient de remplissage, a, et le coefficient d'enfouissement, b avec un modèle approprié. Il existe trois implémentations de ces modèles : le modèle à quatre paramètres, le modèle emin à deux paramètres et le modèle emax à deux paramètres. Le modèle à quatre paramètres utilise deux valeurs du coefficient de remplissage, a, et deux valeurs du coefficient d'encastrement e, b. À l'inverse, les modèles à deux paramètres utilisent un couple du coefficient de remplissage, a, et du coefficient d'enrobage, b. Une étude a été réalisée pour évaluer les précisions relatives des modèles à deux paramètres et du modèle à quatre paramètres dans la prédiction des taux de vide d'indice maximum et minimum des mélanges sable-limon. Il a été constaté que le modèle à deux paramètres emax était le modèle le moins précis. Le modèle emin à deux paramètres et

le modèle à quatre paramètres ont produit des estimations très précises. De plus, des recommandations sont fournies pour choisir le modèle à utiliser en fonction de plusieurs scénarios possibles. 1

INTRODUCTION

For predicting the maximum and minimum index void ratios sand-silt mixtures, one of the more commonly used models was developed by Chang, Wang and Ge (2016). They developed a series of equations for predicting the maximum and minimum index void ratios (emax and emin) for mixtures of sand and silt based on binary packing. Implementing their methodology requires several common soil parameters and either two or four empirical variables: the filling coefficient, a, and the embedment coefficient, b. The number of empirical constants required is dependent on whether the predictions are based on the filling coefficients and embedment coefficients from both the maximum and minimum index void ratio data (the four-parameter model) or solely upon the filling coefficients and embedment coefficients from either the maximum index void ratio data (the emax-two parameter model) or the minimum index void ratio data (the emin-two parameter model). Using Chang, et al.’s models, if one has a collection of maximum and minimum index void ratios at various silt contents for the soil in question, one can determine the filling and embedment coefficients and then predict the maximum and minimum index void ratios at any other silt content using the four-parameter model. It is also possible to determine a and b based solely on minimum density/maximum void ratio tests using one of the two-parameter models. If one does not possess such data, either the data must be obtained through laboratory testing or correlations for predicting the filling and embedment coefficients must be used (Polito, 2021). If the intent of the user is to predict the index void ratios for a mixture of sand and silt with a specific silt content, the need to run multiple index density tests at various silt contents negates the main advantage of using the models to predict the

index void ratios (i.e. reducing the number of laboratory tests performed). For this reason, the author developed a series of equations for predicting the filling and embedment coefficients. These equations are based on the median grain sizes of the sand and the silt, which are easily obtained from a grain-size analysis. The goal of the study was to compare the relative accuracy of the three models in predicting emax and emin for mixtures of sand and silt using parameters obtained through laboratory testing.

entirely contained in the voids between the sand grains, to a silt matrix that contains isolated sand grains. Below the TFC, the soil behaves essentially as a sand; above the TFC the soil behaves essentially as a silt (Polito and Sibley, 2020). For soils with silt contents below the threshold fines content, thus having a coarse-grain dominated soil matrix: 𝑚𝑎𝑥 𝑒𝑀1 = 𝑒1𝑚𝑎𝑥 𝑦1 + 𝑒2𝑚𝑎𝑥 𝑦2 − 𝑎𝑚𝑎𝑥 (1 + 𝑒2𝑚𝑎𝑥 )𝑦2

[1]

2

𝑚𝑖𝑛 𝑒𝑀1 = 𝑒1𝑚𝑖𝑛 𝑦1 + 𝑒2𝑚𝑖𝑛 𝑦2 − 𝑎𝑚𝑖𝑛 (1 + 𝑒2𝑚𝑖𝑛 )𝑦2

[2]

BACKGROUND

As increasing amounts of silt are added to a clean sand, the classification of the soil changes from sand to silty sand to sandy silt and finally to silt. These changes in soil composition lead to changes in both the maximum and minimum index void ratios (emax and emin). Chang, et al. (2016) presented four equations for predicting the maximum and minimum index void ratios for mixtures of a sand and a silt over the range of possible silt contents, where the silt content is defined as being the ratio of the mass of silt in a specimen to the combined mass of sand and silt in the specimen. In the equations, silt content can range from 0 (representing a pure sand) to unity (representing a pure silt). The four-parameter model uses one pairing of the filling coefficient, amax, and the embedment coefficient, bmax, for predicting maximum index void ratios, emax, and a second pairing of filling coefficient, amin, and the embedment coefficient, bmin, for predicting minimum index void ratios, emin. Conversely, two-parameter models use one pairing of the filling coefficient, and the embedment coefficient, for predicting both the maximum index void ratio and the minimum index void ratio. Although Chang et al. only used data from maximum void ratio tests for their two-parameter model, this study also examined a two-parameter model based on data from minimum void ratio tests. Henceforth, the two-parameter model based on data from maximum void ratio tests (i.e. Chang et al.’s model) shall be referred to as the emax-two-parameter model and the two-parameter model based on data from minimum void ratio tests shall be referred to as the emin-two-parameter model. The nature and number of soil parameters required to implement the three models are dependent upon the model chosen. The soil parameters and empirical constants required for each model are outlined in Table 1. Equations for the four-parameter model are provided as Equations 1 through 4. The emax-twoparameter model uses equations 1 and 3 and the emin-two-parameter model uses Equations 2 and 4. The threshold fines content (TFC) represents the silt content at which the soil begins to transition from a sand matrix, in which the silt particles are

For soils with silt contents above the threshold fines content, thus having a fine-grain dominated soil matrix: 𝑚𝑎𝑥 𝑒𝑀2 = 𝑒1𝑚𝑎𝑥 𝑦1 + 𝑒2𝑚𝑎𝑥 𝑦2 − 𝑏 𝑚𝑎𝑥 𝑒1𝑚𝑎𝑥 𝑦1

[3]

𝑚𝑖𝑛 𝑒𝑀2 = 𝑒1𝑚𝑖𝑛 𝑦1 + 𝑒2𝑚𝑖𝑛 𝑦2 − 𝑏 𝑚𝑖𝑛 𝑒1𝑚𝑖𝑛 𝑦1

[4]

Table 1: Soil Parameters and Empirical Constants Required for Each Model

3

MODEL EVALUATION

In order to evaluate the relative accuracies of the three models, the models were used to predict values of emax and emin using best-fit values of the filling and embedment coefficients determined through computer analyses. The predicted values were then compared to the corresponding values determined through laboratory testing and the goodness of fit determined. The coefficients of determination were calculated for emax and emin for each of the 58 soil combinations for each of the three models.

Polito (2021) provides details of how the best-fit filling and embedment coefficients were developed for each combination of silt and sand and how a coefficient of determination, R2, was then developed to evaluate the ability of the best-fit coefficients to predict the maximum and minimum index void ratios for each sand-silt combination. The results of the analyses performed to evaluate the relative accuracy of the four-parameter model and each of the two-parameter models are discussed below.

parameter model against the values of e max measured in the laboratory. Figure 6 plots the values of emin predicted using the emax-two-parameter model against the values of emin measured in the laboratory. Figure 7 presents a histogram of the relative frequencies of the R2 values calculated using the maximum index void ratios for the 58 sand-silt combinations based on the best-fit filling and

3.1 The Four-Parameter Model The results of the analyses performed using the fourparameter model can be seen in Figures 1 and 2. Figure 1 plots the values of emax predicted using the four-parameter models against the values of emax measured in the laboratory. Figure 2 plots the values of emin predicted using the four-parameter model against the values of emin measured in the laboratory. Figure 3 presents a histogram of the relative frequencies of the R2 values calculated using the maximum index void ratios for the 58 sand-silt combinations based on the best-fit filling and embedment coefficients and the four-parameter model. Figure 4 presents a histogram of the relative frequencies of the R2 values calculated using the minimum index void ratios for the 58 sand-silt combinations based on the best-fit filling and embedment coefficients and the four-parameter model.

Figure 2: Measured versus predicted minimum index void ratios developed using the four-parameter model

Figure 3: Relative frequency of R2 values for maximum index void ratios predicted using the fourparameter model

Figure 1: Measured versus predicted maximum index void ratios developed using the four-parameter model 3.2 The emax-Two-Parameter Model The emax-two-parameter model uses the best-fit filling and embedment coefficients (amax and bmax, respectively) determined from the results of maximum index void ratio tests. The results of the analyses performed using the emax-two-parameter model can be seen in Figures 5 and 6. Figure 5 plots the values of emax predicted using the emax-two-

Figure 4: Relative frequency of R2 values for minimum index void ratios predicted using the fourparameter model embedment coefficients and the emax-two-parameter model.

Figure 8 presents a histogram of the relative frequencies of the R2 values on the best-fit filling and the minimum index void ratios for the 58 sand-silt based on the best-fit filling and embedment coefficients and the emax-two-parameter model.

Figure 8: Relative frequency of R2 values for minimum index void ratios predicted using the emaxtwo-parameter model 3.3 The emin-Two-Parameter Model Figure 5: Measured versus predicted maximum index void ratios developed using the emax-twoparameter model

Figure 6: Measured versus predicted minimum index void ratios developed using the emax-two-parameter model

Figure 7: Relative frequency of R2 values for maximum index void ratios predicted using the emaxtwo-parameter model

The emin-two-parameter model uses the best-fit filling and embedment coefficients (amin and bmin, respectively) determined from the results of minimum index void ratio tests. The results of the analyses performed using the emin-two-parameter model can be seen in Figures 9 and 10. Figure 9 plots the values of emin predicted using the emin-twoparameter model against the values of e max measured in the laboratory. Figure 10 plots the values of emin predicted using emin-two-parameter model against the values of emin measured in the laboratory. Figure 11 presents a histogram of the relative frequencies of the R2 values calculated using the maximum index void ratios for the 58 sand-silt combinations based on the best-fit filling and embedment coefficients and the emin-two-parameter model. Figure 12 presents a histogram of the relative frequencies of the R2 values calculated using the minimum index void ratios for the 58 sand-silt based on the best-fit filling and embedment coefficients and the emin-two-parameter model.

Figure 9: Measured versus predicted maximum index void ratios developed using the e min-twoparameter model

Figure 10: Measured versus predicted minimum index void ratios developed using the e min-twoparameter model

Figure 11: Relative frequency of R2 values for maximum index void ratios predicted using the e maxtwo-parameter model

Figure 12: Relative frequency of R2 values for minimum index void ratios predicted using the e mintwo-parameter model A histogram of the coefficients of determination for the maximum index void ratios predicted using each of the three models is presented in Figure 13.

Figure 13: Comparison of coefficients of determination determined for emax using the two- and four-parameter models Similarly, Figure 14 presents a histogram of the coefficients of determination for the minimum index void ratios predicted using each of the three models. Table 2 summarizes the results of the comparison of the three models as well as providing the percentage of R2 values greater than 0.95 and greater than 0.80 produced by each model. All three models predicted the maximum index void ratios with mean R2 values above 0.96. The four-parameter model and the emin-two parameter model both predicted the maximum index void ratios with mean R2 values above 0.94. The emax-two parameter model was less accurate in predicting the minimum index void ratios producing a mean R2 value of 0.88.

Figure 14: Comparison of coefficients of determination determined for emin using the two- and four-parameter models From the results presented in Table 2, it can be seen that, while the emax-two-parameter model produces reasonably accurate predictions, the predictions made with the four-parameter model are more accurate. Both the mean and the median coefficients of determination were approximately 4% higher for the four-parameter model and the

standard deviation of the four-parameter model was about one-third the standard deviation of the emaxtwo-parameter model. Most significantly, the fourparameter model returned results that very closely matched (here defined here as an average R 2 value greater than 0.95) the void ratios measured in the lab 97% of the time, while the emax-two-parameter model only returned such results 87% of the time. The fourparameter models returned R2 values that closely matched (here defined here as an average R 2 value greater than 0.80) the void ratios measured in the lab 100% of the time, while the emax-two-parameter model only returned such results 94% of the time. Similarly, in Table 2 it can be seen that the accuracy of the predictions made using the e min-twoparameter model very closely matched the accuracy of the predictions made with the four-parameter model. Both the mean and the median coefficients of determination are only 1% higher for the fourparameter model and the standard deviation of the two models is comparable. Table 2: Comparison of the average coefficients of determination produced using the two-parameter and four-parameter models

1.

2. 3. 4.

5

For determining the filling and embedment coefficients, maximum and minimum index void ratio data for at least three silt contents in addition to the data for the pure sand and pure silt should be used. If data from both maximum and minimum index data are available, the-four parameter model should be used. If only data from minimum index data are available, the emin-two-parameter model should be used. If only data from maximum index data are available, the emax-two-parameter model can be used with caution. The filling and embedment coefficients should be confirmed using correlations such as those previously developed by the author (Polito, 2021).

CONCLUSIONS

A study was performed to evaluate the relative accuracies of the four-parameter model, the emaxtwo-parameter model and the emin-two-parameter model in predicting the maximum and minimum index void ratios for mixtures of sand and silt with different percentages of silt. In the comparison of the models, the fourparameter model was found to produce the most accurate values for both the maximum and minimum index void ratios. The emin-two-parameter model was also found to produce accurate values for both the maximum and minimum index void ratios. The emaxtwo-parameter model was also found to produce accurate values for maximum index void ratios but was found to be less accurate for minimum index void ratios. 6

ACKNOWLEDGEMENTS

The author would like to thank Valparaiso University for the financial support provided through the Alfred W. Sieving Chair of Engineering. The main difference in accuracy between the two models is that the four-parameter model returned results that very closely matched (here defined here as an average R2 value greater than 0.95) the void ratios measured in the lab 97% of the time, while the emin-two-parameter model only returned such results 87% of the time. Both models returned R2 values larger than 0.80 for all 58 sandsilt combinations. 4

MODEL SELECTION

Based on the results of this study, the author recommends the following guidelines:

7

References

Chang, C., Wang, L., and Ge, L. 2016, Maximum and minimum void ratios for sand-silt mixtures, Engineering Geology, 211:7-18. Polito, C. 2021, Regression Models for Estimating Parameters a And b For Chang, Wang and Ge’s Maximum And Minimum Void Ratio Models, GeoNiagra 2021, Niagara Falls, Ontario, Canada, Paper 288. Polito, C. and Sibley, E. 2020, Threshold fines content and behavior of sands with nonplastic silts, Canadian Geotechnical Journal, 57: 3.

Study of fresh properties of cemented tailings material with ternary cement blends Aparna Sagade, Dr. Mamadou Fall Department of Civil Engineering University of Ottawa, Ottawa, ON, Canada ABSTRACT For the sustainable advancement of the mining sector, alternative binders, such as ground granulated blast furnace slag (Slag) and fly ash, have been adopted to partially replace ordinary Portland cement (OPC) in the preparation of cemented paste backfill (CPB). However, the supply of these materials is limited and may not be sufficient for the industry's future demands. Limestone (LS), an inexpensive and abundant resource, has the potential to complement these binders when used as a ternary mix. This paper presents experimental results on the effects of ternary blended cement with slag and limestone powder on key engineering fresh properties of CPB: (i) rheological properties and (ii) setting time. CPB samples with PCI/Slag ratios of 50/50 and 80/20 were considered, and the effect of LS in the ternary mix was investigated by replacing the slag with increasing doses of LS (5, 10, and 20 wt. %). The findings have shown that the replacement of slag by a higher dosage of LS in the ternary binder reduces the yield stress and increases the viscosity of CPB. The effect of LS on the rheological properties of CPB has mainly been attributed to the physical (e.g., filler effect, lubrication, particle size) and chemical (nucleation and changes in repulsive forces between CPB particles) effects of LS. Further, an increase in the proportion of limestone in the CPB ternary binder system prolonged the setting time (initial and final) of the CPB mixture due to the nucleation and dilution effects of LS. Overall, the optimal usage of LS and slag in the ternary system can serve as a sustainable alternative to the widely used OPC or OPC/Slag binary binder, thereby decreasing the energy consumption and carbon footprint associated with cement and CPB technology. Keywords: cemented paste backfill; tailings; rheology; setting time; ternary binder; sustainable mining RÉSUMÉ Pour l'avancement durable du secteur minier, des liants alternatifs, tels que le laitier granulé de haut fourneau (Slag) et les cendres volantes, ont été adoptés pour remplacer partiellement le ciment Portland ordinaire (OPC) dans la préparation du remblai en pâte cimentée (CPB). Cependant, l'offre de ces matériaux est limitée et pourrait ne pas être suffisante pour les demandes futures de l'industrie. Le calcaire (LS), ressource peu coûteuse et abondante, a le potentiel de compléter ces liants lorsqu'il est utilisé comme mélange ternaire. Cet article présente des résultats expérimentaux sur les effets du ciment mélangé ternaire avec du laitier et de la poudre de calcaire sur les principales propriétés techniques fraîches du CPB : (i) propriétés rhéologiques et (ii) temps de prise. Des échantillons de CPB avec des rapports PCI/Slag de 50/50 et 80/20 ont été considérés, et l'effet de LS dans le mélange ternaire a été étudié en remplaçant le laitier par des doses croissantes de LS (5, 10 et 20 % en poids). Les résultats ont montré que le remplacement du laitier par une dose plus élevée de LS dans le liant ternaire réduit le seuil d'écoulement (ou de cisaillement) et augmente la viscosité du CPB. L'effet du LS sur les propriétés rhéologiques du CPB a été principalement attribué aux effets physiques (par exemple, effet de remplissage, lubrification, taille des particules) et chimiques (nucléation et modifications des forces répulsives entre les particules de CPB) du LS. De plus, une augmentation de la proportion de calcaire dans le système de liant ternaire CPB a prolongé le temps de prise (initial et final) du mélange CPB en raison des effets de nucléation et de dilution du LS. Dans l'ensemble, l'utilisation optimale du LS et du laitier dans le système ternaire peut servir d'alternative durable au liant binaire OPC ou OPC/Slag largement utilisé, réduisant ainsi la consommation d'énergie et l'empreinte carbone associées à la technologie du ciment et du CPB. 1

INTRODUCTION

The mining sector contributes considerably to the economy of numerous nations worldwide. Sustainable management of huge amounts of mining waste (e.g., tailings) is one of the most critical issues and challenges faced by the mining sector (Liu & Fall, 2022; Jiang et al., 2020) Traditional practices such as surface waste disposal often cause geotechnical and geo-environmental hazards, such as acid

mine/rock drainage and tailings dam failures (Ercikdi et al., 2009; Wu et al., 2013). Therefore, innovative mine waste management technologies, such as cemented paste backfill technology, have been developed over the recent decades to reduce and tackle the above-mentioned problems related to the surface disposal of mine waste (tailings). Cement paste backfill (CPB) has proven to be effective for mine waste management and ground support. Considering

its economic, environmental, and social benefits, CPB is widely used in the modern mining industry of several nations, such as Australia, Canada, China, Germany, and South Africa (Fall & Benzaazoua, 2005; Wu et al., 2013). CPB is a composite mixture of tailings (70-85% of total solids), water (processed or fresh), and a relatively low amount of binder (typically 2-8% by weight of total solids) (Fall & Benzaazoua, 2005; Haruna & Fall, 2020). Important performance and technical properties of fresh CPB include flowability and setting time. The fresh CPB mixture is typically prepared on the ground surface in a backfill plant and then transported by means of gravity and/or pumping through the pipeline to the excavated cavities underground (Liu & Fall, 2022). The successful transport of CPB, which is a function of its fluidity, is key to the successful application of CPB technology in any mine, as poor fluidity can lead to pipe clogging, delayed transport, disruption of mining operations, and ultimately financial losses (Simon & Grabinsky, 2013). The challenge of practical and efficient transportation of CPB is growing as many mines are located at greater and greater depths, often resulting in longer transport distances, which requires fresh CPB to be prepared with excellent flowability. The fluidity of CPB is mainly related to its yield stress and viscosity (Ali et al., 2021; Liu & Fall, 2022). Indeed, with the technological advances and the progressive exhaustion of ore at shallow depths in numerous world regions, mining activities are going deeper, with a maximum depth of up to 4350 m deep (Mponeng, South Africa) (Ziegler et al., 2015). The setting time of CPB provides crucial information on its early age strength development, in other words, its mechanical stability at early ages. A rapid gain in early age strength is important to reduce the risk of liquefaction of CPB to ensure or improve the safety of working conditions and increase mine productivity (Yin et al., 2012). Ordinary Portland cement (OPC) is the predominant binder used in the production of CPB; however, it accounts for 75-80% of the cost of CPB (Grice, 2001; Haiqiang et al., 2016). In addition, clinker production accounts for 5-8% of the world’s man-made CO2 emissions (Gartner & Hirao, 2015). The above factors have driven mining firms to look for cement substitutes that decrease the Portland cement content (and thus the cost of CPB) and reduce the mining industry's carbon footprint while keeping or enhancing the mechanical and rheological properties of CPB. Thus, in recent decades, the use of a binder consisting of Portland cement and supplementary cementitious material (SCM), such as blast furnace slag (slag), also known as binary cement, has been increasingly adopted in CPB technology and practice. These SCMs are often utilized in CPB to lower costs and improve material performance properties (Haiqiang et al., 2020). For example, replacing Portland cement with slag or fly ash in the binary mix format is an effective way to improve the long-term mechanical strength and microstructural properties of CPB (at older ages), consequently improving the mechanical stability and environmental performance of the CPB structure at advanced ages, thereby reducing the overall cost (Fall & Pokharel, 2010) and carbon footprint of CPB. However, due to the poor reaction kinetics of slag or fly ash, their use as a binary binder with cement in CPB results in low

compressive strength at young ages and a low rate of strength gain of CPB in comparison to those of CPB with cement only (Snellings et al., 2022). Additionally, the fly ash and slag supply may be limited and insufficient to meet future industry needs (Bentz et al., 2017; Snellings et al., 2022). Moreover, Xiao (2021) noted an increase in yield stress and viscosity with increasing slag percentage, which reduces the fluidity of CPB, ultimately negatively affecting CPB flowability and delivery to stopes (Wu et al., 2013). Consequently, in the past few years, the use of limestone powder (LS) as a partial replacement material for cement has received increasing interest. Limestone has the benefits of being abundant and cheap (Bentz et al., 2017; Wang et al., 2018a). Several previous investigations on cementitious materials have reported that limestone has effectively improved the rheological behavior and workability of cement paste, concrete, and mortar materials (Courard and Michel, 2014; Jiang et al., 2020; Vance et al., 2013a). This is mainly attributed to its filler and nucleating effect (Kumar et al., 2013; Vance et al., 2013b). Wang et al. (2018a) recommended the addition of LS aluminum rich SCMs into cementitious systems for overall performance improvement. These studies revealed that the addition of LS to Portland cement systems leads to an increase in the rate of hydration at early ages, thus causing high early strength, but it can diminish the subsequent strength because of the dilution effect when added at a higher replacement (Menéndez et al., 2003; Mounanga et al., 2011). From the preceding discussion, it can be deduced that the combined use of limestone powder and slag in a ternary blended cement (LS, slag, OPC) has the potential to formulate a CPB binder with adequate early age strength development and suitable rheological properties while decreasing the cost of CPB and the carbon footprint of the mining industry. However, the effect of ternary cement mix (Portland cement, slag, limestone) on fresh CPB properties is not well understood, and no studies on the development of rheological properties (yield stress, viscosity) and setting time (initial, final) of CPB with ternary cement mixes have been conducted. All previous studies on the impact of this ternary cement mixture on the fresh properties of the cementitious mix system such as concrete or mortar. However, concrete and mortar are different from CPB and thus cannot be directly applicable to CPB. The aims of this research are to experimentally investigate the effects of the ternary cement mixture (Portland cement, slag, limestone) on the fresh properties of CPB. These properties include rheological properties (viscosity and yield stress) and setting time. 2 2.1

EXPERIMENTAL PROGRAMS Materials

Tailings: Silica tailings (ST) with 99.8 wt% quartz are the main constituents of the CPB preparation for this experimental program. Their physical characteristics are presented in Table 1. Quartz is the most common mineral in tailings from Canadian hard rock mines; therefore, the use of ST is most appropriate to correlate the results of the experimental work with the actual Canadian mining

scenario. Moreover, its chemically inert nature reduces the interference from additional chemical reactions that may occur due to active minerals or impurities present in the natural tailings.

of the CPB mix is presented in Table 4. The fresh samples were prepared by dry mixing predetermined proportions of the materials in the mixer for 1 min, followed by wet mixing for a total of 7 minutes to ensure homogeneity of the mix.

Table 1. Physical characteristics of the tailings

2.2

Element Unit

-

Gs

D10 µm

D30 µm

D50 µm

ST

2.7

1.9

9.0

22.5

---

1.8

9.1

20.0

Avg. of tailings

9

D60 µm 31.5 30.8

Gs: spec. Gravity; Ss: spec. surface area(m2/g); ST: silica tailings; wt.: weight.

2.2.1

Test methods Rheological tests

The prepared samples were then used to perform viscosity and yield stress tests to investigate the rheology of fresh CPB using a 10 cm x 10 cm mold for the yield stress and viscosity tests. The specimens were covered with a plastic film throughout the test. In addition, to account for the continuous movement of the CPBs during transport and to ensure homogeneity, the samples were shaken for 1 minute before performing each yield stress or viscosity test. and then stabilized for 30 seconds. This process mimics the shearing of paste backfill during transportation and avoids the settlement of tailings particles (due to selfweight) during curing (Haiqiang et al., 2016). The yield stress and viscosity tests were conducted twice to ensure the accuracy of the results. Table 2. Mix composition of the CPB samples Sample nomenclature

Tailing

50P-50S-0L

Figure 1. Grain size distribution of the silica tailings ST, PCI, Slag, and LS. Table 1. Physical characteristics of the binders Element Unit GGBS

RD

Ss

2.8

LS PCI

Binder Content (%)

50P-45S-5L 50P-40S-10L 50P-30S-20L

ST

4.5

%PCI

%Sl ag

% LP

50

50

0

50

45

5

50

40

10

50

30

20

D50 µm 8.4

D90 µm 27.6

80P-20S-0L

80

20

0

2.10

D10 µm 0.77

80P-15S-5L

80

15

5

2.93

2.69

0.28

2.31

7.94

3.2

1.30

0.99

14.9

39.76

80P-10S-10L

80

10

10

80P-0S-20L

80

0

20

Binders and Mixing water In this experimental program, CPB specimens were made using PCI as the main binder with varying weight percentages of slag and limestone powder (CaCO3) in place of cement. Figure 2 displays the particle size distribution of ST and all binders. Two mix proportions using 4.35 wt.% binder and 7.35 water/binder ratio were chosen in this study, with PCI/Slag proportions of 50/50 and 80/20 by weight. In addition, slag was replaced with 5, 10, and 20% limestone in the ternary binder. The chemical compositions and physical characteristics of the binders are listed in Tables 2 and 3, respectively. To study the rheological properties of the sample, distilled water is used to avoid the impact of impurities from tap water. Specimen preparation Two groups of samples A and B, with 50 wt.% and 80 wt.% CPB, respectively, were prepared using a binder content of 4.35 wt.% and a water-to-binder ratio of 7.35. The design

W/B

7.35

Yield Stress Test: Yield stress and viscosity are the primary parameters that represent the flowability of fresh CPBs (Liu & Fall, 2022). Yield stress is the lowest stress needed to initiate flow, indicating the magnitude of particle-particle forces (Simon & Grabinsky, 2013). Since CPB is a non-Newtonian fluid, the Wykeham-Farrance vane shear apparatus suitable for soil and non-Newtonian fluids were used for this study. The apparatus consists of a vane arrangement with four 2.5cm x 2.5cm blades powered by an electric motor. Then the torque was applied, and the resistance of the sample against the blades of the vane was measured by the attached deflection spring. The maximum torque reading was measured on the comparator. The test was performed on all CPB specimens with different proportions of binder mixture over a curing period of 0, 0.5, 1-, 2-, and 4 hours following ASTM D4648/D4648M-16 (ASTM 2011). The specimens were covered with a thin plastic film. The yield stress was determined based on the following equation:

τy =

2Tm 1 3

𝐻 𝐷

π𝐷3 [ + ]

here τy is the yield stress, Tm is the maximum torque, H is the length, and D is the diameter of the vane, respectively. Viscosity test: Viscosity is a crucial aspect of CPB behavior. It corresponds to the force required to maintain the flow of the mixture. The viscosities of the samples were measured using the Brookfield digital viscometer (model LVDV-E, with a base spring torque of 673.7 dynes-cm). The principle involves, measuring the viscous drag of the fluid measured against the rotating spindle through the spring deflection; finally, the viscosity was reported in cp (Brookfield, 2016). The size and shape of the spindle and the spindle speed are chosen in rpm depending on the type of mixture. For this study, spindle number 5 and a rotational speed of 50 rpm were chosen. Fresh samples were poured into a 10cm x 10cm mold and capped with thin plastic wraps to prevent water evaporation, as when the backfill was transported in practice by pipes. The samples were tested for 0, 0.5, 1, 2, 4 hours curing time, 3 times for accuracy of results. 2.2.2

Setting time measurements

Knowledge of setting time is important to the overall productivity, mechanical and economic performance of the backfill operation. Initial set denotes the loss of workability and the start of paste backfill stiffening or strength gain. In addition, early strength gains are crucial for barricade opening and extraction of neighboring stopes to ultimately increase mining efficiency by reducing the mining cycle (Yin et al., 2012). Therefore, the impact of ternary binder (PCI-Slag-LS) on setting time was investigated using the Vicat apparatus. The test is based on the principle of mixture resistance to needle penetration and is performed according to ASTM C191-13. In this test, cylindrical molds of 100 mm diameter and 40 mm height were used. The initial setting time was recorded when the needle reached 25 mm penetration from the bottom, and the final setting time was considered when the outer collar of the needle did not leave an impression on the specimens. 2.2.3

Microstructural analyses

To comprehend the evolution of the hydration process of CPB with the ternary binder at the microstructural level, by measuring the sudden mass changes under the application of heat, thermogravimetry (TG) and differential TG (DTG) were performed on cement paste specimens with considered binder proportions. The specimens were made with a binder-to-water ratio of 1:1 to accelerate the hydration process, and samples were oven dried at 45 °C until the mass stabilized, then powdered and sieved through a 40-micron sieve. The thermal analyzer, TGA Q5000 V20.13, was used to carry out a thermogravimetric analysis on the cement paste samples up to a temperature of 1000°C. The progression of cement hydrate decomposition is represented by the TGA curve, and the endothermic peak by the derivative of the TGA curve. Furthermore, the typical cement hydration curve is divided

into three phases. The phase between 50 °C and 200 °C 400 °C and 500 °C, and 600 °C and 800 °C indicates free water and calcium silicate hydrates, ettringite, monosulfate decomposition, Portlandite (CH) dihydroxylation, and CaCO3 decarbonization, respectively (Deboucha et al., 2017). 2.2.4

Monitoring experiments

A set of monitoring experiments were performed on fresh CPBs prepared in the same way as the samples used for rheological testing. An electrical conductivity (EC) test was done on fresh CPB specimens to examine the progress of the cement hydration by monitoring changes in EC caused by ion movement and changes in water with time. Changes in EC were monitored using the 5TE sensor. The sensor has an accuracy of ± 10% for EC measurement, ranging from 0 dS/m to 23 dS/m. The freshly prepared samples were poured into a 20cm x 10cm mold, the sensors were inserted into the mold in the center and closed tightly with the cap. EC and temperature changes were tracked by an EM50 data logger. 3 3.1

RESULTS AND DISCUSSIONS Effect of ternary binder system (PCI-Slag-LS) on the rheological behavior of CPB

It was noticed that the partial substitution of slag by LS powder, with PCI as the ternary binder, has a considerable impact on the overall rheology of CPB (yield stress and viscosity). Figure 2 illustrates the influence of the ternary binder with varying dosages of LS on the yield stress evolution in the CPB samples. The yield stress of the mixtures in both groups A and B, with PCI/Slag ratios of 80/20, and B 50/50, respectively, decreased with increasing dosage of limestone in place of slag. Regardless of the cement and slag content, i.e., the yield stress of the freshly prepared sample (0 hours), after the addition of 0-20 wt% LS, decreased from 230 Pa to 187 Pa, in group A and from 216 Pa to 158 Pa in group B, respectively. In addition, during 4 hours of curing, the yield stress reduced from 489 Pa to 374 Pa in group A and from 461 Pa to 360 Pa in group B, respectively. In other words, the addition of LS to CPB improves its flowability, which is beneficial in mine backfilling practice. This yield stress reduction effect due to LS addition is attributed mainly to the following physical effects or factors (morphological, filler, and dilution) of limestone, as explained below: (i) The lubrication provided by the limestone particles to the mixture (Bentz et al., 2017); in other words, during the hydration process, different hydration products are produced, and ions are released in suspension, the location of hydroxyl groups (OH-) on the surface of calcite (Ca2+), gives a means of electrostatic repulsion between the particles and thus, improve the flowability of the suspension and reduces further flocculation (Bentz et al, 2017; Sekkal & Zaoui, 2013); ii) LS finer than cement acts as a filler, fills water-filled pores and expels excess water, thereby increasing the fluidity and decreasing the yield stress of the mixture (Zheng et al., 2016). However, further increasing the dosage of fine LS may increase the surface

area and water demand, which could increase the yield stress. An additional factor (chemical factor) should be considered as contributing to the decrease in the yield stress of the CPB mixture when adding LS up to 20 wt.%. This factor is the change in the repulsive forces between particles in the CPB system due to the addition of LS.

a) b)

water film. The dominant phenomenon of these two effects decides the ultimate increase or decrease in viscosity (Hoshino et al., 2006). Indeed, the addition of LS dosage increased the viscosity of the samples in groups A and B with PCI/Slag (50/20 and 80/20). However, the influence of LS viscosity on the samples in group B with 80% PCI was noted to be higher than on the samples in group A with 50% PCI. This may be due to the greater availability of cement and reactive alumina for hydration. In addition, more cement in the system utilizes all available LS as nucleation sites, enhancing the hydration reaction and potentially forming more hydration products.

Group A CPB samples (binder with 50% PCI)

a)

Group A CPB samples (binder with 50% PCI)

b)

Group B CPB samples (binder with 80% PCI)

b) Group B CPB samples (binder with 80% PCI) Figure 2. Influence of LS on the yield stress of CPB samples a) Group A b) Group B On the contrary, the viscosity of the samples in both groups, A with PCI/Slag 50/20 and B with 80/50, increased, with the percentage of limestone replacing slag, regardless of the PCI/Slag ratio in the ternary binder as depicted in Figure 3. The increase in viscosity upon the addition of LS is mainly attributed to its filler and nucleating effect. The viscosity is mainly a function of solid content, particle size distribution in the mixture, and water demand (Vance et al., 2013; Xiao et al., 2021), which can be correlated with the surface area of LS. Indeed, the SSA of LS is 2.69 (m 2/g) and higher than that of slag and cement, which may increase the water demand of the mixture, increasing the viscosity. The finer limestone induces the nucleation effect in the mixture (Kumar et al., 2013; Oey et al., 2013), which accelerates the degree of hydration and can produce more hydration products (C-S-H, C-H). Thus, higher hydration products increase the volume and concentration of solids in the mixture and, ultimately, the viscosity of the samples. However, this can also increase the amount of free water on the top surface, which can increase the thickness of the

Figure 3. Influence of LS on the viscosity of CPB samples a) Group A CPB samples b) Group B CPB samples. The acceleration of the hydration rate due to the nucleation effect of LS is also correlated with the monitoring of EC; as shown in Figure 4a, it is seen that the conductivity of the samples with 5 and 10 wt.% LS reached a maximum value of 3.90 mS/cm and 3.76 mS/cm at 5 hours and 5.4 hours, while the EC of 50P-50S-0L peaked at 6 hours with 3.56 mS/cm, which is lower compared to the ternary mixture samples, indicating a slow hydration rate. A similar pattern is observed in Group B samples, from Figure 4b, the samples with 5 and 10 wt.% LS peaked before 4 hours with peak values of 4.74 mS/cm and 4.26 mS/cm, respectively, while the EC of 80P-20S-0L peaked after 4 hours. This rise in hydration products due to LS addition mentioned above is also observed and confirmed by the DTG/TG results, as shown in Figure 5. The first and second peak, which represent the hydration products (e.g.,

C-S-H, ettringite, and CH) in the system, were found to be slightly higher for the samples with 20% LS 80P-0S-20L than the control sample 80P-20S-0L, respectively, this indicates the acceleration of cement hydration in the presence of limestone. Also, as expected, an intense increase during the 3rd peak, which represents the decomposition of CaCO3, was noted for the samples with LS because it added CaCO3 into it (Feng et al., 2022).

a)

Group A CPB samples (binder with 50% PCI)

b)

Group B CPB samples (binder with 80% PCI)

Figure 4. Changes in EC of the CPB samples with 0%, 5%, and 10% of LS a) Group A b) Group B

3.2

Development of initial and final setting times of CPB with binary and ternary cement blends

The results of the measurements of the setting time (initial and final) of all the CPB samples with binary and ternary cement blends are shown in Figure 6. It indicates the trend of increase/retardation in the setting time with a decrease in cement content, irrespective of the variation in slag and limestone content. Control sample 80P-20S-0L from group B (with 80% PCI) has a shorter initial and final setting time (at 18 and 32 hrs) than that of the control sample of group A 50P-50S-0L (with 50% PCI) (at 22 and 38 hrs). This effect was attributed to the generation of more hydration products in the samples with higher cement content, leading to the early setting as already demonstrated with DT-DTG results discussed above. Further, hardening/solidification of the mix containing cement mainly occurs due to the creation of hydration products of the cement-like C-S-H, Portlandite, and ettringite (Klein & Simon, 2006). As in the hardening pastes, the setting is the process of connection of isolated or weakly bound particles which form solid paths in paste (Yin et al., 2012). Figure 6 also shows the influence of the ternary binder, with a varying dosage of LS in replacement with slag, on the setting time of the CPB mix. It is noticed that the addition of an increasing percentage of limestone prolonged/delayed the setting time of the CPB mix. The initial and final setting time of group A control sample 50P50S-0L was 22.5 hrs and 38hrs respectively; upon addition of 5% to 20% LS, the initial set of samples was noted as 23.5 and 26.5 hrs, and the final setting time at 39 hrs and 41 hrs respectively. The initial setting time was delayed by 4hrs and the final setting time by 3hrs, compared to the control sample with 0% LS. The same pattern was observed in the samples of group B; the initial and final setting time of control sample 80P-20S-0L was observed at 18 and 31hrs, respectively. Further, upon addition of incremental dosage of LS from 5% to 20%, the initial setting time was delayed and noted at 18.5 hrs and 31.5 hrs and the final setting time at 20.5 and 33 hrs. This infers the maximum delay with 20% of LS in the initial and final setting time was 2.5 and 2 hrs, respectively.

Figure 5. TG/DTG analysis results for specimens cured for 4hrs of curing time. Figure 6. The effect of the addition of LS in the CPB samples on the evolution of initial and final setting time a) Group A CPB samples and b) Group B CPB samples

This delay in setting time upon the addition of LS is primarily due to the nucleation and dilution effects as previously observed in cement-based materials. However, from Figure 5 TG results of samples, it is seen that less product of hydration formed in 50P-30S-20L, with 20wt% of LS, than in 50P-50S-0L, no such formation of additional hydration products is reported. Indeed, in a sample from group B, a sample with 20 wt.% LS, 80P-0S-20L, a somewhat higher peak was observed than 80P-20S-0L. However, the amount of the additional hydration products was insufficient to shorten the sample's setting time. Further, as indicated in Figure 4, an increase in EC was noted when LS powder was added to the control samples from both groups. However, higher EC may not be directly correlated with the formation of more hydration products as it may not be the result of only additional hydration products but due to the finer nature of LS with higher surface area, which may influence the movement of ions in pore solution. Hence it can be inferred that mainly nucleation and dilution effects are dominating and influencing setting time, as the chemical effect of LS seems to have negligible or little impact (delay) on the setting time of CPB as the chemical reaction between cement hydrate and LS does not occur before 24hours (Ren et al., 2020) . The Delay in setting time observed upon the addition of LS is similar to elsewhere (Meddah et al., 2014) 4

SUMMARY AND CONCLUSIONS

Extensive experimental work was performed on several CPB specimens with variable PCI/Slag content (80/20 and 50/50) and increasing dosage of LS in place of slag were cast and cured, and their rheological properties were evaluated over a curing period of 4 hours (0, 0.25, 1, 2, 4 hours). The impact of LS on the flowability of CPB and the microstructure of CPB at different curing times is investigated through rheological tests (yield stress and viscosity) and microstructural analysis. A summary of the major findings from the results is as follows, i.) Regardless of the different dosages of cement, slag, and LS, yield stress and viscosity with increased curing time due to the increase in hydration products, with the progress of cement hydration and the self-desiccation. ii.) The binary samples with PCI/Slag, i.e., 50/50, gained higher yield stress with time than the samples with PCI/Slag 80/20. The phenomenon is due to interparticle friction resulting from the filler effect of the slag and a denser matrix due to its finer nature. iii.) The addition of LS with incremental dosage (5-20 wt%) in the form of a ternary binder impacts the rheology (flowability) by decreasing the yield stress and increasing the viscosity of fresh CPB. Interestingly, the effect of LS is complementary to the effect of slag on the rheology of the mix. (iv.) This effect of LS on the rheology of CPB, the reduction in yield stress, was primarily attributed to the physical effects of LS, i.e., the finer nature., which provides additional lubrication to the mix, and additional repulsive forces. However, the viscosity increased due to the nucleation effect of finer limestone and the higher water demand with higher surface area.

v.) The setting time (initial and final) of the samples with 80/20 PCI/Slag mix was found to be shorter than the sample with 50/50 because of formation of more hydration products (C-S-H, ettringite, AFm) in the sample with higher cement content. vi.) The delay in setting time of CPB samples when LS was added in increasing dosage was observed and is consistent with the DTG/TG results. This indicates the nucleation effect of LS on cement hydration; however, more hydration product was not formed in all samples. Despite the results obtained in this work, detailed chemical and microstructural studies are recommended to gain a deeper insight into the impact of LS on the microstructure and chemical composition of the CPB system with a ternary binder (PC, slag, LS). In addition, the use of a modeling tool to optimize the use of LS in CPB with a ternary binder. 5

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Paste Considering Fineness Effects. Journal of Materials in Civil Engineering, 32(10), 04020289. Sekkal, W., & Zaoui, A. (2013). Nanoscale analysis of the morphology and surface stability of calcium carbonate polymorphs. Scientific Reports, 3(1), 1587. Simon, D., & Grabinsky, M. (2013). Apparent yield stress measurement in cemented paste backfill. International Journal of Mining, Reclamation and Environment, 27(4), 231–256. Snellings, R., Machner, A., Bolte, G., Kamyab, H., Durdzinski, P., Teck, P., Zajac, M., Muller, A., de Weerdt, K., & Haha, M. B. (2022). Hydration kinetics of ternary slag-limestone cements: Impact of water to binder ratio and curing temperature. Cement and Concrete Research, 151, 106647. Vance, K., Aguayo, M., Oey, T., Sant, G., & Neithalath, N. (2013a). Hydration and strength development in ternary portland cement blends containing limestone, fly ash, or metakaolin. Cement and Concrete Composites, 39, 93–103. Vance, K., Kumar, A., Sant, G., & Neithalath, N. (2013). The rheological properties of ternary binders containing Portland cement, limestone, and metakaolin or fly ash. Cement and Concrete Research, 52, 196–207. Wang, D., Shi, C., Farzadnia, N., Shi, Z., Jia, H., & Ou, Z. (2018). A review on use of limestone powder in cementbased materials: Mechanism, hydration and microstructures. Construction and Building Materials, 181, 659–672. Wang, Y., Wu, A., Zhang, L., Jin, F., & Liu, X. (2018). Investigating the Effect of Solid Components on Yield Stress for Cemented Paste Backfill via Uniform Design. Advances in Materials Science and Engineering, 2018, 1–7. Wu, D., Fall, M., & Cai, S. J. (2013). Coupling temperature, cement hydration and rheology of fresh cemented paste backfill. Minerals Engineering, 42, 76–87. Xiao, B., Fall, M., & Roshani, A. (2021). Towards Understanding the Rheological Properties of SlagCemented Paste Backfill. International Journal of Mining, Reclamation and Environment, 35(4), 268–290. Yin, S., Wu, A., Hu, K., Wang, Y., & Zhang, Y. (2012). The effect of solid components on cemented paste backfill's rheological and mechanical properties. Minerals Engineering, 35, 61–66. Zheng, J., Zhu, Y., & Zhao, Z. (2016). Utilization of limestone powder and water-reducing admixture in cemented paste backfill of coarse copper mine tailings. Construction and Building Materials, 124, 31–36. Ziegler, M., Reiter, K., Heidbach, O., Zang, A., Kwiatek, G., Stromeyer, D., Dahm, T., Dresen, G., & Hofmann, G. (2015). Mining-Induced Stress Transfer and Its Relation to a Mw 1.9 Seismic Event in an Ultra-deep South African Gold Mine. Pure and Applied Geophysics, 172(10), 2557–2570.

Study of the Impact of Nano-particles on strength development of cemented paste backfill Amirreza Saremi, Mamadou Fall Department of Civil Engineering – University of Ottawa, Ottawa, Ontario, Canada ABSTRACT This paper evaluates how adding various types of nanoparticles (NP) affects one of the key engineering parameters of cemented paste backfill (CPB), which is strength development as a function of curing time. To achieve this objective, varying doses of four distinct kinds of NPs, including nano-silica (SiO2), nano-calcium carbonate (CaCO3), nano-iron oxide (Fe2O3), and nano-aluminum oxide (Al2O3), were utilized. To avoid NP agglomeration in the CPB microstructure, which could have a detrimental impact on the CPB's mechanical performance, an ether-based Polycarboxylate Superplasticizer (SP) was used to evenly disperse the NPs throughout the hydration reaction medium. Type I ordinary Portland cement was used to prepare CPB and tap water was also utilized to blend the CPB components. A comprehensive testing and monitoring programme was developed to evaluate the effect of NPs on the development of the strength of CPB. Uniaxial compressive test (UCS) was done to measure the sample's strength over curing time. Nano-CPB samples or cement pastes were subjected to several microstructural analyses or tests, including thermal analysis (thermogravimetry (TG), differential thermogravimetry (DTG)), mercury intrusion porosimetry (MIP) tests, and X-ray diffraction (XRD) analysis to gain insight into the fundamental mechanisms responsible for the behaviour observed. In the absence of the dispersing agent, the aggregated NPs were found to degrade the strength development of nano-CPB samples. In contrast to the control sample, nano-CPB samples combined with a dosage of SP exhibited greater UCS values. Microstructural analysis results indicated that the addition of NPs increased the generation of hydration products, which primarily increased the resistance to shear stress between tailings particles. The highest overall improvement in nano CPB's strength was reported in samples containing a fixed concentration of SP (0.125%) and NPs (1%). RÉSUMÉ Cet article évalue comment l'ajout de différents types de nanoparticules (NP) affecte l'un des principaux paramètres techniques du remblai en pâte cimentée (CPB), à savoir le développement de la résistance en fonction du temps de durcissement. Pour atteindre cet objectif, des doses variables de quatre types distincts de NP, dont la nano-silice (SiO2), le nano-carbonate de calcium (CaCO3), le nano-oxyde de fer (Fe2O3) et le nano-oxyde d'aluminium (Al2O3), ont été utilisées. Pour éviter l'agglomération des NP dans la microstructure du CPB, qui pourrait avoir un impact négatif sur les performances mécaniques du CPB, un superplastifiant polycarboxylate (SP) à base d'éther a été utilisé pour disperser uniformément les NP dans le milieu de réaction d'hydratation. Le ciment Portland ordinaire de type I a été utilisé pour préparer le CPB et l'eau du robinet a également été utilisée pour mélanger les composants du CPB. Un programme complet d'essais et de surveillance a été mis au point pour évaluer l'effet des NP sur le développement de la résistance du CPB. Des essais de compression uniaxiale (UCS) ont été réalisés pour mesurer la résistance de des échantillons au cours du temps de durcissement. Les échantillons de nano-CPB ou les pâtes de ciment ont été soumis à plusieurs analyses ou tests microstructuraux, notamment des analyses thermiques (thermogravimétrie (TG), thermogravimétrie différentielle (DTG)), des tests de porosimétrie par intrusion de mercure (MIP) et des analyses de diffraction des rayons X (XRD) afin de mieux comprendre les mécanismes fondamentaux responsables du comportement observé. En l'absence d'agent dispersant, il a été constaté que les NPs agrégées dégradaient la résistance des échantillons de nano-CPB. Contrairement à l'échantillon de contrôle, les échantillons de nano-CPB combinés à un dosage de SP ont présenté des valeurs UCS plus élevées. Les résultats de l'analyse microstructurale ont indiqué que l'ajout de NP augmentait la génération de produits d'hydratation, ce qui augmentait principalement la résistance à la contrainte de cisaillement entre les particules de résidus. L'amélioration globale la plus importante de la résistance des nano CPB a été observée dans les échantillons contenant une concentration fixe de SP (0,125 %) et de NP (1 %). 1

INTRODUCTION

The mining industry serves as a vital driving force in the economic progress of both developed and developing nations. Over the past few decades, there has been a remarkable surge in the demand for essential minerals, prompting an increased extraction of ore bodies from the earth's crust. Mining operations generate a considerable volume of tailings during the processes of mineral

extraction and refinement. The most prevalent method for storing mine waste, surface tailings disposal, presents multiple potential hazards to the environment. Firstly, owing to the high water content in tailings and construction challenges, conventional tailings impoundments are vulnerable to geotechnical failures, such as liquefaction failure, dam collapse, and foundation failure. These failures frequently lead to severe consequences, including human casualties, financial losses, and far-reaching social and

environmental impacts. Secondly, from an ecological standpoint, the surface disposal of tailings heightens the risk of environmental contamination by exposing hazardous constituents, like heavy metals and acidic compounds, to the surrounding environment. Consequently, it is crucial to explore and adopt alternative techniques for managing mine waste more effectively and sustainably (Ghirian and Fall 2016, Yilmaz and Fall 2017, Haruna and Fall 2020b). Backfilling has emerged as a more eco-conscious approach to tailings management and has been widely adopted in underground mines worldwide. This process entails the return of a substantial portion of mine waste, such as tailings, to underground mine stopes. In recent times, cemented paste backfilling has gained recognition as an innovative technique within the realm of mine backfilling. Cemented paste backfill (CPB) typically consists of a blend of tailings (75-85 wt %), hydraulic binder (3-9 wt % of total dry paste weight), and water, offering a sustainable solution to address the environmental concerns associated with tailings disposal (Fall and Benzaazoua 2005, Sivakugan et al. 2006). The mechanical resilience of CPB is fundamentally tied to its structural stability. A CPB with greater mechanical strength ensures improved ground support and enhances the safety of subterranean mining operations. Thus, the pace at which CPB develops mechanical strength is of equal importance, as it dictates the effectiveness and productivity of the mining procedure. A quicker strength-gaining rate enables mine workers to return to the backfilled areas promptly after a stope is filled with CPB, optimizing work schedules and mitigating potential holdups that can impact the mine's financial efficiency (Yilmaz and Fall 2017, Roshani and Fall 2020b). Additionally, an accelerated strength-gaining rate contributes to more efficient mine tailings management and promotes eco-friendly mining practices by guaranteeing timely and safe containment of tailings materials. The uniaxial compressive strength (UCS) of CPB is typically used to measure mechanical stability. Once CPB is introduced into the stopes, mining activities in adjacent areas are suspended until the CPB achieves the necessary strength for stability. As a result, the rate of CPB strength development bears a substantial influence on mining productivity. To enhance mining effectiveness, it is crucial to decrease the curing time of CPB. In other words, during the early stages of curing, strength should develop as rapidly as feasible. Moreover, the structural integrity of CPB is of utmost importance, as a collapse may lead to serious repercussions, including injuries or fatalities among workers and damage to machinery. Consequently, the rate at which CPB strength increases is an essential factor in design considerations. Self-desiccation, which occurs due to water consumption during cement hydration, is a key performance factor in CPB (Li and Fall 2018). This phenomenon leads to a reduction in pore water pressure and/or the creation of matric suction in the CPB, positively impacting early-age strength development by increasing effective stress within the backfill structure. As a result, self-desiccation lowers the chances of CPB liquefaction.

Greater strength during CPB's early curing stages and reduced time between mining cycles contribute to enhanced mine productivity. One practical approach to decrease the time needed for higher strength in the early stages of curing is to increase the binder/cement content in the CPB, which results in more cement hydration products and accelerates the self-desiccation process (Fall et al. 2008). However, this approach has drawbacks: increasing binder content raises costs, as the binder can account for up to 75% of the final CPB production cost, and using more cement contributes to carbon dioxide emissions, raising environmental concerns (Fall and Benzaazoua 2003). Therefore, it is crucial to find an alternative method to boost the CPB strength increase rate. Numerous studies have been conducted to enhance the strength and mechanical properties of cement during early curing stages using chemical admixtures or additives, as cement is a widely-used construction material. With significant advancements in nanotechnology, nanomaterials have been employed to improve concrete properties in construction. Research has shown that adding Nanoparticle additives (NP) increases the compressive strength of cement mortar (Ltifi et al. 2011, Nazari and Riahi 2011, Oltulu and Şahin 2013). However, the high potential for agglomeration in nano-scale materials necessitates the use of a suitable dispersing agent such as the superplasticizer. Superplasticizers, when added to the CPB mixture, have a significant impact on its performance, leading to substantial enhancements in various properties. They considerably reduce the yield stress and viscosity of CPB, regardless of the tailings type used, and increase the unconfined compressive strength (UCS) of the CPB (Haruna and Fall 2020a, 2020b, Al-Moselly et al. 2022). Additionally, superplasticizers contribute to better environmental performance by reducing the hydraulic conductivity and reactivity of the CPB (Lothenbach et al. n.d.). Despite studies on adding NP additives to cement, little research has been conducted on their use in CPB or on the effects of nano-additives on CPB's mechanical performance. This experimental study examines the impact of different dosages of four types of nanoparticles (NPs), including nano-silica (SiO2), nano-calcium carbonate (CaCO3), nano-iron oxide (Fe2O3), and nanoaluminum oxide (Al2O3), on the mechanical strength of the CPB. 2 2.1 2.1.1

EXPERIMENTAL PROGRAM Materials Tailings

In this study, synthetic tailings called silica tailings (ST) are utilized. Comprising 99.8% quartz, ST is the primary mineral found in tailings from Canadian hard rock mines (Fall et al. 2010). Employing STs aims to minimize material variability and uncertainties in test outcomes due to the presence of reactive minerals in natural tailings. These minerals may react with the binder during CPB hydration,

complicating the interpretation and analysis of test findings. The STs used in this research have a particle size distribution nearly identical to that of natural tailings, based on tailings from nine hard rock mines in Eastern Canada, as depicted in Figure 1. Table 1 presents the physical properties and mineral composition of the tailings employed in this experiment.

Specific surface area (m2/g)

Nano-Fe2O3

Nano-Al2O3 100 90

ST

80

9 Mines

70 Finer percent (%)

60 50

2.1.3

40 30 10 0.1

1 10 Particle size (µm)

100

1000

Table 1. Physical properties of tailings

2.1.2

Gs 2.7

D10 µm 1.9

D30 µm 9.0

D50 µm 22.5

D60 µm 31.5

2.9

1.8

9.1

20.0

30.8

Nanoparticles

In this investigation, four varieties of NP additives are employed to prepare the requisite CPB specimens. These NPs comprise Nano-Calcium carbonate (CaCO3), NanoIron oxide (Fe2O3), Nano-Aluminum oxide (Al2O3), and Nano-Silica (SiO2). Pertaining to Nano-silica, TetraethylOrthosilicate (TEOS), the manufacturing precursor of nano-SiO2, was utilized. Dow Corning, Inc. provided the TEOS under the brand name XIAMETER® OFS-6697 silane. TEOS was employed as a colloid, resulting in improved nano-SiO2 particle dispersion within the CPB mixture (Thomas et al. 2009). Table 2 delineates the physical and chemical properties of the NP additives. Table 2. Chemical nanoparticles Nano-SiO2

Nano-CaCO3

and

physical

specifications

Appearance SiO2 (%) Particle size (nm) Specific surface area (m2/g)

Clear liquid >99 70%) as the cementitious content and rely minimally on the Portland or limestone cement for property development. Other mix design studies have been completed on soilcement mixtures and have shown greater variation from using limestone cement vs ordinary Portland cement, showing a 10% to 50% reduction in UCS. While this variation has not been confirmed in a controlled study, it highlights a potential issue with using limestone cement in these applications based on previous experience. This requires expectations to be re-established. This industry change has limited confidence in “off-the-shelf” mixes and it is more prudent than ever to complete pre-construction mix designs utilizing the intended materials to be used for the full-scale installation to confirm expected mix properties. 3.4

Bedrock Key and Refusal

Minimum key into competent bedrock is another item regularly specified in slurry trenching that in some cases may not be necessary and will significantly increase cost to the client. Installation cost is rarely considered when a bedrock key is specified and as discussed in Ruffing and Evans (2020) this can increase the overall cost on the order of 50% to 200% in addition to lengthening schedule. Given the potential cost impact, it is important for designers to confirm the necessity of a bedrock key and to refrain from including one just to be conservative. The NAVFAC guidespec for slurry cutoff wall construction (UFGS-02 35 27, 2010), briefly addresses bedrock key in Section 3.3.1 “Confining Stratum Excavation” stating the following: “NOTE: If the confining stratum is a competent low permeability bedrock, a very small penetration into the bedrock may be satisfactory. High costs may result by requiring a 600 mm (2 foot) key into competent bedrock. Remove this paragraph if not required in the project.” It is also recommended that a refusal or termination clause be included in slurry trenching specifications to delineate risk by quantifying the maximum excavation effort to be used in achieving a design depth and/or specified key into bedrock. In many instances, excavating efforts beyond this refusal criterion in competent bedrock offers limited technical benefit and, in some cases, can have a net negative effect as additional higher energy methods, such as rock drilling, chiseling, and blasting, that may be required to disaggregate the bedrock to obtain further key could potentially create flow pathways that did not exist prior. Per Ruffing and Evans 2020, a recommended refusal criterion is as follows: “Refusal shall be achieved after 5 mins [up to 15 mins in extreme cases] of effort over a 6 m (20 ft) long [up to 9 m (~30 ft) long] excavation cut or irregular observations of

the machine behavior indicative of refusal, e.g. excessive ground vibrations or shaking.” 3.5

Statistical Allowances

Statistical allowances in performance criteria have been more commonly utilized but improvements are still possible. As previously discussed by Ruffing and Evans (2020) and Coughenour et al. (2023), “including reasonable statistical allowances in the specifications to account for inevitable variability is not only prudent, but also an important exercise within the context of the overall factor of safety. By not including statistical allowances in specified values, Contractors are likely to use overly conservative mix designs that result in unnecessarily high installation costs to the Owner.” Ruffing and Evans (2020) provides several statistical allowance examples specific to hydraulic conductivity of environmental cutoff walls, and Coughenour et al. (2023) provides similar examples for UCS that can be applied to soil-cement or self-hardening slurry mixtures. The overarching point of each of the referenced papers is that including statistical allowances in performance criteria can typically reduce the overall construction costs to the client without impacting function or safety factors. 4

APPLICATIONS AND INDUSTRIES

Slurry walls have been utilized in Canada for many applications and industries for decades now, with the authors having personal experience on dozens of those projects. The subsections below provide brief summaries of those applications as well as other potential applications. 4.1

Environmental Containment

Containment of contaminated soils and groundwater is one of the most common applications of slurry trenches. Slurry trenches can be utilized to encircle contaminated subsurfaces, limiting or preventing the migration of contamination to adjacent parcels or waterways. Some example environmental slurry wall applications completed in Canada include: landfill closures, papermill lagoon closures, groundwater pump and treat systems, ash/tailings pond construction and closures (discussed in more detail below), among others. 4.2

Dams/Levees

Construction or rehabilitation of dams and levees is one of the most commonly utilized applications for slurry trenches. Slurry trenches can be installed through dams and levees in lieu or in addition to clay cores to provide a lowpermeable core. Slurry trenches installed in dams and levees are typically installed from the crest of the dam/levee to design depth which makes them especially desirable for rehabilitation of dams as it can be done with minimal interruption or impact to the current use of the dam/levee.

4.3

Tailings

Related to the dams and levees section, the recent trend in slurry wall installations is for construction or improvements to dams of tailings and other mine waste ponds. For these applications cutoff walls often serve a dual purpose, to structurally improve the dam to prevent failure, as well prevent the migration of the contents of the ponds which commonly contain heavy industry impacted materials. 4.4

Dewatering

Slurry trenches are also commonly used for groundwater control for excavation dewatering projects. An example project was a deep perimeter slurry cutoff wall installed on the coast of Newfoundland for the purpose of dewatering interior for use as a graving dock for the construction of an offshore oil rig structure (Coughenour and Ruffing 2019). 4.5

Land Reclamation and Re-naturalization

Slurry walls were utilized on the Toronto Port Lands land reclamation and re-naturalization project to allow for the realignment of the Don River outlet into the Toronto Inner Harbor and Lake Ontario. The slurry walls are part of a system to establish a more natural man-made river through the Port Lands, alleviating flood from the Don River and helping to transform the industrialized area back into its previously lush ecosystem. 4.6

Soil Excavation

Slurry trenching can also be utilized as a means for mass soil replacement by excavating undesirable soils, whether contaminated or physically not desirable, under slurry and backfilling with a clean/controlled property fill without the use of traditional shoring. (Geo-Ottawa Reference). 5

CONCLUSION

While cutoff walls have been used for decades, there are constant new developments both in regards to lessons learned during construction and subsequent improvements from those lessons learned, as well as specified requirements for acceptance criteria. The author’s objective with this paper was to share these lessons learned and input on certain specification developments to help guide the overall improvement of cutoff wall installations. 6

REFERENCES

Coughenour, N., Ruffing, D, and Evans, J. 2018. Lessons Learned: Self-Hardening Slurries in Slurry Trenching. GeoEdmonton, 72nd Canadian Geotechnical Conference, Edmonton, AB. Coughenour, N., and Ruffing, D, 2019. Case Study: Installation of a Soil-Cement Bentonite Groundwater Cutoff Wall in Argentia, NL. GeoSt. John’s, 73rd Canadian Geotechnical Conference, St John’s, NL.

Coughenour, N., Ruffing, D, and Artman, S. 2019. Case Study: Design-Build of a Cement-Bentonite Groundwater Cutoff Wall in Fort McMurray. GeoSt. John’s, 73rd Canadian Geotechnical Conference, St John’s, NL. Coughenour, N., Ruffing, D, and Evans, J. 2023. Sampling and Laboratory Testing of Soil-Cement from Soil Mixing And Slurry Trenching. Deep Foundations Institute (DFI), 6th International Conference on Grouting and Deep Mixing, New Orleans, LA. Navin, M.P. and Filz, G.M., 2006. Reliability of deep mixing method columns for embankment support. GeoCongress 2006: Geotechnical Engineering in the Information Technology Age (pp. 1-6). Ruffing, D., Evans, J., and Coughenour, N. 2018. SoilBentonite Slurry Trench Cutoff Wall Longevity. International Foundation Congress and Equipment Expo (IFCEE), Orlando, FL. Ruffing, D. and Evans, J., 2020. “Design and Specification Considerations for Environmental Cutoff Walls,” Proceedings of the 45th Annual Conference on Deep Foundations. Originally: Baltimore, Maryland. United States Army Corps of Engineers, 2010. Guide specification for construction: soil-bentonite (S-B) slurry trench, UFGS-02 35 27.

Could regular satellite InSAR monitoring have helped prevent the Jagersfontein tailings dam failure? Skevi Perdikou, Andrew Lees Geofem, Nicosia, Cyprus ABSTRACT The use of space technologies for infrastructure asset monitoring and assessment has seen a great increase in recent years. New satellite constellations and advancing technologies have contributed to the endorsement of satellite-based assessments for infrastructure assets. In September 2022, a major breach occurred in the Jagersfontein diamond mine tailings storage facility in Free State, South Africa. A mudslide occurred that killed one person and swept away nine homes. This paper presents the results from a satellite-based analysis using the freely available Sentinel-1 data from the Copernicus constellation. Interferometric synthetic aperture radar (InSAR) analysis was used to retrospectively measure displacement in the dam prior to the failure. Results showed clearly increasing dam movements in the vicinity of the southeastern collapse long before the breach. Had this or any other suitable monitoring technique been in place prior to the collapse, adequate warning would have been provided to deploy appropriate prevention, mitigation or evacuation measures RÉSUMÉ L'utilisation des technologies spatiales pour la surveillance et l'évaluation des actifs d'infrastructure a connu une forte augmentation ces dernières années. De nouvelles constellations de satellites et des technologies avancées ont contribué à l'approbation des évaluations par satellite pour les actifs d'infrastructure. En septembre 2022, une brèche majeure s'est produite dans l'installation de stockage des résidus de la mine de diamants de Jagersfontein dans l'État libre, en Afrique du Sud. Une coulée de boue s'est produite qui a tué une personne et emporté neuf maisons. Cet article présente les résultats d'une analyse par satellite utilisant les données Sentinel-1 librement disponibles de la constellation Copernicus. Une analyse radar interférométrique à synthèse d'ouverture (InSAR) a été utilisée pour mesurer rétrospectivement le déplacement dans le barrage avant la rupture. Les résultats ont montré une nette augmentation des mouvements du barrage à proximité de l'effondrement sud-est bien avant la brèche. Si cette technique ou toute autre technique de surveillance appropriée avait été en place avant l'effondrement, un avertissement adéquat aurait été fourni pour déployer des mesures de prévention, d'atténuation ou d'évacuation appropriées. 1

INTRODUCTION

On 11th September 2022, a diamond tailings dam in the town of Jagersfontein, South Africa (Figure 1) was breached. This led to the release of sludge killing at least one person and causing damages to many houses. This recent event reminds us of the Brumadinho dam collapse on 25th January 2019 in Brazil, killing 272 people, causing an enormous environmental and economic disaster. In recent years, space technologies have seen dramatic and continuous advancements in terms of developed technologies but especially in the availability of data. New constellations of satellites have emerged with the Sentinel-1 satellites of the Copernicus programme enabling free data access globally. Sentinel-1 satellite data are used for the monitoring of infrastructure assets and the ground by providing displacement measurements with millimetric accuracy. Such data is available since 2015 (depending on the geographical region) enabling retrospective analysis and identification of trends. Similarly, commercial data are also available mainly by tasking a satellite, providing an improved spatial resolution. In this regard, the plethora of satellite data nowadays including the availability of free data with global coverage allows the monitoring of whole networks of infrastructure assets regularly, accurately and at the same instant.

Figure 1. The Jagersfontein tailings dam, 19 months before its collapse (from Torres-Cruz and O’ Donovan, 2023). This paper presents the results of historical satellite remote sensing analyses leading up to the collapse of the tailings dam in Jagersfontein, using the freely available Sentinel-1 data showing that the warnings were there prior to the disastrous event.

1.1

History of the dam

The city of Jagersfontein has a history of mining activities, dating back to the 1870s. In 1931 the De Beers Group acquired the mine and operated it until 1971 when underground ore extraction ceased. The mine changed ownership a few times: it was sold in 2010 and subsequently twice more with the last sale happening some months before the collapse of the dam. The last owner was the subsidiary company Jagersfontein Developments (Torres-Cruz and O’ Donovan, 2023). 1.2

Lead up to the collapse

On 11th September 2022, the dam collapsed at its southeastern corner in two places as visible from Google Earth imagery captured in October 2022 (Figure 2). A recent study by Torres-Cruz and O’ Donovan (2023) using optical satellite image analysis revealed that in February 2019 erosion gullies had developed on the internal face of the northern wall as well as on the external face of the southeastern wall where it failed. In addition, Google Earth imagery from February 2019 shows evidence of erosion or instability at the same location where the dam collapsed later (Figure 3).

Figure 3. Evidence of erosion or instability at the southeastern corner of the tailings dam (Google Earth imagery captured in February 2019).

Figure 4. How SAR works 2.1

Figure 2. Collapse of the tailings dam as obtained by optical satellites (Google Earth). 2

METHODOLOGY

For the analyses in this paper, satellite radar – or SAR data as they are widely known – have been used. SAR stands for Synthetic Aperture Radar. “Radar” because it transmits electromagnetic waves and receives reflections from objects to determine their distance or range. “Aperture” is another term for the antenna that transmits and receives the electromagnetic energy (Figure 4). The aperture size on board the satellites is restricted by practical considerations. For achieving useful spatial resolution, a sequence of acquisitions is acquired, simulating the behaviour of a larger antenna, thus the name “Synthetic“.

DInSAR

Differential Interferometric Synthetic Aperture Radar (DInSAR) analyses were performed for this study including a time series of single interferograms analysis over the period of August 2017 to August 2022 (prior to the September collapse). Furthermore, the Small Baseline Subset (SBAS) technique was also applied for the period of September 2020 to August 2022 (Berardino et. al. 2022; Lanari et. al. 2007) DInSAR analysis is based on the principle of measuring the phase difference between the radar waves of successive passes to determine the ground surface displacements. When data is obtained from successive passes over the same area, it is possible to detect ground surface displacements by analysing a single interferogram derived from a pair of SAR images (Figure 5). An interferogram is a representation of the displacements over an area between the two dates used to form the interferogram. The displacements are along the satellite line of sight (LOS). The first type of analyses used in this study refers to single interferograms, i.e., pairs of SAR images to determine the ground displacements that occurred between the two dates forming the interferogram. When a large dataset of SAR images is used for the

analysis, millimetric accuracy can be achieved for the measured displacements (Werner et al., 2003). An important consideration during this process is the estimation of the different components comprising the phase difference between successive image acquisitions. These include the topography, atmospheric delay and incoherent components such as presence of vegetation or noise in the data. These are called residual phase components which need to be estimated and removed to accurately determine the ground displacements. In this paper, the SBAS DInSAR technique was implemented to measure the displacements over the study period (September 2020 to August 2022). SBAS is a distributed scatterer technique based on the development of image pairs between any of the images that form the dataset as long as they satisfy the set thresholds of the temporal and spatial baselines in order to mitigate the decorrelation phenomena and minimise incoherence. The image pair used for the generation of the interferograms are selected to minimise the spatial and temporal separation (baseline) between the acquisition orbits. The SBAS technique enables the exclusion of interferograms (image pairs) that are incoherent to improve the results. The use of successive images with high temporal resolution (e.g., every 12 days) limits artefacts and noise related to atmospheric effects and vegetation growth.

3.1 Single interferograms

Figure 5. Representation of the successive acquisition of SAR images for the measurement of the phase difference between them.

Figure 6. Interferogram between 16th and 28th June 2018 showing no displacements at the locations where the wall collapsed (black lines show the boundaries of the location where the dam collapsed, also shown in Figure 7). Some displacements are shown along the east perimeter wall (orange colour).

2.2

Data

The SAR images used were from the freely available Sentinel-1 satellite of the Copernicus programme for both types of analyses. For the single interferograms analyses, data covering the period from August 2017 to August 2022 were used. More than forty interferograms were developed covering different periods. For the SBAS analysis, data covering the period September 2020 to August 2022 were analysed as a time series. All data are from the ascending orbit and measured displacements are along the satellite LOS. 3

RESULTS

Two types of analyses have been performed: single interferograms and the SBAS technique. The results from each of these are described in this section.

A time series of single interferograms covering the period August 2017 to August 2022 was developed. For most of them, the interferograms were produced from images that were 12 days apart to ensure good coherence between them. The results show no displacements along the walls of the tailings dam for August 2017 and June 2018. An example of a single interferogram between 16th June 2018 and 28th June 2018 is shown in Figure 6. A graphical presentation of the displacements for the 12-day intervals in August 2017 and June 2018 are shown in Figure 7. In Figure 7 and all the graphs in the following figures, the horizontal axis shows the distance along the tailings dam crest from the south-west corner in an anti-clockwise direction. The graph in Figure 7 shows some displacements along the tailings dam perimeter wall, along the east part, outside the locations where the wall collapsed. The area collapsed is also marked on the graph (between 520m and 1060m).

A further four interferograms were produced for 12-day periods in July and August 2019 and in September and October 2020. In all four interferograms, significant were detected in the locations where the tailings dam collapsed in September 2022 (Figure 8). Interferograms for 12-day periods in May/June and June/July 2021 did not show any significant displacements along any part of the tailings dam. This is in agreement with reports from Torres-Cruz and O’Donovan (2023) and Banya et. al. (2022) stating that the tailings dam had been shut between 2020 and June 2021 (Figure 9). Subsequent analyses of single interferograms for 12day periods from July 2021 to October 2021 (a few days before the collapse) showed increasing displacements at the locations where the dam collapsed (southeast corner), as shown in Figure 10.

Figure 7. Displacements determined from interferograms for 12-day periods in August 2017 and June 2018. The area along the wall that collapsed is within the black boundary lines (520 to 1060m).

Figure 8. High displacements in the area that later collapsed all four interferograms

Figure 9. No significant displacements in May 2021 to July 2021. Several interferograms were developed for 12-day periods during 2022 which all showed increasing displacements in the area that collapsed. Two separate graphs are presented one for the period April 2022 to July 2022 (Figure 11) and one for August 2022 (Figure 12). It is apparent that the August 2022 graph shows some positive values of LOS displacements followed by negative values. An interferogram from the August imagery is also shown (Figure 13). It is apparent that in none of the interferograms there are significant displacements in any other part of the tailings dam perimeter. All of the interferograms apart from the period when the facility was shut (2020 to June 2021) show high displacements in the locations where the failure occurred for many months. .

Figure 10. Increasing displacements between July 2021 and October 2021.

Figure 11. Increasing displacements between April 2022 and July 2022.

Figure 12. Increasing displacements in August 2022, a few days before collapse. 3.2 SBAS For the SBAS analysis, a two-year dataset covering the period September 2020 to August 2022 was used. The results show increasing LOS mean annual velocity values at the southeast corner shown with red dots indicating movement away from the satellite (Figure 14) with some gaps in the data and a few blue points indicating movement towards the satellite. The direction of movement away and towards the satellite could be due to the orientation of the dam with the east side moving away from the satellite and the south side moving towards the satellite. The satellite is in an ascending orbit while obtaining the data as shown in Figure 14.

Figure 13. Interferogram showing the LOS displacements between 6th August 2022 and 18th August 2022. Blue indicates movements towards the satellite while red indicates movements away from the satellite.

to show the displacements over the 12-day period along the walls. The interferograms analysed covered the period August 2017 to August 2022. The results showed increasing displacements from August 2017, along the east wall outside of the area of the collapsed walls. All subsequent interferograms showed increased displacements at the location of the collapse in the southeast corner of the dam, apart from the interferograms over the period of May and June 2021 where, according to reports, the facility was shut. The interferogram between 6th August 2022 and 18th August 2022, just days before the collapse (Figure 13) shows a part of the wall exhibiting displacement away from the satellite (red) and part of it exhibiting displacements towards the satellite (blue). The SBAS analysis was performed using a dataset of radar images covering the period September 2020 to August 2022. All images had a 12-day interval between them. The results showed increasing displacements in the southeast corner where the collapse occurred. Similarly, an area on the wall exhibiting displacements away from the satellite shown in red and towards the satellite shown in blue is apparent (Figure 14). There is a gap in the data which is likely due to the loss of coherence from the accelerated displacements at those locations. 5

Figure 14. SBAS analysis results showing increasing mean annual velocity values around the southeast corner (red indicates movements away from the satellite, blue towards the satellite). 4

DISCUSSION

This paper presents the results of a study on the collapsed diamond tailings dam in Jagersfontein, South Africa on 11th September 2022. Historical SAR data have been used to examine the behaviour of the tailings dam and whether there were signs of instability prior to its collapse. Radar imagery from the Sentinel-1 satellite were acquired and two different types of analyses were performed: single interferograms assessing the LOS displacements between successive dates and SBAS analysis, a multi-temporal stacking technique using a time series of data. The images used had a 12-day interval between them. Several single interferograms were produced between successive images, most of which were 12 days apart. The results were analysed along the tailings dam perimeter wall

CONCLUSIONS

The collapse of the Brumadinho dam on 25th January 2019 in Brazil, killing 272 people and causing an environmental and economic catastrophe has demonstrated the need to exercise greater responsibility and to ensure safer operations in the mining industry. Yet, recent events such as the Jagersfontein diamond tailings dam collapse show that there is still a lot to be done regarding the safety of these facilities. On the other hand, recent advancements in space technologies such as the availability of radar satellite data with global coverage and the increasing spatial resolution of commercial radar satellite data, cloud technology and automated systems, all provide reliable and regular monitoring methods for tailings storage facilities and other infrastructure assets in general. The results from this study showed that the warning signs of increased displacements at the exact locations where the dam collapsed were there many months before the event. Had regular DInSAR monitoring of the tailings storage facility at this location been undertaken, ample warning would have been provided to take remedial action to avoid the collapse and possibly to have avoided the collapse and resulting damage and loss of life altogether. 5.

REFERENCES

Berardino, P., Fornaro, G., Lanari, R. and Sansosti, E. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Trans. Geosci. Remote Sens., 40(11): 2375–2383. Lanari, R., Casu, F., Manzo, M., Zeni, G., Berardino, P., Manunta, M. and Pepe, A. 2007. An overview of the small baseline subset algorithm: A DInSAR technique

for surface deformation analysis. In Deformation and Gravity Change: Indicators of Isostasy, Tectonics, Volcanism, and Climate Change, 637–661. Werner, C., Wegmuller, U., Strozzi T. and Wiesmann, A. 2003. Interferometric point target analysis for deformation mapping, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 7: 4362-4364. Torres-Cruz, L. A. and O’Donovan, C. 2023. Public remotely sensed data raise concerns about history of failed Jagersfontein dam, Scientific reports, https://doi.org/10.1038/s41598-023-313633-5 Banya, N. 2022. Troubled South African tailings dam had history of high water levels. https://www.reuters.com/world/africa/troubledsouthafrican-tailings-dam-had-history-high-water-levels2022-09-12/.

Influence of Material Damping Characteristics on the Seismic Behavior of Rockfill Dams Seyyed Kazem Razavi1, Kosar Hadidi2 & Samuel Yniesta1 1Department of Civil, Geological, and Mining Engineering, Polytechnique Montréal, Montréal, QC, Canada 2Department of Civil Engineering, University of Tabriz, Tabriz, Iran ABSTRACT Multi-dimensional nonlinear ground response analyses are slowly becoming the norm in earthquake engineering practice. However, they require advanced cyclic models that can reproduce the soil’s dynamic behavior with high accuracy. In this study, a nonlinear cyclic model, developed by combining and modifying the formulations of existing nonlinear cyclic models, is introduced. The model is capable of taking any desired damping and shear modulus reduction curve as input parameters. An example of simulation is presented in which a set of reference curves is used to perform a 2D dynamic analysis to study the seismic behavior of a rockfill dam. The simulations are also performed with input curves obtained from the Masing rules to investigate the effect of material damping characteristics and investigate the limitations of Masing-type models. Results indicate that the damping curves generated by the Masing rules produce lower amplification in PGA for strong earthquakes, because Masing rule induce higher damping at large strains. Using the Masing rules and attempting to modify the shear modulus reduction curves to generate damping curves closer to the reference curve resulted in softer material, which significantly reduced the PGA amplification in the dam. 1

INTRODUCTION

Several nonlinear cyclic models implemented in 2D and 3D geotechnical earthquake engineering commercial software (Itasca 2012, ABAQUS 2011), take a normalized modulus reduction curve and a maximum shear modulus as input parameters, and use a set of rules to define the unloading/reloading path, which typically abides Masing rules (Masing 1926). However, these models have been criticized for their shortcomings, including over-prediction of damping at high cyclic shear strains and underprediction at low cyclic shear strains (Hashash et al. 2010, Numanoglu et al. 2018, Yniesta et al. 2017). To address this issue, some researchers (Dakoulas 2012, Razavi et al. 2021) have attempted to modify the normalized modulus reduction curve to achieve a more reasonable damping curve under the use of Masing rules. The curves obtained through this methodology are referred to as modified MD curves in the present study. Using these curves, Dakoulas (2012) analyzed an earth structure’s seismic behavior to design the most critical elements of dams, such as concrete face slabs, while Razavi et al. (2021) studied the seismic performance of a new plastic concrete cutoff wall as a rehabilitation method for an earth-fill dam. However, it is known that even modest changes in the modulus reduction curve can induce a significant change in the shear stress-shear strain curve of soils and a shear strength that is usually different than expected or desired (Hashash et al., 2010). Consequently, it is essential to evaluate the impact of such assumptions made using the modified MD curves on the seismic response of earth structures. The assessment should be done by using backbone and damping curves derived from experimental data, such as the curves introduced by Rollins et al. in (2020) for gravelly soils, referred to as reference curves in this study. It is also essential to ensure that the backbone

curves obtained from reference curves reach the soil's shear strength at large strains to be considered valid. Here, a nonlinear model is developed using a combination of existing nonlinear cyclic models and implemented in FLAC. The model can reproduce both modified MD curves and reference curves by choosing appropriate material properties, making it possible to analyze the effect of using different backbone and material damping characteristic curves on the seismic response of earth-fill dams. 2 2.1

A COMBINED-IMPROVED NONLINEAR CYCLIC MODEL Maximum shear modulus at small strains

The definition of Gmax can be expressed using the following equation (Richard et al. 1973). 𝐺𝑚𝑎𝑥 = 𝑃𝑎 × 𝐴

(𝐵−𝑒)2 𝜎𝑚 𝑟 ( ) 1+𝑒 𝑃𝑎

[1]

Where e and 𝜎𝑚 are the void ratio and the mean effective stress, respectively. The parameters r, A, and B are material properties, and Pa refers to the atmospheric pressure. 2.2

Backbone Curve

The backbone curve used in the modified MD curves follows equation 2 (Konder and Zelasko, 1963), whereas the backbone curve employed in the reference curves adheres to equation 3 (Yee et al. 2013).

𝜏=

𝐺𝑚𝑎𝑥 𝛾 1+(

𝛾 𝛼 ) 𝛾𝑟

[2]

𝐺𝑚𝑎𝑥 𝛾

𝛾 ≤ 𝛾1

𝛾 𝛼 𝛾𝑟

1+( )

𝜏=

𝜏1 + {

𝐺𝛾1 𝛾 1+

𝛾 𝜏 −𝜏1 ( 𝑚𝑎𝑥 𝐺𝛾1 )

𝛾 > 𝛾1

[3]

Where, 𝜏, 𝛾, 𝛾𝑟 , and 𝛼 are shear stress, shear strain, reference strain, and curve-fitting coefficient, respectively. 𝜏1 and 𝐺𝛾1 are the shear stress and shear modulus for 𝛾 = 𝛾1 obtained from equation 2. While taking 𝛼 a constant value, 𝛾𝑟 varies with the variation of 𝜎𝑚 as well as soil parameters such as over consolidation ratio (OCR), plasticity index, PI, and the coefficient of uniformity, C u. Rollins et al. (2020) proposed equation 4 to calculate 𝛾𝑟 for granular soils. 𝛾𝑟 =

0.0046(𝐶𝑢 )−0.197 (𝜎𝑚 )0.52

[4]

𝜏𝑚𝑎𝑥 in equation 3, is the shear strength of the soil and can be calculated using equation 5 for granular materials. 𝜏𝑚𝑎𝑥 = 𝜎𝑚 sin(𝜙)

[5]

Where 𝜙 is the friction angle. Equation 3 introduces a twohyperbola backbone curve modifying the hyperbola curve of equation 2 to reach 𝜏𝑚𝑎𝑥 by passing 𝛾 from 𝛾1 . Schematic form of the introduced backbone curves is shown in Figure 1. In this paper, 𝛾1 is the shear strain at which 𝜏1 = 0.75𝜏𝑚𝑎𝑥 .

𝐺𝑀 =

𝑑 𝜏𝑀 𝑑𝛾

where

𝜏𝑀 −𝜏𝑟𝑒𝑣 2

=

𝛾−𝛾𝑟𝑒𝑣 ) 2 𝛾−𝛾𝑟𝑒𝑣 𝛼

𝐺𝑚𝑎𝑥 ( 1+(

2𝛾𝑟

)

[7]

Equation 7 is the unloading/reloading path following the Masing rules, where the backbone curve is shifted to (𝛾𝑟𝑒𝑣 , 𝜏𝑟𝑒𝑣 ) and scaled by 2. (𝛾𝑟𝑒𝑣 , 𝜏𝑟𝑒𝑣 ) is a point on the backbone curve where an unloading or reloading occurs. 𝐺𝑠𝑒𝑐 is the secant shear modulus equal to 𝜏𝑟𝑒𝑣 /𝛾𝑟𝑒𝑣 . F in equation 6 is a reduction factor to modify 𝐺𝑀 to produce a new path in the unloading/reloading. Phillips and Hashash, (2009) introduced F as equation 8 to develop lower damping ratio than induced by the original Masing rules, close to measured data at large strains. 𝐹 = 𝑃1 − 𝑃2 (1 −

𝐺𝑠𝑒𝑐 𝑃3 𝐺𝑚𝑎𝑥

)

[8]

Where P1, P2, and P3 are material constants. The applicability of equation 8 has also been verified by Rollins et al. (2020) to generate damping curves that match experimental data for gravelly soils. 𝐺𝑚𝑖𝑛 in equation 6 is responsible for the minimum damping (𝐷𝑚𝑖𝑛 ) required at low strains. When 𝐷𝑚𝑖𝑛 = 0, 𝐺𝑚𝑖𝑛 = 𝐺𝑠𝑒𝑐 . Yniesta et al. (2017) introduced a shear stress-shear strain relationship for unloading/reloading path by having the damping ratio (𝐷) related to 𝜏𝑟𝑒𝑣 , and 𝛾𝑟𝑒𝑣 . If 𝐷 is equal to 𝐷𝑚𝑖𝑛 , then this relationship can be used to calculate 𝐺𝑚𝑖𝑛 . Due to the complexity of the formulation, readers are referred to the original study for details. Figure 2 shows how equation 6 changes the unloading/reloading path from the Masing rules into the non-Masing rules to produce lower damping at large strains and guarantee the presence of minimum damping at low strains.

Figure 1 Using two hyperbola approach to modify the shear stress-shear strain curve at large strains in the backbone curve Equation 3, which is utilized in the reference curves, ensures that the soil's shear strength at high strains is reached. In contrast, equation 2, employed in the modified MD curves, does not achieve this objective. 2.3

Damping curve

The shear modulus in the unloading/reloading denoted here as 𝐺𝑁𝑀 is obtained from equation 6. where subscript NM is the abbreviation for non-Masing (Phillips and Hashash, 2009). 𝐺𝑁𝑀 = 𝐹(𝐺𝑀 − 𝐺𝑠𝑒𝑐 ) + 𝐺𝑚𝑖𝑛

[6]

Here 𝐺𝑀 is the Masing shear modulus obtained from the following equation.

Figure 2 Changes of unloading/reloading path from Masing rules to non-Masing rules using equation 6 By choosing P1=1, P2=0, and 𝐷𝑚𝑖𝑛 =0, 𝐺𝑁𝑀 becomes equal to 𝐺𝑀 producing identical unloading/reloading path as the Masing rules. Based on the above explanations, the model requires the following material properties:

• • •

to define Gmax: e, r, A, B to define the backbone curve:𝛼, 𝐶𝑢 , 𝜙, 𝜎𝑚 to define damping curve: P1, P2, P3, Dmin

The presented model is implanted in FLAC 3D as a userdefined model using C++. 3 3.1

SEISMIC ANALYSIS OF ROCKFILL DAM Dam Geometry and its condition prior seismic loading

The current study performs dynamic numerical analyses of a 90-meter rockfill dam built on hard rock. The dam's geometry and finite difference mesh distribution are illustrated in Figure 3a. The dam's reservoir is assumed to be full, with a 10-meter freeboard. Initial stresses are created through the gravity loading technique before impounding the reservoir. The impoundment is then simulated by applying hydrostatic pressure on the dam's upstream side. For the static conditions, the rockfill behavior is simulated using the Mohr-Coulomb model (i.e. modeling an elastic perfectly plastic stress-strain behavior). Material properties are listed in Table 1. The friction angle and unit weight of the soil are chosen to be representative of the materials used to build earth-fill and rockfill dams, and discussed in the literature (Dakoulas 2012, Razavi et al. 2021). Other material properties are chosen to produce shear wave velocity in the range of 200 to 400 m/s. The generated 𝜎𝑚 , 𝐺𝑚𝑎𝑥 , and 𝑉𝑠 (shear wave velocity) in the dam axis are shown in Figure 3b. The element size is defined to permit the propagation of a signal of a maximum frequency of 15 Hz, by restricting the size of the elements to one-tenth to one-eighth of the wavelength (λs) (Itasca 2012). This Vs of the rockfill range from 200 to 400 m/s, resulting in a mesh size of 1.66 m at the crest and 3.33 m at the base. The hydrodynamic pressures on the dam are simulated using the added-mass technique proposed by Zangar (1952).

Figure 3 a) Geometry of the rockfill dam, b) variation of 𝜎𝑚 , 𝐺𝑚𝑎𝑥 , and 𝑉𝑠 with depth at the dam axis Table 1 material properties of the rockfill parameters

Value

A

3937.5

B

2.17

r

0.48

e

0.25

Poisson Ratio

0.3

Cohesion

0

Friction Angle

55 degrees

Unit Weight

20 kN/m3

3.2

Seismic Loading

Two input motions are used from the Loma Prieta 1989 earthquake, the LPAND270 record (station number 1652), and the LPGIL067 record (station number 47006). Their acceleration time histories normalized with PGA (peak ground acceleration) and baseline corrected are presented in Figure 4a and Figure 4b. Figure 4c and Figure 4d, present the acceleration response spectra normalized with PGA and the Fourier spectra of the records, respectively. In the present study, these motions are scaled to 0.001g, 0.01g, 0.05g, 0.1g, and 0.2g to investigate the influence of different PGAs on the seismic response of the rockfill dam. Note that the two records differ in their frequency content and duration.

Figure 4 Loma Prieta 1989 earthquake record used: a), acceleration time history of the LPAND270 record (station number 1652), b) acceleration time history of the LPGIL067 record (station number 47006), c) Normalized acceleration response spectra, d) and Fourier spectra of the input earthquakes

Figure 5 a) Normalized modulus reduction curves, and b) Damping curves obtained using the two sets of parameters presented in Table 2

3.3

3.3.1

Influence of damping curves

In this study the backbone curve of the rockfill is defined using equation 3 with the following material properties : 𝛼 = 0.84, 𝐶𝑢 = 10, 𝜙 = 55 𝑑𝑒𝑔𝑟𝑒𝑒𝑠. Figure 5a shows the normalized shear modulus reduction curve for two confining effective stresses 𝜎𝑚 = 50𝑘𝑃𝑎 and 𝜎𝑚 = 400 𝑘𝑃𝑎. For the damping curves, two different sets of material properties have been selected, and their properties are listed in Table 2. The first set of material properties, labeled Set 1, yields damping curves obtained via the unloading/reloading pathway of the Masing rules. In contrast, the second set of material properties, labeled Set 2 or reference curve, generates material damping curves that closely resemble those suggested by Rollins et al. (2020). The obtained damping curves are shown in Figure 5b. Table 2 Material damping properties considered in this study Material Damping parameters Set 1 Masing curves

Set 2 Reference curves

P1

1

0.6

P2

0

0.2

P3

0

0.9

Dmin (%)

0

1

Results

Figure 6 displays the amplification ratio, defined as the peak acceleration (PA) divided by the input peak ground acceleration (PGA), at various locations along the dam central axis, specifically at z/H=0 (crest), 0.1, 0.33, and 0.75. Here, z represents the distance from the crest to the dam's base, while H is the total height of the dam. The horizontal axis denotes different levels of PGA at which the input motions were scaled. The results demonstrate that at a low PGA of 0.001g, the amplification ratios of models utilizing the Masing rules get higher than that of nonMasing rules. As the PGA of the earthquake increases the amplification ratios decrease faster for models that use Masing rules. At higher PGA higher strains develop which correspond to the part of the damping curve where the Masing rules significantly overestimate the damping ratio. As the seismic waves are more significantly damped, their intensity decrease leading to a lower amplification.

(2020), as can be observed in the figure. However, the drawback of not considering the strength-correction on the backbone curve can be seen in Figure 7c and d, at low and high confining pressure. The higher discrepancy seen at low confining pressures is the results of the input parameters chosen.

Figure 6 Influence of Masing and Non-Masing rules on the amplification ratio (peak acceleration (PA)/peak ground acceleration (PGA)) at z/H=0, 0.1, 0.33, and 0.75 located at the dam central axis a) for LPAND270 record, b) for LPGIL067 record 3.4

Use of a modified backbone curve

This section introduces a new set of material curves known as the Modified MD curves. These curves do not consider the strength-correction presented in equation 3 but are instead defined by equation 2. The rationale for this decision is that the majority of nonlinear cyclic models available in 2D and 3D software do not offer this option to incorporate the strength-correction factor. These curves follow the Masing unloading/reloading path presented in equation 7, meaning that the MD curves take the Masing properties of set 1 in table 2. To reduce the high damping observed with the Masing rules (Figure 7b), it is required to modify the backbone curve. Here for example by taking 𝛼 = 0.68 and 𝛾𝑟 = 0.07, a sample of modified MD curves can be drawn as shown in Figure 7. In the figure the reference curves proposed by Rollins et al. (2020) are also shown. These curves are obtained using the same material properties as in the previous section. The selected modified MD curves exhibit a close match with the curves proposed by Rollins et al.

Figure 7 Reference curves versus modified MD curves a) normalized modulus reduction curves, b) damping curves, and c and d) effect of confining pressure on the shape of the backbone curves 3.4.1

Results

As depicted in Figure 7, the modified MD exhibit a nearzero damping ratio. As a result, this approach should use Rayleigh damping to introduce small strain damping. Conversely, at high strains, the curves produce a higher damping ratio. The reduction in stiffness also differs from the lab data and differs at low and high confining pressure. As a consequence of the previous observations, lower amplification values are generally observed when compared to the reference curves. Figure 8 presents the amplification ratio (peak acceleration (PA)/peak ground acceleration (PGA)) at the same locations as previously discussed. However, it should be noted that for the LPGIL067 record the opposite results are observed.

rockfill dam. The following conclusions were made as the results of this study: • •

5.

When the same backbone curve is used, the nonlinear cyclic model with higher damping ratio yields lower PGA in the rockfill dam. On the other hand, when the backbone curve is modified to have lower damping ratio using Masing rules, the material can become softer and result in reduced PGA. However, the modified curves still maintain high damping ratios at large strains, resulting in lower amplification ratios for strong input motions.

REFERENCES

ABAQUS, 2011, Users’ Manual, Simulia, Pawtucket, Rhode Island. Dakoulas, P., 2012. Nonlinear seismic response of tall concrete-faced rockfill dams in narrow canyons. Soil Dynamics and Earthquake Engineering, 34(1), pp.11-24. Hashash, Y.M., Phillips, C. and Groholski, D.R., 2010, May. Recent advances in non-linear site response analysis. In 5th International Conference on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics (No. 4).

Figure 8 Influence of different approach in the selection of the backbone and damping curves on the amplification ratios (peak acceleration (PA)/peak ground acceleration (PGA)) at z/H=0, 0.1, 0.33, and 0.75 located at the dam central axis for LPAND270 record 4

CONCLUSION

In the present study, a nonlinear cyclic soil model is introduced by combining different approaches from previously published models. The backbone curve is derived from the hyperbolic equation proposed by Konder and Zelasko (1963), with an additional modification suggested by Yee et al. (2013) to reach true shear strength at large strains. The minimum damping is incorporated using the ARCS method introduced by Yniesta et al. (2017), while the remaining damping adheres to the rules established by Phillips and Hashash (2009). As a result, this model combined several features of the previous models and achieve the following goals: (1) no need for viscous damping (Rayleigh damping) at low strains using the coordinate transformation approach of the ARCS model. (2) more accurate damping using non-Masing rules. (3) strength matching at large strains using two hyperbolic curves for backbone curve. (4) confining pressuredependent curves. The model also has a flexible formulation and the behavior of the nonlinear cyclic models on which it is based can be retrieved by choosing the right input parameters. Using this feature, a parametric study has been done to compare the effect of improvements obtained using the new model on the seismic behavior of a

Itasca Consulting Group, Inc., 2012. FLAC3D - Fast Lagrangian Analysis of Continua in Three-Dimensions, Ver. 5.0. Software Manual. Minneapolis. Kondner, R.L. and Zelasko, J. S., 1963. A hyperbolic stressstrain formulation for sands. In Proc. 2nd PanAmerican Conf. on SMFE, Sao Paulo, Brazil (Vol. 1, pp. 289-324). MASING, G., 1926. Eigenspannungen und verfestigung beim messing. In Proceedings, second international congress of applied mechanics (pp. 332-335). Numanoglu, O.A., Musgrove, M., Harmon, J.A. and Hashash, Y.M., 2018. Generalized non-Masing hysteresis model for cyclic loading. Journal of Geotechnical and Geoenvironmental Engineering, 144(1), p.06017015. Phillips, C. and Hashash, Y.M., 2009. Damping formulation for nonlinear 1D site response analyses. Soil Dynamics and Earthquake Engineering, 29(7), pp.1143-1158. Razavi, S.K., Hajialilue-Bonab, M. and Pak, A., 2021. Design of a Plastic Concrete Cutoff Wall as a Remediation Plan for an Earth-Fill Dam Subjected to an Internal Erosion. International Journal of Geomechanics, 21(5), p.04021061. Richart, F.E., Hall, J.R. and Woods, R.D., 1970. Vibrations of soils and foundations. Rollins, K.M., Singh, M. and Roy, J., 2020. Simplified Equations for Shear-Modulus Degradation and Damping of

Gravels. Journal of Geotechnical and Geoenvironmental Engineering, 146(9), p.04020076. Yee, E., Stewart, J.P. and Tokimatsu, K., 2013. Elastic and large-strain nonlinear seismic site response from analysis of vertical array recordings. Journal of Geotechnical and Geoenvironmental Engineering, 139(10), pp.1789-1801. Yniesta, S., Brandenberg, S.J. and Shafiee, A., 2017. ARCS: A one dimensional nonlinear soil model for ground response analysis. Soil Dynamics and Earthquake Engineering, 102, pp.75-85. Zangar, C. N. 1952. Hydrodynamic pressures on dams due to horizontal earthquake effects. No. 11. Denver: Technical Information Office.

Application of Aggregate Filled Shipping Containers as a Temporary Retaining Wall Kun Yang, Khuram Mohib & Tom Sabourin Kiewit Engineering Group Canada ULC, Oakville, Ontario, Canada ABSTRACT A temporary retaining wall consisting of two levels of stacked intermodal shipping containers was constructed to provide a working platform within the downstream bays of concrete sluiceways at a hydroelectric station. Filled with loose sand and gravel, the containers functioned as a temporary retaining wall to support a working platform for construction equipment, including a 300t crawler crane. The wall was 5 meters high, retaining 6.3 meters of geogrid-reinforced backfill behind it. The container wall was monitored for movement during its service life of about one year. Significant initial movements were observed after commissioning that stabilized over a one-month period. Two-dimensional numerical modelling in Plaxis was used to understand wall movement responses. The successful application of the filled container wall demonstrates a cost-effective solution compared with traditional retaining structures for temporary construction works. RÉSUMÉ Un mur de soutènement temporaire composé de deux niveaux de conteneurs d'expédition intermodaux empilés a été construit pour fournir une plateforme de travail dans les baies en aval des écluses en béton d'une centrale hydroélectrique. Remplis de sable et de gravier meuble, les conteneurs se comportent comme un mur de soutènement temporaire pour soutenir une plateforme de travail pour les équipements de construction, dont une grue sur chenilles de 300t. Le mur mesurait 5 mètres de haut et retenait 6,3 mètres de remblai renforcé par une géogrille. Le mouvement de la paroi du conteneur a été monitoré pendant sa durée de vie d'environ un an. Des mouvements initiaux importants ont été observés après la mise en service et ceux-ci se sont stabilisés sur une période d'un mois. La modélisation numérique bidimensionnelle dans Plaxis a été utilisée pour comprendre le comportement des parois sous l’effet des mouvements. L'utilisation du système de mur de conteneurs rempli de remblai s'est avérée être une réussite et démontre que ce système est une solution rentable par rapport aux structures de soutènement traditionnelles pour les travaux de construction temporaires. 1

BACKGROUND

Temporary structures are often required to build permanent structures and facilitate the movement and operation of equipment and personnel during the construction of permanent structures. The design of these temporary structures and devices has a vital role in the execution of the construction of permanent structures as they are used to either temporarily support the permanent structure or provide the necessary working surface for equipment and personnel (Jin and Gambatese 2020). Their design is presented with different challenges than permanent structures, such as short design life, limitation of available material and space and improvised loading conditions. The same basic engineering principles are used in temporary engineering, though some design considerations, such as earthquake loading, may be omitted due to the temporary nature and factors of safety (FOS) may be relaxed in certain cases considering the short duration for which these structures will be in service. A temporary retaining wall built with shipping containers filled with granular material was used in the downstream spillway structure of a dam (flip bucket spillway). The retaining wall facilitated an access platform to allow concrete rehab and anchor installation across the downstream face of the spillway structure. Intermodal shipping containers (SCs), colloquially referred to as “sea cans” or CONEX boxes, have the advantage of relatively low cost, widespread availability and ease of handling and

transport for use in temporary structures for construction projects. The wall was constructed to provide a temporary working platform for the operation of cranes and other equipment. 2

LITERATURE REVIEW

Temporary structure may be defined as structures built exclusively for the purpose of supporting the construction of permanent structures. Engineering for temporary structures covers a wide range of applications, such as earthwork and shoring, formwork, falsework and access roads or ramps (Ratay 2004; Yuan and Anumba 2016; Jin and Gambatese 2020). There has traditionally been an assumption of greater acceptable risk in temporary systems in the construction industry. However, this has resulted in the continuing occurrence of temporary structure failures due to flawed design (Haduong et al. 2018; Lew 1984). This situation has led to a growing amount of research and attention towards the design and execution of temporary structures. Though there still is ample opportunity for additional research and application of new technologies, the construction industry holds an overall welcoming attitude towards improving the design quality of temporary structures (Jin and Gambatese 2020). There have also been efforts to standardize the design practices of temporary structures design, such as the American Society of Civil Engineers [ASCE] standard of

Design Loads on Structures during Construction (ASCE/SEI 37-14) (ASCE 2014). In general, though having shorter design service life, temporary structures are required to employ the same level of design robustness for the elevated consequences in case of failure. The FOS regarding strength are often identical to those of permanent structures (Gaba et al. 2002; Beale and André 2017). One notable exception is seismic load, which is not usually considered in temporary design unless deemed necessary in specific application (ASCE 2014; Kojima et al. 2019). A retaining wall refers to a gravity structure to restrain the movement of soil by resisting active earth pressures (Fenton et al. 2015). Retaining walls constructed for a temporary service life are a significant component of temporary structures (Yuan and Anumba 2016). The study of temporary retaining wall practices in existing literature primarily focuses on the risk assessment and mitigation (Fok et al. 2014) as well as failures of conventional soil retaining methods, such as sheet piles, soldier piles and caissons (Liew 2008; Lim and Rahardjo 2018). An example of innovation in temporary retaining wall practice is the use of bamboo mats in lieu of geosynthetic in a mechanically stabilized temporary retaining wall application by Sasmayaputra et al. (2017), using more sustainable locally available material for the temporary construction in a way similar to the container retaining wall in this study. In current practice, there has also been a wide variety of construction applications using standard intermodal shipping containers. The majority of documented usage in literature are housing units primarily in developing countries or as temporary housing after disaster events (Giriunas et al. 2012; Bernardo et al. 2013, Zhang et al. 2014; Islam et al. 2016; Madkour 2017). The use of SCs as building material has the advantages of significantly less embodied energy (Vijayalaxmi 2010), cost-effectiveness and reusability (Uittenbroek and Macht 2009). However, documented literature regarding SCs are largely confined within the housing usage context, and the use of filled SCs as temporary retaining walls is limited in existing literature. Therefore, this study seeks to document a design approach, construction methodology and implementation of the usage of SCs as a temporary retaining wall. The case history of this application may provide insight for future construction scenarios where the utilization of a filled SC temporary wall may be favourable. 3 3.1

DESIGN AND CONSTRUCTION PROCESS Retaining Wall Design

As part of the refurbishment of an existing sluiceway structure, a working platform for cranes, drill rigs, excavators, and dump trucks were required to be placed within an existing downstream sluiceway. The required working pad was located inside the central bays of the sluiceways, separated from the rest of the bays by concrete training walls on either side. The proposed retaining wall was located over the curved concrete surface separated by the training walls, known as the flip bucket structure. The adjacent active spillway bays maintained a downstream water flow directly beyond the end of the flip bucket. This

situation, considering the highly variable water level, and the hard bedrock foundation made the installation of more conventional temporary retaining structures, i.e., cofferdam, using steel sheet piles or a concrete secant pile wall difficult to construct and very time consuming. Moreover, the placement of steel or concrete material inside the downstream channel would lead to complicated environmental concerns, rendering traditional solutions unfavourable. After extensive brainstorming with the construction team, it was proposed to build the temporary pad retained by shipping containers. The advantages of ease of construction, availability of materials and ease of future removal were quickly favoured by the engineering team. The design consists of 6.1 m (20 ft) and 12.2 m (40 ft) SCs stacked two levels high. With a standard container height of 2.59 m, two levels produced a wall height is 5.18 m, and a 1.1m back slope behind the back face of the wall brought the working surface level up to about 6.3 m above the base of the SCs; in order to accommodate post-tensioned anchor drilling for the sluiceway structure. At the base of the wall, framed steel panels were set below the lower row of containers and welded to the base of the containers so that backfill material on these steel panels would exert additional gravity loading on the container wall system, increasing the wall’s overturning capacity (Figure 1). The SC retaining wall considers the same gravity retaining wall design principles used in permanent retaining wall design. The wall was designed for overturning, bearing capacity and eccentricity. Sliding was not considered to be a concern because the corner of the bottom level of the containers was fitted snuggly at the upward-curving concrete flip bucket lip. Minimum overturning factor of safety of 2.0 was considered and eccentricity was considered acceptable if the force resultant fell within the middle 1/3 of the base of the wall. These considerations are in line with conventional safety factors for permanent design. However, the minimum bearing capacity FOS was 2.0 instead of 3.0 as commonly used in permanent design (Das 2011), in accordance with the established practice for temporary structures and short-term use. 3.2

Backfill Properties

Locally available crusher run material was used as backfill material behind the wall and as infill material for the containers. The material comprised of primarily sand and gravel was largely cohesionless. The infill material placed inside the containers was not compacted whereas the backfill behind the wall was track packed in lifts of 0.3 m using excavators. See table below for material properties considered in design. Table 1. Geotechnical Properties of Backfill Unit weight (γ)

20 kN/m3

Friction angle (φ’)

38 °

Undrained shear strength (Su)

0 kPa

Figure 1. Plan, elevation, and section views of the shipping container retaining wall

3.3

Construction Process

Before their placement, the SCs were fitted with steel tension ties in the middle of the side wall of the containers to provide additional stiffness and resistance against bending and buckling of the light steel container walls. The thread bar tension ties were fixed into a horizontal wale beam on both sides of the container. In addition, the top surfaces of the containers were cut with large openings to allow the placement of infill material after each level of containers were put in place to form the wall. The construction of the wall began with the placement and compaction of crusher run material at the intended wall bottom elevation in the flip bucket depression of the existing downstream concrete structure. Framed steel panels, which were designed to provide additional resistance to overturning, were placed on the prepared surface before lowering of the first row of containers on top

a. Placement of the bottom steel panels and the first row of containers

of these panels. The panels were welded to the SCs and the SCs were then filled with the crusher run infill. Once the material was placed inside the containers for the first level, the gaps between the containers, and between the containers and the concrete training walls on both sides, were sealed with urethane foam for water tightness. The top openings of the SCs were also covered with plywood panels and sealed with urethane. After the installation and filling of the first row of SCs was complete, backfilling with geogrid reinforcement and compacted granular fill behind the wall commenced. The bottom two lifts of geogrids were placed with 0.6 m vertical spacing while the vertical spacing between the layers of geogrids was 1.2 m for the rest of the wall. Nevertheless, the lift thickness for soil compaction was 0.3 m. After backfilling to the top of the first row of SCs, the second row was completed using the same procedure (Figure 2).

b. Backfilling and geogrid placement against the first row of containers

c. Filling of the first row and placement of the second row d. Completion of the shipping container wall of containers Figure 2. Photos of construction stages of the shipping container wall

4

NUMERICAL MODELLING

To validate the SC retaining wall design obtained through the traditional analytical approach as described above, a two-dimensional finite element model in Plaxis 2D was employed to predict the wall’s behaviour under loading. The material constitutive model chosen for the crusher run backfill material was the hardening soil model. The container walls and steel ties placed across the middle of the containers were modelled as one-dimensional plates with strength properties such as axial and bending stiffness assigned to the plate elements. The finite element model parameters are shown in Table 2. Table 2. Material properties considered in finite element method (FEM) analysis Crusher run material E501

24 MPa

EOED1

24 MPa

Eur1

72 MPa Container wall

E

200 GPa

Cross sectional area of wall element2

1.89 × 10-3 m2

Moment of inertia of wall element

3.64 × 10-7 m4

Unit length of wall element

2

2

1.019 m

EA per m

371 × 103 kN/m

EI per m

71 kNm2/m Wall base steel panel

Cross sectional area, per m

3.4 mm2/m

Moment of inertia, per m

5.37 × 10-9 m4/m

EA per m EI per m

680 × 103 kN.m 1.07 kNm2/m

Steel tension tie E

200 GPa

Diameter

0.0191 m

Obrzud and Truty (2012) 2 Bernardo et al. (2013) 1

4.1

Hardening Soil Parameters

Typical sandy or gravelly material with secant modulus values from Obrzud and Truty (2012) were considered in the model. The Plaxis analysis staging considered each lift of geogrid installation during the construction of the wall, as well as the entry of crawler crane loading into the working pad area. Additional stages were applied when analyzing temporary conditions imposed on the working pad after its construction and introduction of the crawler crane into the workspace. For example, a temporary elevated working pad composed of additional crusher run material for demolition of certain existing sluiceway structures. The resultant displacements were compared with those obtained in initial crane analysis and such temporary conditions would be considered acceptable if the additional deflections induced were not significant. 4.2

Model prediction

The model predicted a moderate amount of deflection of the outside SC wall after the application of loading, about 59 mm. However, the actual movement of the container wall greatly exceeded this value, as explained in the next section. See Figure 3 for the Plaxis 2D finite element model.

a. Plaxis 2D model of shipping container working pad, prior to crane loading

b. Plaxis 2D model of shipping container working pad with crane loading, deformation exaggerated 10x

c. Predicted horizontal displacements of the downstream face of the container wall Figure 3. Finite element model of shipping container wall in Plaxis 2D

5 5.1

WALL PERFORMANCE Monitoring efforts

To monitor and understand the behavior, survey monitoring was set up during the construction and service life of this temporary retaining wall and working pad. Monitoring techniques included survey prisms installed in the top centre of each SC, as well as visual inspection using drones. Survey readings were taken twice daily during the initial month, then reduced to once daily in the subsequent months, before further reducing to twice then once per week after initial movements were stabilized. 5.2

Monitoring Observations

During the initial 48 hours after the completion of the backfilling to the working pad elevation as specified in the design drawing, visible deformation in the 40-ft container of the top level was observed. For the next 14 days movements continued to grow, but the rate of change was slowed. The underestimate of the initial deflections by the 2D Plaxis Modeling resulted from the simplifications selected for modeling of structural components of the shipping containers. Based on structural assessment of the framing and construction of the SCs, it is clear that a significant component of the racking strength of the SCs results from the end frames, and that mid length deflections will substantially exceed average deflections under lateral loading. The understanding of this additional deflection is explained by 3D structural modelling which is not presented herein. 5.3

Discussion of shipping container wall behaviour

The field measurements of the SC wall displacement, taken from sensors installed in the top centre of each container, were found to be larger than the maximum displacement of 59 mm predicted by initial simplified 2D modeling. See Figure 4 for measured displacements on the top row of the SCs. Displacement curves labelled u_FB1 to u_FB6 refer to survey monitoring results from the survey prisms located on the top of each of the six SC of the top row. The under-prediction of the 2D modeling can be explained by 3D modelling which details the structural components of the shipping containers. The SC wall stabilized without significant ongoing movements as crane operation on the working pad became routine. Such stabilization allowed crane lift work planning without continual oversight from the designer. The SC wall and working pad is due to be removed in a few weeks’ time from the writing of this document, as the required work in the sluiceway zone approaches its end. As the wall is decommissioned, the steel shipping containers, steel ties, wales and base panels will be inspected in detail to further refine the understanding of the performance of the system.

Load testing

After initial stabilization of the displacement readings, a field load testing program was performed before putting the SC wall into service. A large excavator was positioned with its track parallel to the back of the wall. The bucket of the excavator was used to lift the far track off the ground transferring more load to the near track of the excavator. By specifically selecting the offset of the track to the wall, this approach was able to reasonably mimic the lateral surcharge pressure that would be produced by the larger cranes at further offset. The 300t crawler crane was tracked onto the working pad after the load test. Hours after the entrance of the crane, another survey shot was taken, and little additional deflection was observed. From that point on ward, the crawler crane was free to move back and forth in its intended operational zone on the working pad. 5.4

5.5

Stabilization of deflections

After the first days of deflection, further readings showed a stabilizing trend. Full stabilization of the displacement trend was achieved after about 60 days.

Figure 4. Surveyed displacements for top row containers, compared with initial predictions from 2D FEM 6

CONCLUSION

Despite under-prediction of deformations with 2D modeling, the shipping container wall stabilized in its lateral deflections and proved to be an effective retaining mechanism for a work platform for construction activities downstream of the sluiceway structure. The successful application of the wall using shipping containers may service as a proof of concept for soil retaining structures of a temporary service life. Materials, energy and cost may be conserved by placing filled containers instead of more traditional retaining structures such as sheet piles or concrete caissons. The initial excessive defections can be attributed to the 3D structural response of the containers.

7

REFERENCES

American Society of Civil Engineers [ASCE]. 2014 Design Loads on Structures during Construction ASCE/SEI 3714, American Society of Civil Engineers, Reston, VA, USA. Bernardo, L.F., Oliveira, L.A., Nepomuceno, M.C., and Andrade, J.M. 2013. Use of refurbished shipping containers for the construction of housing buildings: details for the structural project, Journal of civil engineering and management, 19(5), 628-646. Beale, R. and André, J. 2017. Design solutions and innovations in temporary structures, 1st ed., IGI Global, Hershey, PA, USA. Das, B.M. 2011. Principles of Foundation Engineering, 7th ed., Cengage Learning, Stamford, CT, USA. Fenton, G.A., Griffiths, D. V., and Williams, M. B. 2005. Reliability of traditional retaining wall design, Geotechnique, 55(1), 55-62. Gaba, A. R., Simpson, B., Beadman, D. R., and Powrie, W. 2002. Embedded retaining walls: guidance for economic design, Funders Report FR/CP/96, 2002, Construction Industry Research and Information Association, London, UK. Giriunas, K., Sezen, H., and Dupaix, R. B. 2012. Evaluation, modeling, and analysis of shipping container building structures, Engineering Structures, 43, 48-57. Haduong, A., Kim, J. J., and Balali, V. 2018. Statistical results on incidents for formwork safety in concrete structures, Construction Research Congress 2018: Safety and Disaster Management, ASCE, New Orleans, LA, USA, 645-655. Islam, H., Zhang, G., Setunge, S., and Bhuiyan, M. A. 2016. Life cycle assessment of shipping container home: A sustainable construction, Energy and buildings, 128, 673-685. Jin, Z., and Gambatese, J. 2020. Exploring the potential of technological innovations for temporary structures: a survey study, Journal of Construction Engineering and Management, 146(6), 04020049. Kojima, K., Suzuki, A., Yazaki, S., Honda, M., and Nishiyama, S. 2019. Seismic behavior of temporary retaining wall structures, in Earthquake Geotechnical Engineering for Protection and Development of Environment and Constructions – Silvestri, F. and Moraci, N. (Eds),1st ed. 3397-3402, CRC Press, Boca Raton, FL, USA. Lew, H.S. 1984. Construction Failures and their Lessons, Batiment International, Building Research and Practice, 12:5, 272-275. Liew, S. S. 2008. Case Studies of Support of Open Excavations and Distressed Retaining Walls in Malaysia, Seminar on Deep Excavation and Retaining Walls, The Institution of Engineers Malaysia [IEM] and The Hong Kong Institution of Engineers [HKIE], Petaling Jaya, Malaysia, Vol. 24. Lim, A., and Rahardjo, P. P. 2018. Lesson Learned from retaining wall failures: a geotechnical disaster, International Conference on Disaster Management 2018, Andalas University and Indonesian Disaster Expert Association, Padang, Indonesia.

Madkour, M. 2017. Shipping containers as an approach to increase the quality of economic sustainable buildings in Egypt, 1st International Conference on Towards a Better Quality of Life, Housing & Building National Research Center [HRBC] and Technische Universitat Berlin Campus El GOUNA [TUBCG], El Guona, Egypt. Obrzud R. and Truty, A. 2012. The Hardening Soil Model – A Practical Guidebook, Z_Soil.PC 100701 report, Zace Services Ltd, Software engineering, Préverenges, Switzerland. Ratay, R. T. 2004. Temporary structures in construction– USA practices, Structural engineering international, 14(4), 292-295. Sasmayaputra, N. A., Adi, A. D., and Faris, F. 2017. Bamboo mat as a temporary reinforced soil retaining wall in a railway bed, International Conference on Technology and Vocational Teachers (ICTVT 2017), Atlantis Press, Yogyakarta, Indonesia, 72-77. Uittenbroek, C., and Macht, W. 2009. Sustainable containers: cost-effective student housing, Quarterly & Urban Development Journal, 4th Quarter 2009, 53-60. Vijayalaxmi, J. 2010. Concept of overall thermal transfer value (OTTV) in design of building envelope to achieve energy efficiency, International Journal of Thermal and Environmental Engineering, 1(2), 75-80. Yuan, X., and Anumba, C. J, S. 2020. Cyber-physical systems for temporary structures monitoring, in Cyberphysical systems in the built environment – Anumba, C.J. and Roofigari-Esfahan, N. (Eds),1st ed. 107-138, Springer, Cham, Switzerland. Zhang, G., Setunge, S., and van Elmpt, S. 2014. Using shipping containers to provide temporary housing in post-disaster recovery: Social case studies, Procedia Economics and Finance, 18, 618-625.

Wednesday, October 4, 2023

GEOENVIRONMENTAL III

Assessing the Impact of Hydraulic Hysteresis with Air Entrapment Consideration on Water and Salt Transport in the Vadose Zone Moamenbellah Moustafa, and Rashid Bashir Department of Civil Engineering – York University, Toronto, Ontario, Canada ABSTRACT Brine-produced in oilfields is a major source of saline water, leading to the contamination of soil and groundwater. The interaction of meteoric water and atmospheric evaporative energy with the unsaturated soils controls the water balance at the ground surface, hence dictating the water movement and salt/solute transport in the subsurface. Considering that unsaturated soils typically undergo frequent cyclic drying and wetting events, hydraulic hysteresis, including air entrapment consideration, plays an important role in the movement of water and solutes within the vadose zone. In most instances, hysteresis in hydraulic functions is ignored, and land-climate interaction analysis is carried out, solely by representation of soil water characteristics by either the initial or the main drainage curve, even to represent intermittent cycles of climatic drying and wetting. This research utilizes a variably saturated flow and transport model to study the impact of hydraulic hysteresis on the salt transport in the subsurface using multiyear climate data for varying climatic conditions across the province of Alberta. The hysteretic soil water characteristic curves used for assessment also consider the air entrapment, which is usually excluded in hysteretic analysis. The results show that the inclusion of hysteresis in the land-climate interaction analysis, results in dissimilar solute distributions in the subsurface when compared with non-hysteretic analysis. Furthermore, the relative impact scale of hysteresis on the solute transport is the function of climate type and degree of air entrapment. In addition, the results also show that excluding air entrapment in hysteretic analysis underestimates the advective flux of the solute and can potentially lead to improper assessment of the fate of solute. RÉSUMÉ La saumure produite dans les champs pétrolifères est une source majeure d'eau salée, entraînant la contamination des sols et des eaux souterraines. L'interaction de l'eau météorique et de l'énergie d'évaporation atmosphérique avec les sols non saturés contrôle l'équilibre hydrique à la surface du sol, dictant ainsi le mouvement de l'eau et le transport sel/soluté dans le sous-sol. Considérant que les sols non saturés subissent généralement des événements cycliques fréquents de séchage et de mouillage, l'hystérésis hydraulique, y compris la prise en compte du piégeage d'air, joue un rôle important dans le mouvement de l'eau et des solutés dans la zone vadose. Dans la plupart des cas, l'hystérésis des fonctions hydrauliques est ignorée et l'analyse de l'interaction terre-climat est effectuée uniquement par la représentation des caractéristiques de l'eau du sol par la courbe de drainage initiale ou principale, même pour représenter des cycles intermittents de séchage et d'humidification climatiques. Cette recherche utilise un modèle de flux et de transport à saturation variable pour étudier l'impact de l'hystérésis hydraulique sur le transport du sel dans le sous-sol à l'aide de données climatiques pluriannuelles pour des conditions climatiques variables dans la province de l'Alberta. Les courbes caractéristiques hystérétiques de l'eau du sol utilisées pour l'évaluation tiennent également compte de l'emprisonnement d'air, qui est généralement exclu dans l'analyse hystérétique. Les résultats montrent que l'inclusion de l'hystérésis dans l'analyse de l'interaction terre-climat entraîne des distributions de soluté dissemblables dans le sous-sol par rapport à l'analyse non hystérétique. De plus, l'échelle d'impact relative de l'hystérésis sur le transport des solutés est fonction du type de climat et du degré de piégeage d'air. De plus, les résultats montrent également que l'exclusion du piégeage d'air dans l'analyse hystérétique sous-estime le flux d'advection du soluté et peut potentiellement conduire à une mauvaise évaluation du devenir du soluté. 1

INTRODUCTION

The vadose zone is a vital part of the hydrological cycle. It plays a crucial role in the movement of water, nutrients, and contaminants through the subsurface. However, it is particularly vulnerable to anthropogenic activities, which have the potential to cause contamination in both surface and subsurface environments (Mitchell and Mayer 1998). For example, extraction of natural oil and gas in the Great Plains Area of Canada has been operating since the mid20th century (Robertson and Geol 2006, Bashir and Pastora Chevez 2018), and such activity can result in the release (i.e., spills of) of drilling fluids and water with high salt concentrations, posing adverse potential risk of

contamination source in the vadose zone. Furthermore, since groundwater is directly linked to the vadose zone, it is also susceptible to contamination. In general, the contamination of groundwater poses a significant risk to both the environment and public health, making it a critical concern in countries such as Canada where groundwater supplies approximately 30% of the domestic water demand (Rutherford 2004). The movement and fate of solutes in the vadose zone and groundwater are determined though interaction of the solutes' chemical properties and the physical properties of the soil. Moreover, their transport is strongly influenced by unsaturated flow since the majority of soils under field conditions are mostly found in an unsaturated state.

In recent times, there has been a growing emphasis on the use of numerical modeling to investigate the interactions between unsaturated soils and intermittent atmospheric boundary conditions, such as precipitation and evaporative energy. This approach has gained significant attention as a focal point for studying these complex interactions and the resulting hydraulic response of the soil systems. In addition, extensive efforts have been made since the second half of the 20th century to assess variably saturated flow and solute transport outcomes using hydraulic hysteresis in numerical modeling (Pickens and Gillham 1980, Kool and Parker 1987, Parker and Lenhard 1987, Russo et al. 1989, Lenhard et al. 1991, Vereecken et al. 1995, Mitchell and Mayer 1998). These studies are crucial for understanding Geotechnical/Geoenvironmental soil-system designs. By exploring the coupling effect of hysteresis and solute transport, they illuminate the resulting hydraulic response. Knowledge of hydraulic responses in the vadose zone is particularly important as it serves as a buffer, attenuating water flow and solute migration to prevent potential groundwater contamination (Bashir and Pastora Chevez 2018). To understand the hydraulic behavior of unsaturated soils, it is necessary to establish the relationship between soil suction (ψ) and volumetric water content (θ), which is commonly referred to as the Soil Water Characteristic Curve (SWCC). The SWCC describes how the soil's water content changes with varying soil suction under unsaturated conditions. SWCC can also be used to estimate the Hydraulic Conductivity Function (𝑘(𝜓)); that is the hydraulic conductivity as a function of θ or ψ (Parker and Lenhard 1987, Pham et al. 2005). It is important to note that the SWCC exhibits hysteresis, resulting in the formation of distinct drying and wetting curves when subjected to repeated drying and wetting events, respectively. Figure 1 shows different paths/curves within the SWCCs hysteresis loop. It comprises of an initial drying curve, main drying curve, and main wetting curve. The hysteresis loop also includes infinite orders of scanning curves which depend on the saturation path and the history of saturation reversals. For performing numerical modelling of water flow, most studies assume a non-hysteretic SWCC, meaning that soil’s hydraulic characteristic is represented by only one branch of the SWCC, which is either the drying or the wetting curve, to represent both drying and wetting events. To comprehend the implications of hysteresis on the SWCC, at any given suction, the water content in the wetting path is always lower than the water content from the drying path, as shown in Figure 1, except at residual conditions and at full saturation (i.e., no entrapped air) where both water contents converge. Differences in water content between drying and wetting paths are believed to be caused by the nonuniformity of the void passages, also known as the ink-bottle effect, contact angle variation between advancing and receding water front menisci, air entrapment, where the volume of trapped air changes depending on the path and history of reversals, and aging caused by wetting and drying history (Haines 1930, Klausner 1991, Pham et al. 2005).

Figure 1. Typical SWCC with common terminologies of the hysteretic curves [edited from Pham et al. (2005)] In most studies, the SWCC is commonly assumed to be non-hysteretic when carrying out numerical modeling of variably saturated water flow and solute transport (Kool and Parker 1987, Bashir et al. 2015). Royer and Vachaud (1975) have explained that one rationale for ignoring hysteresis is the local hysteretic impacts being overwhelmed by the spatial variability of the hydraulic properties. In contrary to this rationale, evidence by Stauffer and Dracos (1984), indicates a fast response of groundwater recharge when hysteresis was taken into consideration. Kool and Parker (1987) have argued that disregarding hysteresis in most cases, is not based on the knowledge that hysteresis effects are negligible, but rather due to the lack of data required for the calibration of hysteresis model. In scenarios where soil undergoes either wetting or drying, assuming a non-hysteretic SWCC might be deemed acceptable, as the water flow is monotonic. However, it is important to note that such scenarios are infrequent in real-world field conditions. Soils typically interact with the natural climate, which is characterized by intermittent cycles of evaporation and precipitation. As a consequence, the soil experiences frequent cycles of wetting and drying. This, in turn, gives rise to transient and non-monotonic water flow conditions (Russo et al. 1989, Bashir et al. 2015). Even in the absence of considering the influence of external climate, the soil domain undergoing monotonic water flow can still exhibit a hysteretic hydraulic response. This is because different regions within the domain may experience either wetting or drying simultaneously. For instance, in the case of gravity drainage, the upper part of the soil undergoes drainage, which corresponds to the drying branch of the SWCC. At the same time, the lower part of the soil becomes wet, which aligns with the wetting curve of the SWCC. This clearly indicates that hysteresis within the SWCC manifests even in a simple scenario of monotonic water flow, highlighting its significance in understanding unsaturated soil behavior. Therefore, it is imperative to consider hysteresis when analyzing such situations. This paper explores the effect of hydraulic hysteresis, in the SWCC, on the water balance at the ground surface

and the water flow within the domain. The transport and fate of salt as affected by the hysteretic flow conditions is also studied. Numerical modelling was used to carry out Land-Climate Interaction (LCI) analysis for variably saturated flow conditions. The analysis was conducted on clayey soil. Air entrapment was also considered as part of the hysteresis model, and its effect was compared to analysis without the air entrapment. In addition, variation in the degree of air entrapment was also studied. The effect of hysteresis on the unsaturated hydraulic conductivity function was taken into consideration by relating it to the SWCC. This study was carried out for two different climate types, i.e. arid and dry subhumid. This paper emphasizes the importance of incorporating hysteresis with air entrapment in assessing transport processes and solute fate in the subsurface. It aims to highlight the significance of utilizing hydraulic hysteresis with air entrapment in numerical modeling of water flow and solute transport, enabling, potentially, more accurate practices for better contamination mitigation management.

To solve the above-mentioned equation, soil-water characteristic curve (SWCC) and unsaturated hydraulic conductivity function (HCF) are required. The van Genuchten (1980) function (Eq.2) was used for the mathematical representation of the SWCC. The HFC was estimated using equation 3, by using the parameters from SWCC and the saturated hydraulic conductivity.

2

where θs is the saturated water content (L3/L3), θr is the residual water content (L3/L3), Se is the effective saturation (Eq.4), and α (1/L), n (-), and m = 1-1/n (-) are empirical curve fitting parameters from equation 2. For solute transport, HYDRUS-1D solves the singlephase advection-dispersion PDE for the solutes transport in a variably saturated media. The 1-D advectivedispersive PDE for the transport of the inert and nonadsorbing solutes can be expressed as (Šimůnek and van Genuchten 1995):

SCOPE OF THE RESEARCH

This paper extends the work of Bashir and Pastora Chevez (2018), to include hysteresis in the simulations. Their efforts involved numerical modelling of a one-dimensional (1-D) variably saturated water flow and salt transport within a clay profile. They simulated atmospheric boundary conditions (BC) using daily climate data of a number of locations across Alberta, Canada. The current research applies the atmospheric BC with climate data from Bighorn Dam and Calgary, Alberta. Hysteresis was considered in the SWCC with the inclusion of air entrapment. The following sections describe the Finite Element Model (FEM) used in this research. 3

THEORETICAL BACKGROUND

3.1

Software

HYDRUS-1D was used in the current research, which is a software package capable of simulating 1-D water and heat flow, and the transport of multiple solutes in variably saturated medium (J. Šimůnek, M. Šejna, H. Saito, M. Sakai, and M. Th. van Genuchten 2013). The FEM based program, numerically solves a modified form of Richards’ partial differential equation (PDE), for variably saturated flow (Richards 1931). The governing differential equation for vertical 1-D domain can be expressed as follows:

(𝜃 − 𝜃 )

𝑠 𝑟 𝜃 = [1+(|𝛼 + 𝜃𝑟 ψ|𝑛 )]𝑚

1 𝑚

=

𝜕 𝜕ψ [𝑘(ψ) (𝜕𝑥 𝜕𝑥

+ 1)] − 𝑆

[1]

where θ is volumetric water content (L3/L3), ψ is the negative pressure head (i.e., soil suction) (L), 𝑘(ψ) is unsaturated hydraulic conductivity (L/T), and S is a sink term accounting for plant roots’ water uptake (M/L3).

𝑚 2

𝑘(ψ) = 𝑘𝑠 𝑆𝑒 [1 − (1 − 𝑆𝑒 ) ] (𝜃− 𝜃𝑟 ) 𝑠 − 𝜃𝑟 )

𝜕 (𝜃𝐶) 𝜕𝑡

𝜕

[3]

[4]

𝑆𝑒 = (𝜃

𝜕𝐶

𝜕

= 𝜕𝑧 (𝜃𝐷𝑒 𝜕𝑧 ) − 𝜕𝑧 (𝑞𝐶)

[5]

Where C is the solute’s total concentration (M/L3), q is the Darcy’s flux (L/T), and De is the hydrodynamic dispersion coefficient (L2/T). De is a function of dispersivity, molecular diffusion in free water, and tortuosity, 𝜏. HYDRUS-1D estimates the tortuosity as a function of volumetric water content using the relationship proposed by Millington and Quirk (1961):

𝜏= 3.2

𝜕𝜃 𝜕𝑡

[2]

𝜃7/3 𝜃𝑠2

[6]

Hysteresis Model

Over the years, a number of models have been proposed for the consideration of hysteresis in the SWCC. One of the most widely used hysteresis model for the SWCC has been proposed by Kool and Parker (1987) (Bashir et al. 2015). This model combines the van Genuchten (1980) SWCC model with empirical scaling hysteresis relationship proposed by Scott et al. (1983). The model predicts the scanning curves by scaling the main wetting and main drying curves. However, the model by Kool and Parker

(1987) suffers from the so-called pumping error. The pumping errors are produced by the non-closure of soil water characteristic scanning loops in simulations of cyclic wetting and drying. The pumping error has been discussed in detail by Werner and Lockington (2006). The hysteresis model by Parker and Lenhard (1987) uses a similar scaling procedure of Kool and Parker (1987). However, the scaled scanning curves pass through the previously-stored reversal points to force closure of hysteresis loops, which in return eliminates the so-called pumping errors (Lenhard et al. 1991). The wetting scanning according to this model curve can be described as:

𝑆(𝜓) =

[𝑆𝑒𝑤 (𝜓)− 𝑆𝑒𝑤 ( ∆𝜓𝑤𝑑 )](∆𝑆𝑒𝑑𝑤 − ∆𝑆𝑒𝑤𝑑 ) 𝑆𝑒𝑤 ( ∆𝜓𝑑𝑤 ) − 𝑆𝑒𝑤 ( ∆𝜓𝑤𝑑 )

+ ∆𝑆𝑒𝑤𝑑 [7]

Bashir et al. (2015). A zero-gradient water flow boundary condition was imposed at the bottom of the column, to simulate a freely draining profile. This condition is best suited for water table that exists far below the domain of interest (J. Šimůnek, M. Šejna, H. Saito, M. Sakai, and M. Th. van Genuchten 2013). Water content at field capacity (according to the drying curve) was used as the initial condition. The same value was used for all hysteretic and non-hysteretic simulations to ensure that appropriate comparison in terms of water storage can be made. For the salt transport, a non-reactive and conservative solute source, located at a depth between 1.0 m and 1.5 m, with a relative concentration of 1.0 was assumed. A concentration flux BC and a zero-concentration gradient were assigned to the top and bottom boundaries, respectively. 4.1.1

Where Se is the effective saturation (estimated using Eq. 2 and Eq. 4), and wd term describes the reversal from wetting to drying curve, also; vice versa is true for drying to wetting denoted by dw. The superscript, Δ, describes the target reversal point. In addition, drying scanning curve can be described as:

𝑆(𝜓) =

[𝑆𝑒𝑑 (𝜓)− 𝑆𝑒𝑑 ( ∆𝜓𝑤𝑑 )](∆𝑆𝑒𝑤𝑑 − ∆𝑆𝑒𝑑𝑤 ) 𝑆𝑒𝑤 ( ∆𝜓𝑤𝑑 ) − 𝑆𝑒𝑤 ( ∆𝜓𝑑𝑤 )

+ ∆𝑆𝑒𝑑𝑤

[8]

The empirical model by Parker and Lenhard (1987) is implemented in HYDRUS-1D (J. Šimůnek, M. Šejna, H. Saito, M. Sakai, and M. Th. van Genuchten 2013) and is used in this research. It should be noted that this model is also capable of accounting for air entrapment in hysteretic SWCC. 4

METHODOLOGY AND MODEL SETUP

The models comprised of 1-D soil domain that is 3 m deep. An atmospheric boundary condition with surface runoff was applied at the top boundary. This boundary condition is important in simulating land climate interaction (LCI) analysis. In such analyses the potential atmospheric fluxes are adjusted to actual values, depending on the available soil moisture and/or head condition at the soil surface. The climatic interactions of nine-year daily climate records (2006-2014) from Bighorn Dam, and Calgary were used. The climate records comprised of daily values of precipitation and potential evaporation. For Bighorn Dam, the average annual precipitation and potential evaporation were estimated to be 409 mm and 589 mm, respectively for the above-mentioned period. The climate of this area can be classified as dry-subhumid climate according to the Thornthwaite climate classification system (Thornthwaite and Hare 1955) (Bashir and Pastora Chevez 2018). On the other hand, Calgary has a much drier climate which was classified as arid, with average annual precipitation and potential evaporation of 320 and 981 mm, respectively. The simulations were run for only the active period (i.e., thawed condition) as suggested by Fredlund et al. (2012) and

Hydraulic Characteristics and Model Parameters

Table 1 shows the relevant van Genuchten (1980) parameters for hysteretic and non-hysteretic SWCCs for clay, where subscript d pertains to the drying curve and w pertains to wetting curve. The parameter, n, related to pore-size distribution, was assumed to be the same for wetting and drying curves. The validity of this assumption has been examined by a number of researchers (Kool and Parker 1987, Pham et al. 2005, Likos et al. 2014). In addition, Kool and Parker (1987) and Likos et al. (2014) have suggested that experimental evidence indicates that assumption αw ≈ 2αd is valid for a wide variety of soils. Since one of the objectives of this research is to quantify the impact of the increasing volume of trapped air on flow and transport, two hypothetical volumes of trapped air (0.05 and 0.1) were also considered. This assumption leads to different saturations for main drying (θsd) and wetting (θsw) curves. Based on experimental measurements conducted on 25 different cohesionless and cohesive soils, Likos et al. (2014) established a range of 2-27% for the degree of air entrapment, which aligns with the values chosen in this study. In this study, seven simulations were conducted for each climate type. The initial two simulations were non-hysteretic and involved the use of drying (DC) and wetting curve (WC) parameters respectively. The third simulation considered hysteretic effects (HC) but did not include air entrapment. Two additional hysteretic simulations were performed, assuming air entrapment (TA) percentages of 13% and 26% (i.e., HC-TA13% and HC-TA26%). Lastly, two more nonhysteretic simulations were carried out, utilizing the wetting curve parameters while considering air entrapment percentages of 13% and 26% (i.e., WC-TA13% and WCTA26%). Table 1. VG (1980) Fitting Parameters for Hysteretic Clay Textural Soil

θsd

θsW

θr

αd (1/m)

αW (1/m)

n

Ks (m/d)

TA13%

0.38

0.33

0.068

0.8

1.6

1.3

0.048

TA26%

0.38

0.28

0.068

0.8

1.6

1.3

0.048

5

RESULTS AND DISCUSSION

In this paper, positive flux values indicate water entering the system, while negative values indicate water leaving the system. Runoff is always expressed as a positive value. 5.1

Hysteresis and Water Flow

Figure 2 illustrates the cumulative values of actual evaporation (AE), net infiltration (NI), bottom flux (BF), and runoff (RO) at the end (9 years) for the Bighorn Dam region. The results for hysteretic and non-hysteretic simulations, with and without air entrapment are presented in this figure. While the cumulative differences between the hysteretic and non-hysteretic simulations are relatively modest, it can be observed that the non-hysteretic simulation employing drying curve parameters (DC) yielded the highest AE value of 3310 mm, compared to the hysteretic simulation (HC) with 3294 mm and to non-hysteretic simulation employing wetting curve parameters (WC) with 3082 mm. These differences are reflected in the net infiltration (NI) values which is calculated as follows: 𝑁𝐼 = 𝑃 − 𝐴𝐸 − 𝑅𝑂

[9]

where P is the precipitation and other variables are as defined previously. To explain this trend, it is important to note that the DC simulation employs the drying curve characteristics with the highest air entry value (indicated by the lowest α value as shown in Table 1) compared to WC simulation. The higher air entry value associated with the drying curve results in higher water retention near the surface, thereby making water more readily available for evaporation. Consequently, this translates to larger values of AE and smaller NI for DC in comparison to WC. On the other hand, wetting curve has a higher α value (i.e., αw = 2αd) which implies lower air entry value and lower water retention leading to a reduced amount of available water for evaporation. This results in lower AE and higher NI. The HC simulation exhibits an intermediate AE and NI value between DC and WC simulation as it uses both the drying and wetting characteristics. It is also important to note the relationship between increasing degree of air entrapment and the corresponding increase in fluxes, as shown in Figure 2. In the specific case of a hysteretic simulation, where air entrapment is present, the increase in the amount of entrapped air has been found to correspond to an increase in NI flux. For instance, in the simulation with 26% air entrapment (HCTA26%), the NI flux estimated was 366 mm. Comparatively, in the hysteretic simulations with 13% air entrapment (HCTA13%) and zero air entrapment (HC), the measured NI fluxes were 326 mm and 295 mm, respectively. These findings suggest that the presence and amount of entrapped air significantly impacts the quantity of water entering the top boundary of the soil domain (NI). As the volume of trapped air increases, the NI flux also increases, as demonstrated by the higher values observed in the simulation with greater air entrapment. Due to the inclusion

of air entrapment in the SWCC, for the same water content, suction becomes lower after air entrapment. Additionally, the main wetting branch and scanning wetting branches are characterized by approaching full saturation more readily compared to the case without air entrapment, thereby reducing soil suction and facilitating a greater downward water flow. Consequently, introducing air entrapment reduces the water retained near the surface, leading to a lower rate of AE and a larger NI. This behavior is further amplified with a higher degree of air entrapment. On the other hand, simulating the characteristics of non-hysteretic main wetting curves after entrapment of air (i.e., smaller saturated volumetric water content), the downward water flow accelerates, resulting in larger infiltration values compared to their corresponding hysteretic simulations. Specifically, using the characteristics of wetting branch after air entrapment, particularly with 26% air entrapment, yielded the lowest rates of AE and the highest NI compared to all other simulations. The trends of bottom flux observed in various hysteretic and non-hysteretic simulations are consistent with the trends in the NI, as there is a relationship between the bottom fluxes and the fluxes entering the soil surface (NI).

Figure 2. Bar Chart of Cumulative Fluxes for the Clay’s Profile using Hysteretic and Non-hysteretic Characteristics – Bighorn Dam. Due to the lower hydraulic conductivity of the clay, runoff was generated. However, the runoff was more significant for the hysteretic models when air entrapment was considered. Runoff was found to be directly proportional to the degree of air entrapment. In general, runoff is generated when the surface reaches its full saturation, and the potential water flux (P-PE) becomes higher than the surface’s infiltration capacity (i.e., saturated hydraulic conductivity). Air entrapment results in reduced pore space for water leading to easier reach for full apparent saturations. This is true when air entrapment

occupies a sufficient pore volume, preventing further water entry into the system. 5.2

Hysteresis and Salt Transport

To assess the influence of hysteresis and air entrapment on solute transport, numerical solutions of the advectivedispersive equation were employed. The results obtained from different simulations are then compared by calculating the center of mass (COM) of the salt plume over time. The center of mass is the average penetration depth of the solute and is illustrated in Figure 3 for Bighorn dam. The center of mass is calculated as follows (Mitchell and Mayer 1998, Fetter et al. 2018):

It is also interesting to note that for non-hysteretic simulations using the wetting curves, especially after entrapment of air, produced the largest COM values as well as the earliest advancement of salt towards the bottom boundary. This trend corresponds to the rapid water flow that these simulations exhibit as well as their cause for large atmospheric fluxes entering the soil system.

∞

𝐶𝑂𝑀 =

∫0 𝜃(𝑧,𝑡) 𝐶(𝑧,𝑡) 𝑧 𝑑𝑧 ∞

∫0 (𝑧,𝑡) 𝐶(𝑧,𝑡) 𝑑𝑧

[10]

Where z is the vertical depth, t is time, θ is the volumetric water content, and C is the concentration. Important to note that to better show the extent of the salt within the clay profile as influenced by hysteresis, the LCI analyses were extended to 18 years, by duplicating the 9-year atmospheric BC (Figure 3). Results in Figure 3 show that COM of the salt was initially at 1.25 m which is indicative of halfway distance between 1.0 m and 1.5 m. This figure illustrates that the penetration depth of salt, increased with time, indicating that the salt is moving downwards as the result of advective flow. In Figure 3, the COM values for the hysteretic simulation without air entrapment (HC) produced a temporal distribution that is almost similar to that from the nonhysteretic drying curve (DC) simulation. It is important to note that this result is not surprising as the water flow results (NI & BF) for HC and DC simulation had minimal differences between them. However, larger differences in the COM values (in reference to the non-hysteretic DC simulation) can be observed after inclusion of air entrapment in the hysteretic SWCC. Salt considerably penetrated deeper after the introduction of air entrapment, indicated by the larger COM values for HC-TA13%, HCTA26% respectively than those from HC and DC simulations. Proportional relationship between higher degree of air entrapment and larger COM movement, is consistent with the amount of NI for these simulations (water balance results in Figure 2). The increased salt penetration depth with the consideration of hysteresis with air entrapment or using the wetting branch of hysteretic SWCC, with and without consideration of air entrapment can be explained by the quantity of water entering the system and magnitude of the advective flux. As described above, hysteretic simulations with air entrapment or non-hysteretic simulations employing wetting branch of SWCC, in general result in larger quantities of water entering (NI) or exiting (BF) the system. In addition, the reduced volumetric water contents as a result of air entrapment results in larger magnitude of adjective flux. This is rationale is also supported by results and discussion from Mitchell and Mayer (1998).

Figure 3. Center of Mass of Salt in the Clay’s Profile using Hysteretic and Non-hysteretic Characteristics – Bighorn Dam. To examine the extent of salt displacement as a function of the climate type, influenced by hysteresis and air entrapment, Figure 4 plots temporal distribution of COM for Calgary (arid climate) and Bighorn Dam (dry sub-humid climate). Furthermore, the results depicted in Figure 4 illustrate the differences between the COM values obtained from individual simulations and the DC simulation. The figure shows that differences between the DC simulation and hysteretic or non-hysteretic wetting simulations with air entrapment are more pronounced for drier climate than for wetter climate. However, this finding should be interpreted with caution since climate from different regions can be similar but can potentially have unique elements such as intensity and temporal distribution of precipitation. 6

CONCLUDING REMARKS

This study utilized HYDRUS-1D to numerically model the water flow and salt transport in an unsaturated clayey soil. Nine-year climate data from two contrasting climate types from Bighorn Dam and Calgary (Alberta, Canada), were simulated as part of the LCI analysis. The investigation focused on examining the impact of hysteresis in SWCC, with and without the consideration of air entrapment, as well as with consideration on varying degree of air entrapment. The results are presented in the form of water

balance at the ground surface and the temporal distribution of COM of the salt, as a function of the climate type. It was discovered that neglecting hysteresis in the SWCC can lead to noticeable variations in the resulting estimations of fluxes and the assessment of the solute's fate, depending on which branch of SWCC (i.e., drying or wetting curve) is used in the simulations. The inclusion of hysteresis with air entrapment was observed to accelerate water flow, resulting in reduced moisture retention near the surface, which, in turn, decreased evaporation and increased the net infiltration. This had a pivotal impact on advancing the salt, facilitating its transport to deeper depths. Furthermore, increasing degree of air entrapment appears to intensify the facilitation of salt movement. An additional important discovery was that the presence of trapped air caused runoff to occur. This phenomenon occurs when the volumetric water content in the soil decreases below the porosity, specifically after air entrapment, leading the soil pores to readily achieve a suction of zero. This particular aspect also had a substantial impact on the behavior and distribution of salt.

Figure 4. Center of Mass of Salt Relative to DC Simulation, from Two Climate Types: Calgary with Arid Climate (Top) and Bighorn Dam with Dry Sub-Humid Climate (Bottom). Comparison of the results from different climates indicated that the hysteretic and air entrapment impact on the transport of salt was higher for drier climate compared to wetter climate. This implies that different climatic conditions could potentially yield diverse assessments for hysteresis impact on solute fate. It is important to understand that

climate is complex and dynamic, and that distribution and intensities of the climate forces varies from one geographical location to another, dictating how hysteresis can play a role in unsaturated flow and transport. It is important to consider various locations with similar/different climate types, as intensities and distribution of precipitation as well as intensities of potential evaporation can vary drastically. The current research aims to establish a foundational understanding of the effects of hysteresis and air entrapment on solute transport under multi-year atmospheric interactions. 7

REFERENCES

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Geoenvironmental Engineering, 140(4): 04013050. doi:10.1061/(ASCE)GT.19435606.0001071. Millington, R.J., and Quirk, J.P. 1961. Permeability of Porous Solids. Transactions of the Faraday Society, 57: 1200–1207. doi:10.1039/tf9615701200. Mitchell, R.J., and Mayer, A.S. 1998. The Significance of Hysteresis in Modeling Solute Transport in Unsaturated Porous Media. Soil Science Society of America Journal, 62(6): 1506–1512. doi:10.2136/sssaj1998.03615995006200060005 x. Parker, J.C., and Lenhard, R.J. 1987. A Model for Hysteretic Constitutive Relations Governing Multiphase Flow: 1. Saturation-Pressure Relations. Water Resources Research, 23(12): 2187–2196. doi:10.1029/WR023i012p02187. Pham, H.Q., Fredlund, D.G., and Barbour, S.L. 2005. A Study of Hysteresis Models for Soil-Water Characteristic Curves. Canadian Geotechnical Journal, 42(6): 1548–1568. doi:10.1139/t05-071. Pickens, J.F., and Gillham, R.W. 1980. Finite Element Analysis of Solute Transport Under Hysteretic Unsaturated Flow Conditions. Water Resources Research, 16(6): 1071–1078. doi:10.1029/WR016i006p01071. Richards, L.A. 1931. Capillary Conduction of Liquids Through Porous Mediums. Physics, 1(5): 318– 333. doi:10.1063/1.1745010. Robertson, C., and Geol, P. 2006. In-Situ Remediation of Brine Impacted Soils and Groundwater Using Hydraulic Fracturing, Desalinization and Recharge Wells. Wiebe Environmental Services Inc. Royer, J.M., and Vachaud, G. 1975. Field Determination of Hysteresis in Soil-Water Characteristics. Soil Science Society of America Journal, 39(2): 221– 223. doi:10.2136/sssaj1975.03615995003900020006 x. Russo, D., Jury, W.A., and Butters, G.L. 1989. Numerical Analysis of Solute Transport during Transient Irrigation: 1. The Effect of Hysteresis and Profile Heterogeneity. Water Resources Research, 25(10): 2109–2118. doi:10.1029/WR025i010p02109. Rutherford, S. 2004. Groundwater Use in Canada. West Coast Environmental Law, Vancouver, BC. Scott, P.S., Farquhar, G.J., and Kouwen, N. 1983. Hysteresis effects on net infiltration, Advances in Infiltration. American Society of Agricultural Engineers Publication 11-83,: 163–170. Šimůnek, J., and van Genuchten, M.Th. 1995. Numerical Model For Simulating Multiple Solute Transport In Variably-Saturated Media. U.S. Salinity Laboratory, USDA, ARS, 4.50 Big Springs Road, Riverside, CA, 92507, USA,: 21–30. Stauffer, F., and Dracos, T. 1984. Local Infiltration into Layered Soil and Response of the Water Table, Experiment and Simulation. Frontiers in hydrology,: 228–242.

Thornthwaite, C.W., and Hare, F.K. 1955. Climatic Classification in Forestry. Vereecken, H., Diels, J., and Viaene, P. 1995. The Effect of Soil Heterogeneity and Hysteresis on Solute Transport: a Numerical Experiment. Ecological Modelling, 77(2–3): 273–288. doi:10.1016/03043800(94)00183-I. Werner, A.D., and Lockington, D.A. 2006. Artificial Pumping Errors in the Kool–Parker Scaling Model of Soil Moisture Hysteresis. Journal of Hydrology, 325(1–4): 118–133. doi:10.1016/j.jhydrol.2005.10.012.

LABORATORY ASSESSMENT OF THE CLAY-BASED COVERS ABILITY TO LIMIT THE ACID MINE DRAINAGE PRODUCTION. Emmanuel Niamké, Abdelkabir Maqsoud, Tikou Belem, Mamert Mbonimpa, Université de Québec en Abitibi-Temiscamingue, Rouyn-Noranda, QC, Canada. ABSTRACT Clay materials deposit cover a large area in the Abitibi region but are less used in mine site reclamation due to their susceptibility to freeze-thaw (FT) effects. These effects induce changes in their hydrogeological properties and increase their saturated hydraulic conductivity (ksat). Recently, Abitibi clay materials (CM) were amended in the lab and submitted to different FT cycles. Investigation results show that FT effects on the k sat were very limited. To evaluate the possibility of using the amended clay materials as low hydraulic layer (with sand and/or silt material up to 25%) in the mine site reclamation, this study was initiated. Eight Laboratory-based, instrumented experimental columns, were used to simulate, low saturated hydraulic conductivity covers (LSHCC) made with different amended clay materials and submitted to wetting-drying cycles, where volumetric water contents, and suctions were measured. Investigation results show that during tests, the amended clay materials remained close to water saturation and the water infiltration across LSHCC were very limited. These results allow to conclude that amended clay materials can be used adequately as LSHCC. RÉSUMÉ Les matériaux argileux couvrent une grande superficie en Abitibi mais leur utilisation comme matériaux de construction dans la restauration des sites miniers reste limitée en raison de leur susceptibilité aux effets du gel-dégel (GD). Ces effets induisent des changements dans leurs propriétés hydrogéologiques et engendre une augmentation de leur conductivité hydraulique saturée (ksat) Récemment, les matériaux argileux de l'Abitibi ont été amendés et soumis à différents cycles de GD au laboratoire. Les résultats de ces essais montrent que les effets du GD sur la ksat étaient très limités. Afin d'évaluer la possibilité d'utiliser les matériaux argileux amendés (avec du sable ou du silt jusqu'à 25 %) dans les recouvrements de type barrière hydraulique, cette étude au laboratoire a été initiée. Huit colonnes expérimentales instrumentées simulant un recouvrement de type barrière hydraulique à base d’argile amendée ont été utilisées. Ces colonnes ont été soumises à des cycles de mouillage et de drainage où les mesures de teneurs en eau volumiques, et des succions ont été réalisées. Les résultats de ces investigations montrent que les matériaux argileux amendés ont été maintenu de façon permanente proche de la saturation et que les infiltrations d’eau à travers le recouvrement étaient très limitées. Ces résultats permettent de conclure que les recouvrements à base d’argile peuvent être utilisés dans la restauration des sites miniers comme barrière face à l’infiltration d’eau. Keywords: Acid mine drainage, Mine site reclamation, Low saturated hydraulic barrier, amended clay material. 1

INTRODUCTION

When mine wastes are considered, AMD generating, different management options and rehabilitation strategies are available to inhibit the AMD production. Among of these technics one can find oxygen and hydraulic barriers. The hydraulic barrier or low saturated hydraulic conductivity cover (LSHCC) can be made with different materials (Maqsoud et al. 2021): natural materials such as claybased materials, geomembranes, and geosynthetic clay liners. In the Abitibi region, there is an abundance of clay materials; however, their effectiveness as LSHCC is limited due to their vulnerability to freeze-thaw effects, which can lead to an increase in saturated hydraulic conductivity (ksat). Nonetheless, advancements have been made by introducing sand and silt as amendments to these clay

materials. Laboratory tests conducted on these materials demonstrated that after undergoing freeze-thaw cycles, no significant changes were observed in the ksat (Merzouk et al. 2002). The objective of this paper is to test the performance of the amended clay materials as a LSHCC and evaluate their effectiveness in limiting water infiltration towards reactive tailings. This paper provides a brief overview of the LSHCC technic and discusses the factors that influence the hydrogeological properties of clays materials used in LSHCC. Additionally, it presents the characterization of the clay-based materials employed and describes the laboratory experimental column configurations. The preliminary evaluation results of the hydrogeological behaviors of the physical models will be presented. Finally, the paper concludes with a conclusion.

2

LSHCC

LSHCC can be used to control AMD generation due to their low permeability, which limits water infiltration toward underlying tailings. However, it is recommended to use double liners to control water infiltration in mine site reclamation (Das 2021, Maqsoud et al. 2021). When clay materials are used as LSHCC, the final cover thickness ranges between 0.45 and 0.90 m. The material that can be used in these LSHCC must have the following properties (Benson et al. 1994; Marcoen et al. 2000; Maqsoud et al. 2021): i) liquid limits must be greater than 20%, ii) plasticity indices must be greater than 7%, iii) plasticity index (PI) must be less than 20%, iv) fine particles percentage must be greater than 30% and the ksat must be less than or equal to 10-9 m.s-1, and finally, the percentage of gravel must be less than 50%. Clay materials have several advantages, such as their ability to retain contaminants through sorption, self-seal, and low costs associated with their use as natural materials when they are present near mine sites (Maqsoud et al. 2021). However, the clay materials can be affected by different phenomena such as wetting and dying cycles, freeze-Thaw cycles, and compaction effect. 2.1

Wet-dry cycles

After the construction the LSHCC will be exposed to atmospheric conditions, and therefore, they will be subjected to wetting and drying cycles. When clay-based materials did not have the appropriate properties, wetting and drying cycles can lead to desiccation phenomena, During the dry period, water loss due to evaporation leads to an increase in the matrix suctions of these materials. As result their volume reduces, and cracks start to form. Unfortunately, these materials do not posses the ability to deform laterally, and these cracks create pathways that facilitate water percolation (Bussière and Guittonny 2020, Maqsoud et al. 2021). This percolation allows water to reach the underlying reactive tailings, leading to the oxidation of sulfide materials. Consequently, this oxidation triggers reactions that are responsible for the generation of AMD. However, for clays with high shrinkage volume, amendments with sand or other suitable materials can reduce their respective shrinkage volumes. This enables the use of clay as cover materials (Omidi et al. 1996). 2.2

Freeze and thaw cycles

Low temperatures can reduce the covers statured hydraulic conductivity during frozen state and reduce the sulfide oxidation which control AMD (Bussière and Guittonny 2020). Fine grain layer covers including claybased covers can indeed be affected by freeze and thaw cycles (FT). These cycles can lead to changes in the k sat which can have implications for controlling AMD generation. During FT cycles, the suction generated by the freezing front in clay layer can attract unfrozen water molecules towards the frozen zone. This can result to the material

structure and the formation of cracks particularly plastic material with PI ranging between 20 to 100. However, for low plasticity soils with PI less than 10 to 20 they are less affected by FT since they can self-heal (Othman et al. 1994; Eigenbrod 2003; Maqsoud et al. 2021). When material is used as LSHCC, they can prevent water from the groundwater table to reach the frost fronts to form ice lenses (McCarthy and McCarthy 1977), which alternated their hydraulic properties. That is why claybased amendment materials were recently tested in the lab to overcome FT cycles negative impacts. These clay-based amendment materials aim to improve their respective hydraulic properties to constitute effective LSHCC, since the granulometric contrast of the mixed materials can facilitate their entanglements, which will enable the reduction of their void indices by compaction to make them more impermeable to water. Consequently, it is crucial to consider the potential effects of FT cycles on fine grain layers and take appropriate measures to preserve their hydraulic properties in order to maintain effective control over AMD. Indeed, the recent amendment of clay materials with sand and silt materials has shown promising results in overcoming the challenges posed by freeze and thaw (FT) cycles and enhancing the overall performance of these materials, particularly in terms of their saturated hydraulic conductivity (ksat). The incorporation of sand and silt materials into clay amendments helps to improve the granulometric properties and compaction characteristics of the materials, resulting in reduced void indices and enhanced impermeability to water. These improvements contribute to better performance in terms of k sat, indicating a more effective water barrier. The study by Merzouk et al. (2022) likely provides valuable insights and detailed findings regarding the positive effects of clay-based amendment materials on overcoming FT cycle challenges and improving ksat performance. 2.3

Compaction conditions

Clays are cohesive materials characterized by finegrained particles and high-water content due to their mineral structures. When clay materials are used as cover materials with low saturated hydraulic conductivity, several factors such as in situ water content and compaction can affect their ksat (Maqsoud et al. 2021). Indeed, achieving a desired ksat of 10-9 m.s-1, requires specific considerations during material placement. It is crucial to remove clods, which are compacted masses or aggregates of soil particles, to ensure a uniform and consistent material distribution (Benson and Daniel, 1990; Maqsoud et al. 2021). This removal helps to avoid potential pathways for preferential and ensures more homogeneous hydraulic properties. Also, water content and compacting forces should be controlled to reach the appropriate criteria as those propose by Daniel and Benson (1990). It is important to recall that materials with a wide range of grain sizes have a better compactness, which facilitates the reduction of their voids and, consequently, their respective permeability (McCarthy and McCarthy 1977). In the context of this

study, which focuses on testing amended clay materials, all the aforementioned factors have been taken into consideration. In the following section, the materials and methods employed in this research are presented. 3

MATERIAL AND METHODS

Based on previous studies where the clay materials were amended and tested (Merzouk et al. 2022; Granados et al. 2022), six mixtures were prepared by using clay, silt and sand materials. Table 1. Clay-based mixtures and percentage of sand and silt Samples

Clay (%)

Sand (%)

Silt (%)

Mixture 1

100

0

0

Mixture 2

85

15

0

Mixture 3

85

0

15

Mixture 4

95

5

0

Mixture 5

75

25

0

Mixture 6

95

0

5

Mixture 7

75

0

25

Table 2. Geotechnical characterization results of the claybased materials Mixtures

LL (%)

PL (%)

IP (%)

Mixture 1

29.08

19.00

10.08

Mixture 2

27.40

10.42

16.98

Mixture 3

27.55

9.22

18.33

Mixture 4

21.32

10.54

10.78

Mixture 5

25.50

10.68

14.82

Mixture 6

27.33

10.70

16.63

Mixture 7

27.79

15.01

12.78

3.1.2 Physical characterization The particle size distribution of the clay-based materials was obtained using the Malvern laser particle analyzer, and the results are presented in Figure 1.

All the materials utilized in this study underwent physical, geotechnical, and hydrogeological characterization. 3.1

Materials characterization

3.1.1 Geotechnical characterization The liquid limits (LL) of the clay-based materials were obtained using the Swedish cone penetrometer, and their respective plasticity limits (PL) were determined according to the ASTMD 4318 standard (ASTM 2005). The plasticity indices (IP) of the clay-based materials were calculated by subtracting their respective plasticity limits from their liquid limits (ASTM 2005). The geotechnical characterization results of the claybased materials are shown in the Table 2. It has been observed that the liquid limit, and plastic limit values of the clay-based materials amended with sand and silt are below that of clay be. While the plasticity index the clay-based material amended with sand and with silt are above that of the clay.

Figure 1 Materials particle size distribution It is observed that all the studied materials exhibited particle size curves that were below that of the 100% clay material. Furthermore, the analysis related that as more silt was added, the granulometric curves of the clay-silt mixture tend to approach that of the 100% clay material when more silt is added. While the particle size curves of the clay-sand claybased materials tend to deviate from the particle size curve of the 100% clay material when more sand is added, except for the 85% clay material with 15% of sand. In comparison to the criteria mentioned above for the use of clay-based materials in LSHCC, the particle size distribution analysis revealed that the clay-based materials had percentages of particles less than 2 µm greater than 15%. Specifically, these percentages ranged from 17 to 27.5%. Additionally, exhibited percentages of fine particles greater than 30% with values ranging from 61% to 75.5%. On the other hand, the percentages of gravel content in the

materials were found to be less than 50% with values ranging from 0% to 0.5% of gravels. The plasticity diagram in Figure 2 depicts the liquid limits and plasticity index values of the clay-based materials. By analyzing the liquid limits and plasticity index values, the materials can be categorized into different classes or categories.

expansion and fluctuations.

contraction

during

environmental

Figure 2 Plasticity diagram for the clay-based materials

Figure 3 Volume change potential classification

Based on the position of the points representing intersections between the liquid limits and the respective plasticity indices of the clay-based materials on the diagram, it is evident that all the materials fall within the (CL) zone. This classification indicates that the clay-based materials can be characterised as inorganic clays with low plasticity based on the plasticity diagram analysis. In addition to the aforementioned analysis, the percentages of clay-based material particles smaller than 2 microns obtained from the particle size, were utilized along with their plasticity indices calculated from the Atterberg limits. These data were used to plot the materials on the potential volume change classification diagram for clay soils as illustrated in Figure 3. This diagram allows us to determine whether the clay-based materials under study are susceptible to swellings or not. By examining the placement of the materials on the diagram, we can assess their potential for volume changes and identify if they are prone to swelling. Based on the points obtained on the volume change diagram, it can be concluded that the clay-based materials will experience slight effects from expansion phenomena, as they fall into the low expansion category on the diagram. However, it should be noted that the 85% clay material mixed with 15% silt is expected to be moderately prone to swelling based on its position on the diagram. This suggests that this particular mixture may exhibit a higher potential for volume changes compared to the other claybased materials studied. Indeed, based on the classification of the clay-based materials as having low expansion capacities, they are expected to be less susceptible to the effects of wet and dry cycles as well as freeze and thaw cycles. The materials' limited potential for volume changes indicates that they may exhibit relatively stable behavior under such conditions. This is a positive characteristic that suggests these clay-based materials may be less prone to significant structural damage or performance issues associated with

3.1.3 Hydrogeological characterization As part of hydrogeological characterization, ksat and water retention curve (WRC) measurement tests were performed. The ksat was determined using flexible wall permeameters for clay-based soils and rigid wall for sandy and silty soils. Table 3 illustrates that the clay-based materials exhibit significantly lower ksat values compared to sands and silts. Table 3. Materials saturated hydraulics conductivity Material

ksat

100% Clay

2.22E-10

95% Clay 5% Silt

1.10E-10

85% Clay 15% Silt

8.52E-10

75% Clay 25% Silt

1.39E-10

95% Clay 5% Sand

1.86E-10

85% Clay 15% Sand

3.51E-10

75% Clay 25% Sand

1.04E-10

100% Sand

7.07E-05

100% Silt

6.75E-06

Additionally, the saturated hydraulic conductivity values of the clay-based materials are found to be less than 10-9 m/s, fulfilling one of the criteria for their utilization as LSHCC. This implies that the clay-based materials possess a low permeability, limiting the infiltration of water and indicating their potential suitability for controlling acid mine drainage (AMD) as part of LSHCC systems. The observed saturated hydraulic conductivities align with the desired requirements for effective water barrier performance in LSHCC application. WRC was determined using pressure plate test and Temp cells. The resulting curves illustrating the relationship between volumetric water content and suction water are displayed in the Figure 4.

3.2

Figure 4 WRC of amended clay-based material The Air Entry Value (AEV) corresponds to the suction or pressure at which the material starts to desaturate. This pressure was determined using the tangent method. Table 4 shows the AEV values for different materials as the material porosities.

LSHCC physical modeling

Experimental columns were employed to simulate LSHCC using the mixture materials. In these configurations, the following parameters were varied: 1) type of cover (monolayer vs. multi-layer) and 2) mixture materials (see Figure 5). The experimental columns used in the tests had a diameter of 14 cm. The columns were filled from the bottom to the top, with a 30 cm layer of tailings followed by the respective mixture materials. For the monolayer columns, clay-based materials were used. The first column consisted of mixture 1 with a thickness of 80 cm. The second, third, fifth, sixth, seventh, and eighth columns were filled with mixture 2, 3, 5, 6, 7, and 8, respectively, as specified in Table 1. The thicknesses of these mixture layers are about 60 cm (see Figure 5).

Table 4. Materials AEV and porosity Material

Porosity (n)

AEV (kPa)

100% Clay

0.44

95

95% Clay 5% Silt

0.37

53

85% Clay 15% Silt

0.35

57

75% Clay 25% Silt

0.32

113

95% Clay 5% Sand

0.34

105

85% Clay 15% Sand

0.33

170

75% Clay 25% Sand

0.31

89

100% Sand

0.38

2

100% Tailings

0.39

5

100% Silt

0.40

60

It is observed that with the increase in the quantity of silt added to the clay materials, the AEV also tends to increase. This suggests that as the proportion of silt increases, the mixtures become more impermeable to water. A higher pressure is required to force the desaturation of these mixtures due to the presence of silt particles, which can enhance their water-holding capacity. However, the AEVs of the mixture made with i) 95% clay and 5% silt, and ii) 85% and 15 silt are lower compared to the 100% clay material. While, for amended clay material with sand, their AEVs tend to decrease, with the increase of sand proportion added to the mixture, except for the 85% clay for 15% of sand, which has higher AEV. Based on the information provided, it appears that the prepared mixes meet all the criteria required to serve as low saturated hydraulic conductivity roofing materials. These different mixtures will be tested as LSHCC component by using instrumented experimental columns.

Figure 5: Experimental configuration and sensors locations The multi-layer column in the experiment corresponds to the fourth column. It is constructed with a specific configuration that includes a 40 cm layer of sand sandwiched between two clay layers, each 40 cm thick. The objective of this configuration in column 4 is to create the capillary barrier effect at the interfaces between the sand and clay layers. This effect is intended to enhance

water retention by taking advantage of the contrast in grain size between the sand and clay materials. This will eventually lead to high saturation levels in the upper and lower clay layers. The control column in the experiment is represented by the ninth column. This column is made with a tailing height of 100 cm. The control column serves as a reference column in the laboratory, allowing for the evaluation of water flow in a physical model without any cover on top. To monitor the hydrogeological parameters, each column, is equipped with EC5 probes and watermark sensor for volumetric water content and suction measurements, respectively. These sensors are strategically placed within the columns at specific locations, as indicated in Figure 5. It is worth noting that these sensors are connected to dataloggers, which allow for continuous measurement. In the experiment, each column underwent wetting and drying cycles. After each cycle, the amount of water infiltration, if any, was collected and measured. This process allowed for the assessment of water movement and infiltration characteristics within the columns under wetting and drying conditions. 4

INVESTIGATION RESULTS

This Figure shows a succession of an increase of volumetric water content following each wetting process. Then a decrease in volumetric water content during the drying period. This behaviour is typical of tailing materials. The measured values range from 0.29 to 0.46 near the bottom of the column, from 0.41 to 0.50 in the middle, and from 0.26 to 0.42 near the top. It is important to note that the lower values of volumetric water content near the top of the control column can be attributed to the evaporation effect at the end of the drying cycle. 4.1.2 Control with LSHCC It is crucial to highlight that during the first wetting cycle, it was observed that water remained above the clay-based covers for a period exceeding 9 months, despite the application of strong suction using peristaltic pumps on column 2 for 55 days, on column 4 and 5 for 41 days, on column 6 and 8 for 35 days and on column 7 for 25 days. This indicates that the clay-based covers had a limited ability to drain or remove the water that accumulated on top of them. The Figure 7 shows the presence of water above the clay-based covers after the 9-month experimentation period.

In this section, only typical results of volumetric water content and suction measurements will be presented. 4.1 Volumetric water content 4.1.1 Control column (column 9) The control column was submitted to different wetting and drying cycles. The materials were wetted every four weeks using deionized water; the volume of deionized water corresponds to 2 L with the objective of saturating the materials. Volumetric water content measurement in the different location in the control column (see Figure 5 for sensor location) are presented in the Figure 6, shrinkages curve was performed and the equation y=1.0052x was used for tailings, where y is the corresponding water content value and, x the readings from the probes.

Figure 7: Water accumulation presence above covers in the experimental columns after 9 months experimentation. These water accumulations during 9 months after the first wetting indicate that infiltration across the cover material is very limited.

Figure 6: Volumetric water content measurements performed in the control column (column 9)

4.1.2.1 Column 2 For the column 2, volumetric water content measurements are presented in the Figure 8, shrinkage curve were performed and the equation y=1.0052x was used for tailings, y=0.7013x for Mixture 2, where y is the corresponding water content value and, x the readings from the probes. In the upper zone (80 cm from the bottom – Port 4) the measured volumetric water content values are included between 0.15 and 0.21. In the central zone of the cover (at 60 cm from the bottom - port 3) the measured values are included between 0.14 and 0.48. In the lower

y=0.5991x for sand material where y is the corresponding water content value and, x the readings from the probes The Figure illustrates that the sandy layer exhibits a lower volumetric water content, which can be considered close to the residual value of 0.008. In contrast, the other layers demonstrate a sustained high degree of saturation at various levels, primarily due to the capillary effect created by the presence of the sandy layer.

Figure 9: Volumetric water content measurements performed in the column 4 (Multi-layer column) 4.2

Suction measurements

4.2.1 Column 9 (control column) Suction measurements performed at different level in column 9 (control column) are presented in the Figure 10. PORT1

PORT2

100

Suctions (kPa)

zone of the cover (at 40 cm from the column bottom - port 2), measured values of volumetric water content are included between 0.14 and 0.28. While in the tailings (at 20 cm near the interface - port 1) measured VWC values ranged between 0.24 and 0.44. The observed fluctuation at different level can be explained by: • at levels 3 and 4, the water contents experienced an increase during the pumping period that was initiated from the bottom of the column, spanning from June 15, 2022, to June 28, 2022. Following this period, the water contents remained relatively constant throughout the test. • at levels 1 and 2, a decrease in volumetric water content was observed due to the pumping conducted from June 15, 2022, to June 28, 2022. However, from June 28, 2022, to July 29, 2022, the water content started to rise again. This increase can be attributed to the column being saturated from below during this period. Subsequently, a drop in the water content at ports 1 and 2 was observed during the pumping period from July 29 to September 08, 2022. Following this period, the water content gradually increased, and notable peaks in water content were observed on November 1, 2022, and January 12, 2023, due to water injections into the tailings. This water was injected directly into the tailings to assess the water quality within them, primarily because infiltration from above was extremely limited, if not absent. This direct injection method was utilized to ensure an accurate evaluation of the water quality specifically within the tailings themselves.

10

1

Dates

Figure 10: Suctions measurements performed in the Column 9 (control) Except during wetting event in the tailing material, all the measured suction are higher than 10 kPa (AEV = 5 kPa). These values indicate a desaturation of this material. Figure 8: Volumetric water performed in the column 2

content

measurements

4.1.2.2 Column 4 (multi-layer cover) Volumetric water content measurements for the multilayer cover, filled with a sandy layer sandwiched between two clay layers, are depicted in Figure 9 ,shrinkage curve were performed and the equation y=1.0052x was used for tailings, y=0.9625x for mixture 1 clay material and

4.2.2 Column 2 Suction measurements performed in the column 2 are present in the Figure 11. This Figure shows that all measurement permed in amended clay material remain below 10 kPa indicating that the clay material remains close to the saturation (AEV = 170 kPa). However, the suction measured in the tailings were included between 8 and 49 kPa indicating a desaturation of this material (AEV close to 5 kPa).

Suctions (kPa)

100

PORT1

Moreso the extreme conditions (extra dry year and extra wet year) will be considered within the simulation in addition to these results. However, these parameters are still being evaluated to confirm the partial results obtained in the final phase of the project.

PORT4

10

6

ACKNOWLEDGEMENTS The authors would like to express their special gratitude FRQNT, NSERC, and the Horne Foundry for their generous financial support.

1

Dates Figure 11: Suctions measurements performed in the Column 2 4.2.3 Column 4 (Multi-layer cover) Suction measurement performed in the multi-layer cover are presented in the Figure 12. This last Figure show that the suctions measured in the clay materials ranged from 0 to 12 kPa, which confirms that these materials remained saturated throughout the entire test period. These values are consistent with the measurements of volumetric water content. 100

Suctions (kPa)

PORT2

PORT3

PORT4

10

1

Dates

5

Figure 12: Suctions measurements performed in the Column 2 CONCLUSION

Based on the characterizations results obtained for the clay-based material, we can conclude that amended clay materials possess the required properties to be used as low saturated hydraulic cover (LSHCC). They satisfy the LSHCC characterization criteria and have low expansion capacities, which makes them less exposed to negative effects of wet-dry and freeze-thaw cycles. Furthermore, the monitoring of hydrological parameters in the laboratory experimental columns reveals that these physical models of clay-based materials effectively limit the flow of water towards the underlying reactive tailings compared to the control column. The presence of water at the top of the clay-based cover and the recorded volumetric water contents and suctions confirmed this assertion.

7 REFERENCES ASTM, D. (2005). D4318-Test Method for Liquid Limit." Plastic Limit, and Plasticity Index of Soils. Benson, C. H., Zhai, H., & Wang, X. (1994). Estimating hydraulic conductivity of compacted clay liners." Journal of geotechnical engineering 120(2): 366387. Bussière, B. and M. Guittonny (2020). Long-term evolution of reclamation performance. hard rock mine reclamation: from prediction to management of acid mine drainage. Daniel, D. E. and C. H. Benson (1990). Water contentdensity criteria for compacted soil liners. Journal of Geotechnical Engineering 116(12): 1811-1830. Das, B. M. (2009). Principles of Geotechnical Engineering, - SI Version. Stanford, CT: Stanford, CT: Category: Cengage Learning Publisher, p. 544. Eigenbrod, K. (2003). Self-healing in fractured fine-grained soils. Canadian Geotechnical Journal 40(2): 435449. Granados, A., Maqsoud, A., Belem, T., Viger, M-E (2022). Évaluation sur le terrain de la performance des recouvrements à base d’argile. Conférence Canadienne de Géotechnique, GéoCalgary. Maqsoud, A., Bussière, B., Mbonimpa, M. (2021). Low saturated hydraulic conductivity covers. Hard Rock Mine Reclamation: From Prediction to Management of Acid Mine Drainage; Bussière, B., Guittonny, M., Eds. Marcoen, J. M., Tessier, D., Thorez, J., Monjoie, A., & Schroeder, C. (2000). Manuel relatif aux matières naturelles pour barrières argileuses ouvragées pour CET (centres d'enfouissement technique) et réhabilitation de dépotoirs en région wallonne. McCarthy, D. F. and D. F. McCarthy (1977). Essentials of soil mechanics and foundations, Reston Publishing Company Virginia. Merzouk, A., Maqsoud, A., Belem, T., , Viger, M-E (2022). Évaluation de la performance des Amendement Des Matériaux Argileux comme barrière aux fluides dans un contexte de recouvrement minier. Conférence Canadienne de Géotechnique, GéoCalgary. Othman, M., C. Benson, C. Chamberlain, and T. Zimmie (1994). Laboratory testing to evaluate changes in hydraulic conductivity of compacted clays caused by freeze-thaw: state-of-the-art. ASTM Special Technical Publication 1142: 227-227.

Design and construction of instrumented buried field columns to assess the environmental fate and transport of munition residues in the vadose zone in cold regions Marc-Alexandre Fillion, Richard Martel, Vincent Taillard, Thomas Robert, Luc Trépanier & Marco Boutin Institut National de la Recherche Scientifique (INRS), Quebec City, Quebec, Canada Vincent Boulanger-Martel Research Institute on Mines and the Environment (RIME) - Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada Marie-Claude Lapointe Defence Research and Development Canada (DRDC), Quebec City, Quebec, Canada ABSTRACT Environmental problems resulting from military activities have been identified worldwide, increasing the importance of environmental fate and transport studies munitions residues. For this purpose, an infrastructure composed of twelve instrumented buried field columns was constructed to assess the mobility of propellant and explosives in unsaturated soils. Such a setup has the advantage of monitoring the complex snow-ground-infiltration interactions observed during the spring and assessing their impacts on contaminant transport. It was instrumented to assess the materials’ thermo-hydrogeological behaviour, the total dissolved mass of contaminants as well as their fate and transport in unsaturated soils. This study will provide insights into the water and mass flows of munition residues in cold regions soils. The proposed infrastructure will allow studies to be carried out on other emerging contaminants in different climatic and hydrogeological settings. RÉSUMÉ Des problématiques environnementales résultant d'activités militaires ont été identifiées dans le monde entier, ce qui accroît l'importance d'études sur le devenir et transport environnemental des résidus de munitions. À cette fin, une infrastructure composée de douze colonnes enfouies et instrumentées a été construite afin d'évaluer la mobilité des propulsifs et des explosifs dans des sols non saturés. Cette installation a l'avantage de permettre le suivi des interactions complexes entre la neige, le sol et l'infiltration observées au printemps et d'évaluer leur impact sur le transport des contaminants. Elle est instrumentée afin d’évaluer le comportement thermo-hydrogéologique des matériaux, la masse totale dissoute de contaminants ainsi que leur devenir et transport dans des sols non saturés. Cette étude permettra de mieux comprendre les flux d'eau et de masse des résidus de munitions dans des sols de régions froides. L'infrastructure proposée permettra de réaliser des études sur d'autres contaminants émergents dans différents contextes climatiques et hydrogéologiques.

1

INTRODUCTION

Over the past 30 years, environmental contamination concerns have been identified worldwide in connection with the use of military munitions, sometimes resulting in large plumes of contaminants in aquifers (e.g., Clausen et al. 2004; Bordeleau et al. 2008; Filipovic et al. 2015). A common practice performed by trained military personnel is the demilitarisation of ammunition. It is the process by which the lethal nature of weapons and ammunition is neutralised. This practice is commonly achieved by either open burning or open detonation, which are efficient techniques (Diaz et al. 2017), but can generate unexploded ordnance. The generated residues are found as particles of varying size on and within receiving soils (Thiboutot et al. 2012) and are prone to dissolution by precipitation, facilitating their transport to water bodies, such as streams, rivers, lakes and groundwater (e.g., Lewis, 2007; Jenkins

et al. 2008; Won & Borden, 2017). Fine particles can also be transported by wind (Lapointe et al. 2017). Therefore, it is important to study the environmental fate and transport of munition residues generated following the demilitarisation of military items to prevent potential environmental and human health risks (Ryu et al. 2017; Skalny et al. 2021). The impacts of seasonal freezing and thawing on soils are numerous and can include changes in their hydraulic, thermal, mechanical, biological, chemical, and physical properties (Sun et al. 2021). Freeze-thaw cycles can lead to the creation of preferential pathways in soils, thus modifying their hydraulic conductivity and impacting their capacity for adsorption of components due to changes in the soil’s bio-thermo-hydro-physicochemical properties (e.g., Qi et al. 2006; Sun et al. 2021). A potent assessment of water balance components is needed to quantify contaminant transport processes and to

help understand the occurring natural processes. However, most available laboratory techniques do not accurately quantify the effects of seasonal freezing and thawing on the water balance and mass transport of contaminants, whereas large-scale field experiments lack the required level of control on experimental parameters. Among these experimental methods, Martel et al. (2020) conducted insulated outdoor column tests assessing the fate and transport of PAX-48 explosive formulation. Temperatures, water contents and contaminant transport were monitored under natural environmental conditions (Martel et al. 2020). This study was conducted using stainless steel polytetrafluoroethylene (PTFE)-lined columns mounted on wooden benches. One of the main downfalls of this experimental setup was that preferential thawing of the material was observed along the column’s walls, which created preferential thawing and water flow during melting, thus impacting contaminant transport. To overcome the limitations from the Martel et al. (2020) experiment, an infrastructure composed of twelve instrumented buried field columns was constructed and will be used to assess propellant and explosives mobility in unsaturated soils sampled from a destruction site. Such a setup allows for the monitoring of complex snow-groundinfiltration interactions that occur during the spring and assess their impacts on contaminant transport. This paper presents the design, construction and installation of the instrumented buried field columns implemented at CFB Valcartier (Quebec City, Quebec). 2 2.1

MATERIALS AND METHODS Equipment and Instrumentation

Prior to the design and construction of the instrumented buried field columns, various equipment and materials were acquired. The targeted equipment and materials were selected to study various types of contaminants of concern, such as trace metals, pesticides, explosives, and pharmaceutical and personal care products. Equipment and instrument selections were based on the applied cases of Lewis et al. (2009), Lewis and Sjöstrom (2010), Bordeleau et al. (2014) and Martel et al. (2020). 2.1.1

Stainless Steel 316 Columns

Columns used for this infrastructure are made of stainless steel 316 (SS316). They have a capacity of 209 liters, have an interior diameter of 18'-1/4'' (46.35 cm) and a height of 49'-1/4'' (125.09 cm). The bottom of each barrel has a ¾'' diameter SS316 stainless steel male spigot welded to the centre with a SS316 female-female elbow connected to it. The spigot is coupled to a “tube to pipe” connector for the connection of ½ outer diameter PTFE tubing. To minimize interaction between the contaminants of concern and the column, a PTFE coating was applied to the inner surface of each column, as inspired by previous studies (Lewis et al. 2009; Martel et al. 2020). 2.1.2

Concrete Tank

A 7.5 m long, 2.7 m wide and 2.1 m high prefabricated concrete tank was manufactured to accommodate the sampling apparatus. The tank has a concrete slab equipped with an access hatch and a staircase. A guardrail built into the access hatch was also installed for security purposes. This configuration allows autonomous access to the sampling apparatus. 2.1.3

Monitoring System

To evaluate the material’s thermo-hydrogeological behaviour, some parameters such as temperature, precipitation, water content and solar radiation will be monitored continuously throughout the year. Therefore, a data acquisition system was acquired from OnSet® HOBO®. The equipment can be connected either by cable or remotely to an on-site remote monitoring station (RX3000 station). The monitoring station is equipped with a solar panel and a cellular connection, and it is mounted on a weather station mat tripod to ensure stabilization. Such configuration allows the experiment to run remotely and to signal alarms when an on-site intervention is required. Besides, it simplifies the data processing associated to the ongoing experiment by building a database via HOBOlink®, a remote monitoring software. Multi-depth probes (HOBO®, RXW-GP6-900) measuring temperature and soil humidity are used in control columns, whereas other probes are installed to monitor air (HOBO®, RXW-THC-900) and surface soil (HOBO®, S-TMB-M017) temperatures throughout the site. Pyranometers (HOBO®, RXW-LIB-900) are mounted up and down the weather station to assess the solar radiation emitted from the atmosphere and the surface soils. A rain gauge (HOBO®, RGB-M002) and sonic snow thickness sensor (JUDD, DS-RS) were installed to evaluate annual precipitation. Tipping buckets (METER®, KIPP100) will be used as a multi-increment sampling apparatus and to measure water infiltration in each column. Additionally, results obtained can be used to evaluate water and contaminant mass balances throughout experiments. 2.2

Laboratory Development

Prior to site installation, improvements to the setup were necessary to make the columns easily reusable and compliant to the experimental conditions. Hence, the columns internal design had to be adapted to simulate the presence of an unsaturated zone and to limit the presence of a capillary fringe. Fibreglass wicks were installed within the bottom cap to manage infiltrated water drainages percolating through the buried soil columns. Such a system has proven to be effective at controlling the position of the water table in soil columns (e.g., Lewis et al. 2009; Bordeleau et al. 2014; Martel et al. 2020). Implementing fibreglass wicks into the soil columns, while also guaranteeing the use of materials that are both reusable and chemically inert, presents a significant challenge. Thus, supports made of high-density polyethylene (HDPE) panels and PTFE plates were designed. HDPE panels are used as supports to uphold the plate and the soil column weight. PTFE plates were designed with a proper sealing method to ensure that

infiltration water does not flow along the column’s walls. PTFE plates are perforated in a hexagonal pattern and have an exit hole in the center. Wicks are strategically positioned in a specific pattern (i.e., following the plates pattern) on the plate surface to facilitate infiltrated water drainage. By reducing the capillary pressure within the soil columns, the wicks draw out the effluent and help maintain unsaturated conditions. The use of supports within the columns allow the soil's backing and the use of fibreglass wicks, while making cleaning easier between experiments. 3

3.1

CFB VALCARTIER INSTRUMENTED BURIED FIELD COLUMNS – DESIGN AND CONSTRUCTION Site Selection

The site chosen for this project is in CFB-Valcartier (46°54′10″N 071°30′13″W), located in Quebec City. This site was selected because: • The water table is deep enough to avoid any problems that may arise from burying the concrete tank and installing the soil columns, in which the base must constantly be above the water table. A water table level logger (Solinst®, Levelogger® 5, M05) was installed in an observation well on-site to monitor the water table fluctuation yearly. • Soil observed on-site is a coarse sand with gravel which allows for easy machine operation and ensures that the soil surrounding the columns is well drained. • CFB-Valcartier is subjected to a humid continental climate with a temperate summer. This climate is characterised by a long and cold winter that can last from 4 to 6 months, with abundant precipitation, including both rain and snow, and an annual temperature variation between -35 and 35°C. 3.2 3.2.1

temperature of 7°C, which represents the average ground water temperature in the area. Surface boundary conditions were also developed based on the site’s average air temperature function and representative nfactors for sand and concrete (Andersland and Ladanyi 2004). Good practices in numerical modelling were also followed, which requires assessing the impact of domain and mesh sizes as well as time steps on the obtained results. This step involved performing four model runs with varying mesh (0.1 vs. 0.05 m) and domain sizes (10 vs. 12 m) as well as time steps (0.1 vs. 0.05 d). Figure 1A shows the temporal evolution of modelled temperatures at selected distances from the excavation and concrete tank wall at a depth of 1 m.

Experimental Design and Instrumentation Determination of Optimal Distance Between the Columns and the Concrete Tank

In order to limit heat disturbance and interactions between the buried columns and the concrete tank containing the sampling apparatus, thermal analyses were performed to establish the optimal distance between these two structures. Thermal analyses were performed with the TEMP/W module in GeoStudio 2021.3 software suite (version 11.2.2.23210, GeoSlope International Ltd.), a finite element software for modelling heat transfer and phase changes in solids, porous media and other geomaterials. Thermal analyses were performed using typical physical (i.e., porosity), thermal (i.e., thermal conductivity and heat capacity) and hydraulic (i.e., volumetric water content with respect to the elevation above the water table) properties for sand and concrete. Material properties were selected based on field conditions. Thermal analyses were developed with boundary conditions also representing in-situ conditions. The bottom boundary condition was at a constant

Figure 1. (A) Thermal analyses to assess the impact of excavation on heat exchanges and (B) Results of the sensitivity analysis The results indicated that the influence of the excavation and concrete tank on the thermal regime within the sandy soil becomes limited at distances greater than 5.0 m. Moreover, the sensitivity analysis showed that the numerical results were stable and of high quality (Figure 1B). Based on these results, the buried columns were installed at 5.0 m from the concrete tank. 3.2.2

Installation Design

Figure 2 shows a sketch (not to scale) illustrating the major components of the installation. It demonstrates the twelve buried soil columns, the concrete tank, and the monitoring system. The cross-section shows a schematic of the PTFE tubing that connects the buried column to the tipping bucket. The installation is arranged in three “clusters” of four columns granting the ability to study three contaminants of concern simultaneously. Every column has a specific function. One in each cluster is used as an entry function column to monitor the amount of precipitation going through the upper surface of the columns (like a rain gauge), and to monitor contaminant of concern input concentrations into the columns. These columns allow these measurements without the influence of soil. Another

Figure 2. Buried soil columns infrastructure scheme – Plan and sectional drawings column in each cluster will be used to monitor soil properties. These columns will be filled with clean soil and equipped with probes to evaluate the temperature and water content at different depths over time. In this case, no ammunition residues will be put on top as a source zone. At last, the two remaining columns in each cluster will be filled with clean soil and will contain the source zone of contaminants on top. All the columns will be connected to PTFE tubes subjected to a slope of 2-3% allowing the columns effluent transport to the water tipping bucket in the concrete tank. A volume of 1 to 2% of water that flows in the tipping bucket will be sampled periodically and recovered for chemical analyses. Probes for soil temperature and air temperature will also be installed close to the column setup. Multi-level probes will also be installed between the column set up and the concrete tank to measure the soil profile temperature and relative humidity for the purpose of freeze and thaw modelling. The rain gauge and snow thickness sensor will be put in place close to the column setup to refine the water balance evaluation. 3.3 3.3.1

area was levelled, and a MG-20 graded crushed gravel bed compacted. Subsequently, the concrete tank was installed on the gravel bed (Figure 3), and the zone secured before further work.

Field Experimental Laboratory Installation On-Site Site Preparation

Prior to columns installation, the site was excavated according to the engineering drawings produced (not displayed). Following the excavation, the concrete tank

Figure 3. Concrete tank installed on-site 3.3.2

Buried Soil Columns Installation

To accomplish the buried soil columns installation, six holes were drilled through the concrete tank, at predefined positions, to install the PTFE tubing (Figure 4A). A slope of at least 2% was levelled in the area between the columns and the concrete tank (Figure 4B).

Figure 4. (A) Drilled hole in the concrete tank and (B) Levelled area with a slope of at least 2% In order to ensure PTFE tubing protection throughout the area between the columns and the concrete tank, they were mounted and unrolled on 8-foot T-headers and covered with 1 ½ inch PVC pipes (Figure 5).

Figure 6. PVC pipes installation along the 2% sloped area The concrete tank holes hosting the pipes were sealed with a thermoplastic rubber-based sealant (Figure 7A) and the area backfilled and compacted (Figure 7B).

Figure 7. (A) Sealing of concrete tank holes and (B) Backfilling of the PVC pipes

Figure 5. Mounting and protection of the PTFE tubing PVC pipes containing the PTFE tubing were passed through the concrete tank and levelled to ensure a 2% slope (Figure 6).

Prior to the columns positioning, ground was levelled and settled at the desired depth. Following this operation, concrete blocks were positioned and levelled to raise the columns to allow clearance of the column elbows. Columns were then placed on top of the concrete blocks (Figure 8A) and connected to their corresponding PTFE tubing (Figure 8B).

Figure 8. (A) Positioning of the columns and (B) PTFE connection to their respective columns

PTFE tubing not covered by PVC pipes were covered by geotextile and soil without gravel. Following the PTFE tubing connection, the column area was backfilled to ⅓ of columns height (Figure 9A) in order to maintain them vertically before the first soil filling (Figure 9B).

Figure 9. (A) Backfilling near the columns and (B) Buried soil columns prior to soil filling 3.3.3

Monitoring System Implementation

The equipment described in section 2.1.3 were implemented on-site following the buried soil columns installation. Wooden planks inside the concrete tank were installed to support the tipping buckets (Figure 10).

BURIED FIELD COLUMNS This study aims to present scientists with a comprehensive methodology for a newly developed instrumented buried field columns facility, specifically designed to study the vadose zone in cold regions. It aims to encourage scientists to conduct more outdoor trials in controlled environments. This permanent infrastructure equipped with an online environmental monitoring system and twelve buried columns that can contain soil will be used for studying contaminant transport in cold climates, evaluating the impact of freeze and thaw on the contaminants mobility and their environmental fate and behaviour. Additional experiments may be conducted in such an infrastructure. Decontamination experiments may be performed directly on contaminated soils while the efficiency of reactive wall filled with amendments subjected to freeze and thaw may be studied. In parallel to the buried field columns implementation, detailed laboratory characterisations are being conducted on two different soils before their use in the columns. Those include physicochemical properties (e.g., total organic and inorganic carbon and cation exchange capacity) and hydraulic properties (e.g., thermal conductivities, water retention curves, hydraulic conductivities). The studied soils will be in contact with three different munitions residues. Their fate and transport in the soil will be studied over a period of at least one year. More information will be presented in future scientific communications. 5

ACKNOWLEDGMENTS

The authors would like to thank Directorate Land Environment Program Staff (DLEPS) (Dana Pantea) for the financial support of the research project. We thank Dana Pantea and Marie-Anne Lambert from DLEPS, as project managers, for their advice, help and technical support. We thank Defence Research and Development Canada (DRDC) scientist David Brochu for technical and logistical support. We thank Directorate Land Environment (DLE) (Justin Thomas) for the financial contribution to buy the column setup constituents and CFB-Valcartier range control for the access to the experimental site in the range training area. Jean-Marc Ballard from INRS is also thanked for his contribution in the laboratory and on field work. Figure 10. Wooden planks for tipping buckets support The concrete slab was perforated to allow the passing of tipping bucket cables through the concrete tank. A mat tripod was installed on the concrete slab to host the remote monitoring station and its solar panel. Additionally, two pyranometers, an air temperature probe and a rain gauge were fixed to the mat. Tipping buckets were connected directly to the station while communicating remotely with the other probes. The snow depth probe was installed on a designed arm connected to the mat tripod. 4

OUTLOOK ON THE USE OF INSTRUMENTED

6

REFERENCES

Andersland, O. B. and Ladanyi, B. 2004. Frozen ground engineering, 2nd ed., John Wiley & Sons, Hoboken, NJ, USA. Bordeleau, G., Martel, M., Schäfer, D., Ampleman, G. and Thiboutot, S. 2008. Groundwater flow and contaminant transport modelling at an air weapons range, Environmental Geology, 55: 385-396. Bordeleau, G., Martel, R., Drouin, M., Ampleman, G. and Thiboutot, S. 2014. Biodegradation of nitroglycerin from propellant residues on military training ranges, Journal of Environmental Quality, 43(2): 441-449.

Clausen, J., Robb, J., Curry, D. and Korte, N. 2004. A case study of contaminants on military ranges: Camp Edwards, Massachusetts, USA. Environmental Pollution, 129(1): 13-21. Diaz E., Thiboutot, S., Ampleman, G., Marois, A., Kervarec, M., Gagnon, A. and Martinet, A. 2017. Application of DRDC’s approach for the measurement of residues deposited on the surface soil from large-scale open burning-open detonation at CFAD Dundurn, A four-year study: from 2011 to 2015 (U), DRDC-RDDC-2017R085, Quebec City, QC, Canada. Filipovic, M., Woldegiorgis, A., Norström, K., Bibi, M., Lindberg, M., and Österås, A.-H. 2015. Historical usage of aqueous film forming foam: A case study of the widespread distribution of perfluoroalkyl acids from a military airport to groundwater, lakes, soils and fish, Chemosphere, 129: 39-45. Jenkins, T.F., Ampleman, G., Thiboutot, S., Bigl, S.R., Taylor, S., Walsh, M.R., Faucher, D., Mantel, R., Poulin, I., Dontsova, K.M. and Walsh, M.E., 2008. Characterization and fate of gun and rocket propellant residues on testing and training ranges, Engineer Research and Development Center, Vicksburg, MS, USA. Lapointe M., Martel R. and Diaz, E. 2017. A Conceptual Model of Fate and Transport Processes for RDX Deposited to Surface Soils of North American Active Demolition Sites. Journal of Environmental Quality, 46(6): 1444-1454. Lewis, J. 2007. The transport and fate of detonation residues originating from cracked unexploded ordnance in the vadose zone. Ph. D. thesis, Institut National de la Recherche Scientifique, Quebec City, Quebec, Canada. Lewis, J. and Sjöstrom, J. 2010. Optimizing the experimental design of soil columns in saturated and unsaturated transport experiments. Journal of Contaminant Hydrology, 115(1-4): 1-13. Lewis, J., Martel, R., Trépanier, L., Ampleman, G. and Thiboutot, S. 2009. Quantifying the transport of energetic materials in unsaturated sediments from cracked unexploded ordnance. Journal of Environmental Quality, 38(6): 2229-2236. Martel, R., Fillion, M.-A., Robert, T., Batailler, E., Gosselin, J.-S., Houle, K., Lévesque, R. and Boulanger-Martel, V. 2020. Environmental fate of the PAX-48 energetic formulation (small and intermediate laboratory tests), Institut National de la Recherche Scientifique, Centre Eau Terre et Environnement, Research Report R1911, Quebec City, Quebec, Canada. Qi, J., Vermeer, P.A. and Cheng, G. 2006. A review of the influence of freeze‐thaw cycles on soil geotechnical properties. Permafrost and periglacial processes, 17(3): 245-252. Ryu, H., Han, J. K., Jung, J. W., Bae, B., and Nam, K. 2007. Human health risk assessment of explosives and heavy metals at a military gunnery range. Environmental geochemistry and health, 29: 259-269.

Skalny, A.V., Aschner, M., Bobrovnitsky, I.P., Chen, P., Tsatsakis, A., Paoliello, M.M., Djordevic, A.B. and Tinkov, A.A. 2021. Environmental and health hazards of military metal pollution. Environmental Research, 201: 111568. Sun, B., Ren, F., Ding, W., Zhang, G., Huang, J., Li, J. and Zhang, L. 2021. Effects of freeze-thaw on soil properties and water erosion. Soil and Water Research, 16(4): 205-216. Thiboutot, S., Ampleman, G., Brochu, S., Poulin, I., Marois, A., and Gagnon, A. 2012. Guidance document: surface soils sampling for munitions residues in military live fire training ranges: Canadian protocol, Defence Research and Development Canada, Quebec City, QC, Canada. Won, J. and Borden, R.C. 2017. Laboratory column evaluation of high explosives attenuation in grenade range soils. Journal of Environmental Quality, 46(5): 968-974.

Hydraulic properties of crushed rockbentonite mixtures used as low permeability materials in mine waste confinement structures Yosra Hfaiedh, Vincent Boulanger-Martel and Bruno Bussière Université du Québec en Abitibi-Témiscamingue, Research Institute on Mines and the Environment, Rouyn-Noranda, Québec, Canada ABSTRACT This study aims to characterize and optimize the hydraulic properties of crushed-rock bentonite mixtures for potential use as construction materials in mine wastes confinement structures. Several permeability tests were performed to assess the effects of bentonite type, bentonite content, crushed rock grain-size distribution and freeze-thaw cycle on the saturated hydraulic conductivity (ksat) of crushed rock-bentonite mixtures. Tests results show that the ksat of the tested mixtures varied from 5.8×10-7 to 3.5×10-8 cm/s for bentonite contents ranging from 5.0 to 9.0%. Crushed rock-bentonite mixtures with higher bentonite contents and higher uniformity coefficients generally resulted in lower measured ksat. The impact of freezethaw cycles on the ksat of the tested mixtures was limited. The results of this study provide insights on the factors governing the ksat of crushed-rock bentonite mixtures and their potential use as low permeability materials in confinement structures. KEY WORDS : Hydrogeological Characterization, Mine Reclamation, Crushed Rock-Bentonite Mixture. RÉSUMÉ Cette étude vise à caractériser et à optimiser les propriétés hydrauliques des mélanges gravier-bentonite utilisés comme matériaux de construction dans les structures de confinement des déchets miniers. Plusieurs essais de perméabilité ont été réalisés pour évaluer les effets du type de bentonite, de la teneur en bentonite, de la distribution granulométrique du gravier et des cycles gel-dégel sur la conductivité hydraulique saturée (ksat) des mélanges du gravier-bentonite. Les résultats montrent que la ksat des mélanges varie de 5.8×10-7 à 3.5×10-8 cm/s pour des teneurs de bentonite de 5.0 à 9.0%. Les mélanges gravier-bentonite ayant des teneurs en bentonite plus élevées et des coefficients d’uniformité plus importants ont observé des ksat plus faibles. L’influence des cycles de gel-dégel sur ksat des mélanges est demeuré faible. Les résultats de cette étude permettent de mieux comprendre les facteurs régissant sur ksat des mélanges de gravierbentonite et leur utilisation potentielle comme matériaux à faible perméabilité dans les structures de confinement. Mots-clés: Caractérisation hydrogéologique, Restauration minière, Mélange gravier-bentonite. 1

INTRODUCTION

Soil-bentonite mixtures are commonly used as impervious materials in engineering construction. Typically, a combination of sand (Chapuis, 2002; Kenney et al., 1992) or, to a lesser extent crushed rock (e.g., Mata et al., 2005; Villar, 2006) is blended with bentonite to enhance the hydraulic characteristics of the resulting mixture. Bentonite has specific physical, hydraulic, structural, and chemical properties (i.e., high swelling capacity, low hydraulic conductivity, high water adsorption and cationic exchange capacities and high specific surface) that result in a low permeability and mechanically stable material once mixed within a granular matrix (Mitchell, 1993;Olsen and Daniel 1981).Thus, soil-bentonite mixtures are often used for the construction of dam cores, cover systems for municipal landfills, and impervious liners for confinement of various hazardous waste, or as buffer and backfill materials for underground isolation of nuclear wastes (e.g., Chapuis 1981; Chapuis et al., 1990a, 1990b; 1992; Lundgren 1981; Abeele, 1986; Komine et al., 2008; Tripathi, 2006; Guo et al., 2010).

In mine waste confinement structures such as tailings dams and engineered covers for the reclamation of tailings and waste rock storage facilities, fine-grained materials are usually required to build a barrier between the reactive waste and the environment. While soil-bentonite mixtures haven’t been used widely for mine waste confinement applications, they offer an interesting alternative, especially when natural fine-grained soils are not available close to the construction site (Boulanger-Martel et al., 2014; 2016). This is particularly the case for remote and Arctic mine sites, for which the occurrence of natural fine-grained materials deposits can be limited. Such operations also usually produce crushed rock materials that are used for construction and maintenance purposes associated with transport infrastructures (i.e., roads and airstrips: Boulanger-Martel et al., 2021). In this context, crushed rock-bentonite mixtures represent a promising option.

One of the key hydraulic properties required for the design of such mine waste confinement structures is the saturated hydraulic conductivity (ksat). Several studies have been carried out on sand-bentonite mixtures used as low permeability materials (e.g., Chapuis 1990, 2002b; Kenney et al., 1992; Komine and Ogata, 1999), however, only a few studies, thus far, have focused on the hydraulic properties of crushed rock-bentonite mixtures. This study aims to characterize and optimize the hydraulic properties of crushed-rock bentonite mixtures for potential use as construction materials in mine waste confinement structures. This article first presents the material preparation procedure as well as the basic physical and geotechnical properties of the tested materials. Then, several permeability tests were performed to assess the effects of bentonite type, bentonite content, grain-size distribution of the crushed rock, and the influence of freeze-thaw cycles on the ksat of crushed rockbentonite mixtures. Finally, this study provides insights on the ability of existing models to predict the ksat of crushed rock-bentonite mixtures. 2 2.1

MATERIALS AND METHODS Materials

The soil-bentonite mixtures tested in this study were made from two different crushed rock materials and two powdered bentonites. The first crushed rock (identified as CR1) originated from an active mine site and is currently used as a construction and maintenance material for the mine’s transport infrastructures. CR1 was sampled directly from the mine’s stockpile into 15-25L buckets. Materials were then manually homogenized, oven dried and, re-separated into 15 subsamples. A commercially available crushed rock (identified as CR 2) was also used for some of the tested mixtures. Two commercially available powdered Wyoming sodium bentonites were used as constituents of the crushed rock-bentonite mixtures: the Big Horn® 200 (identified as B1) from Wyo-Ben and the Premium gel (identified as B2) from American Colloid Company. Powdered bentonite was chosen to facilitate mixing and dispersion in the soil pore space. Several crushed rock-bentonite mixtures were prepared, with bentonite contents ranging from 5.0 to 9.0% by weight. Such bentonite contents fall within the practical range for waste containment structures. The selected bentonite contents are sufficiently high to prevent flowinduced washing of the bentonite while remaining within the optimal range for controlling water seepage. Within the spectrum of bentonite contents spanning from 5.0% to 15.0%, ksat typically increases in proportion to the bentonite content (Chapuis 1990a However, for greater bentonite contents, ksat stabilizes and there is practically no further gain in increasing the bentonite content. The optimal bentonite content is the lowest amount that satisfies the hydraulic design criterion.

Each crushed rock-bentonite sample was prepared by mixing dry crushed rock with bentonite. Then, water was added to the mix to reach a gravimetric water content of 10% corresponding to degree of saturation greater than 90%. The mixture was then manually homogenized. A curing time of at least 48 hours was allowed for bentonite hydration before the permeability test was initiated. The liquid used for wetting, hydration, saturation, and permeation of the samples was tap water. 2.2

Basic physical and geotechnical characterizations

Before performing the various permeability tests, several techniques were used to characterize the basic physical and geotechnical properties of the materials. The grain size distribution (GSD) of the bentonites was determined using laser diffraction techniques (with a Mastersizer S analyzer from Malvern Instruments) in air (Merkus 2009). The GSD of crushed rocks was obtained through sieving for particles  80 μm (ASTM, D422; ASTM, 2007) and laser diffraction for particles  80 μm. the specific surface (S.S) of the bentonites was determined using The BET (Brunauer, Emett and Teller) method and methylene blue using the LC-21-255 method (BNQ, 2013). The specific gravity (Gs) of the bentonites and crushed rocks was measured using ASTM standard D5550-14 (ASTM, 2014). The free swell void ratio of the bentonites was determined using ASTM D5890 standard (ASTM, 2019). The plastic index (PI) of the bentonites was obtained by determining the liquidity limit (LL) and plasticity limit (PL), as described in ASTM D4318 (ASTM, 2017). The liquid limit was assessed with a cone penetrometer using standard method A of ASTM D4318 (ASTM, 2017). Modified Proctor compaction curves were also obtained for the crushed rock-bentonite mixtures at 6.5% bentonite following standard method C of ASTM D1557 (ASTM, 2012b). 2.3

Saturated hydraulic conductivity tests

Saturated hydraulic conductivity tests were performed in flexible wall permeameters following test method B (falling head with a fixed tailwater elevation) of ASTM D5084 (ASTM 2010). All test specimens were prepared from cured crushed rock-bentonite and compacted in a mold at a targeted porosity of 0.25. Compaction was achieved with a Proctor hammer with the energy required to reach the targeted dry density considering the initial water content of the mixture (10.0%). Saturation of the specimens was performed by circulating deaired water from bottom to top. Permeation was conducted for 240 to 360 hours at a back pressure of 25kPa. Permeability tests were performed every 24 to 48 hours to track changes in ksat associated with saturation and hydration processes. Saturation and hydration were considered achieved when three successive ksat values were stabilized within 1% of each other. ksat was determined by performing three permeability tests at hydraulic gradients of about 20, 30 and 40.

2.4

Intrinsic factors of influence on ksat

Figure 1 presents the test matrix that was followed to assess the influence of bentonite content, bentonite type, grain-size distribution of the crushed rock, and the influence of freeze-thaw cycles on the ksat of crushed rockbentonite mixtures. The influence of bentonite content on ksat was assessed by performing permeability tests on crushed rock-bentonite mixtures made with CR1 and 5.0, 6.5, 7.0, 8.0, and 9.0% B1 (Figure 1). The effect of bentonite type on ksat was evaluated by comparing the ksat values obtained for mixtures made of CR1-6.5% B1 and CR1-6.5% B2. Results were also compared to those of Boulanger Martel et al. (2016) obtained for a CR1-bentonite mixture at 6.5%. BoulangerMartel et al. (2016) tested mixtures made with a PDSCo Grout sodium powdered bentonite (identified as B3; Figure 1). Additional permeability tests were performed on samples composed of CR2 and B1. Results obtained for CR1 and CR2 at 5.0, 7.0 and 9.0% B1 were then analyzed to highlight the impact of the GSD of crushed rock on ksat (Figure 1).

2.5

Influence of freeze and thaw cycle

All tested crushed rock-bentonite mixtures were submitted to cyclic freeze-thaw cycles permeability tests (Figure 1), as described by Boulanger-Martel et al. (2014; 2016). Samples were housed in a specifically designed freezethaw insulating mold that aims to simulate one-dimensional freezing and thawing. The top portion of the samples were also covered to prevent samples from drying. Freezing was achieved by placing the setup into a freezer for 48h. Freezing temperature was –26°C, and thawing was performed at room temperature (22.1°C on average). Permeability tests were performed after 1, 3, 5, and 10 freeze-thaw cycles. 2.6

Ability of existing models to predict Ksat

The material’s basic physical and geotechnical properties were used to assess the ability of selected predictive models to estimate the ksat of the tested crushed rockbentonite mixtures. Models specifically designed for soilbentonite mixtures, such as those of Tripathi (2013) (Eq.1), Sivapullaiah (2000) (Eq.2) and Chapuis (1990) (Eq.3 and Eq.4), were investigated. The Mbonimpa et al. (2002) (Eq.5) model was also tested. The interested reader can refer to the specific reference for details on the selected predictive models.

Figure1. Matrix of crushed rock-bentonite mixture permeability and freeze-thaw tests.

k sat =

1.30×10−8 e2f

×(

Gb ×γw x×γd

−(

1−x

Gb

x

Gs

)

2

− 1) × (1 −

(1−x)γd Gs γw

) [1]

where ksat : saturated hydraulic conductivity (cm/s); ef :bentonite swell ratio ; Gb : specific gravity of bentonite; Gs : specific gravity of crushed rock ; γw : water density (kN/m³ ); γd : dry density of mixture( kN/m³ ) ; and x: ratio of bentonite content to crushed rock content in the mixture.

log10 k =

e−0,0535×LL−5,286

[2]

0,0063×LL+0,2516

equilibrium after about 360h. Such behavior is a result of the bentonite saturation and hydration process. The slow swelling of bentonite within the crushed rock matrix gradually reduces the pore space available to water flow which results in an overall decrease in ksat. This behavior has been witnessed by several authors who showed that the time required for saturation of a soil-bentonite mixture varies from 240 to 360 hours using the flexible wall permeameter (e.g., Chapuis 1990; 2002). The degree of saturation of all samples was verified based on mass-tovolume relationships measured at dismantling.

where e : bentonite void ratio; and LL : liquidity limit of bentonite. [3]

Log k préd = 20 × (n∗préd − 0.45) n∗ = ncrushed rock (Sr = 100%) −

2Vb Vs

[4]

where ncrushed rock : porosity of crushed rock ( base material); Vb : volume of bentonite in the mixture (cm³) ; Vs: volume of soil (cm³).

k sat = Cp ×

γw μw

×(

e3+x 1+e

)×

1 ρ2s ×LL2χ

Figure 2. Evolution of ksat with permeation time for crushed rock-bentonite at bentonite content of 9.0%.

[5] Table 1. Basic characterization results for bentonites

where ksat : saturated hydraulic conductivity (cm/s); Cp: a constant (5.6g²/m4); 𝛾𝑤 : water density kN/m³ (9.81 KN/m³) ; 𝜇𝑤 : water viscosity (𝜇𝑤 =10-3 Pa.s) ; s : density of mixture (Kg/m³) ; X: tortuosity coefficient (X=2); LL: liquidity limit (%) ;  : a constant between [0 ; 2] (in this equation =1.5); and e: void ratio of mixture. 3 3.1

EXPERIMENTAL RESULTS Basic characterization results

Tables 1, 2 and 3 summarize the characterization results obtained for the bentonites and crushed rocks. The GSDs show that the grain size distributions of B1 and B2 were similar and finer than B3. Table 1 also shows that the GSD of CR2 was more uniform (Cu = 6.5) than that of CR1 (Cu = 22.8). The free swell void ratios of B1 (ef = 30.50) and B2 (ef =32.5) were greater than that of B3 (ef = 11.32), which can be explained by the significant difference in their specific surfaces. Greater swelling is generally observed at greater specific surface area (Mitchell, 1993). 3.2

Saturation, hydration, and stabilization of ksat

Figure 2 shows an example of the evolution of ksat with permeation time. These results show that ksat decreased markedly in the first 100 permeation hours. Then the decrease in ksat was more gradual until it reached

Material

B1

B2

B31

Gs (-)

2.42

2.67

2.59

D10(m)

5.2

5.1

54

D30(m)

16.1

15.8

-

D60(m)

46.8

43.1

-

D80(m)

87.7

80.1

-

D90(m)

132

118

494.6

Cu (-)

9.05

8.39

6.02

S.S (m²/g)

475.02

-

18.65

LL (%)

352.5

239.1

149

PL (%)

29.4

38.5

-

PI (-)

323.1

254.6

-

ef (-)

30.5

32.5

11.3

1:

data from Boulanger-Martel (2016). Dx: w/w% of particles having a diameter less than Dx; Gs: specific gravity ; S.S : specific surface ; LL: liquidity limit; PI: plastic index; PL: plasticity index; ef: swell ratio; Cu : uniformity coefficient.

Table 2. Basic characterization results for crushed rock Material Gs (-) D10(m) D30(m) D60(m) D80(m) D90(m) Cu (-)

CR1 2.96 368 2380 8570 13600 16600 22.8

CR2 2.78 1750 7400 11300 12600 13300 6.5

Table 3. Modified Proctor curve for crushed rock – bentonite Mixture

Bentonite content (%)

Wopt (%)

d-max (g/cm³)

n (-)

CR1-B1

6.5

4.6

2.36

0.189

CR2-B1

6.5

6.3

2.29

0.169

CR1-B2 CR2-B2

6.5 6.5

4.4 6.4

2.46 2.28

0.163 0.178

CR1-B31

6.5

5.5

2.48

0.175

1:

data from Boulanger-Martel (2016).

3.3

Figure 3. Saturated hydraulic conductivity (ksat) as a function of bentonite content. The void ratio of bentonite in the mixtures eb strongly controls the ksat of soil-bentonite mixtures (Chapuis, 1990b; Kenney et al., 1992; Studds et al., 1998; Tripathi, 2013). The use of eb is a common way to quantify the ability of bentonite to control water flow. The value of eb for the mixtures can be calculated using Eq.6 (Kenney et al. (1992):

Factors controlling ksat 1

ρw

r

ρdm

1

The following sections aims to present the main experimental results highlighting the factors controlling the ksat of crushed rock-bentonite mixture.

eb = Gb [(1 + ) (

3.3.1

where dm is the dry density of mixture; r is the ratio of the dry masses of bentonite to that of the crushed rock and w is the density of water. Figure 4 shows the strong correlation that exists between ksat and eb. The lower eb, the lower ksat. These findings suggest that the seepage in such mixtures is primarily governed by the properties of bentonite, particularly its void ratio.

Effect of bentonite content on ksat

Figure 3 and table 4 present the evolution of ksat as a function of the bentonite content for CR1-B1 mixtures (also refer to Table 3). Results show that ksat decreased from 5.8×10-7 to 3.5×10-8 cm/s for bentonite contents increasing from 5.0 to 9.0%. The higher the bentonite content, the lower the ksat.

)–

rGg

− 1]

[6]

Table 4. Saturated hydraulic conductivity values of CR1B1 mixtures Bentonite content (%) 5.0 6.5 7.0 8.0 9.0

eb (-)

n (-)

ksat(cm/s)

5.52 4.26 3.96 3.47 3.09

0.25 0.25 0.25 0.25 0.25

5.76×10-7 2.9×10-7 9.5×10-8 6.5×10-8 3.5×10-8

Figure 4. Saturated hydraulic conductivity as a function of the void ratio of the bentonite in the mixture eb.

3.3.2

Influence of bentonite type on ksat

Table 5 shows that all measured ksat values for the CR1B1, CR1-B2, and CR1-B3 mixtures are within the same range. Considering that several other factors such as the method of mixing, the distribution of bentonite within the samples, the method of compaction, or the time of hydration or saturation of the mixture could also influence ksat, the obtained results are considered similar. The ksat values in the table 4 were obtained from crushed rock-bentonite mixtures made of bentonites with different properties: the GSD of B3 is coarser than B1 and B2 and the free swell ratios of B1 and B2 are almost twice that of B3. The obtained results suggest that all tested bentonites swelled enough to occupy the pore space and control water flow. Because the measured ksat values were similar and obtained from samples with similar eb values, it is deemed that there is no significant difference in hydraulic properties. However, tests at lower bentonite contents and higher eb values may yield a different behaviour. Table 5. Comparison between the ksat values of CR1- B1, CR1-B2 and CR1 – B3 mixtures Mixture CR1-B1 CR1-B2 CR1-B31

1:

Bentonite content(%) 6.5 6.5 6.5

eb(-)

ksat (cm/s)

4.26 4.93 4.18

2.9×10-7 4.0×10-7 2.6×10-7

data from Boulanger-Martel (2016).

3.3.3

Influence of the grain-size distribution of the crushed rock on ksat

Table 6 shows that the ksat values of the CR2-B1 mixtures ranged from 7.7×10-6 to 3.6×10-7 cm/s with bentonite contents ranging from 5.0 to 9.0%, whereas the ksat of the CR1-B1 mixtures ranged from 5.76×10-7 to 3.5×10-8 cm/s over the same range of bentonite content (as shown in Table 3). Overall, this suggests that the ksat values measured for CR2-B1 were approximately one order of magnitude larger than for CR1-B1. The difference in ksat is mostly explained by the difference in the uniformity coefficient of the two types of crushed rocks used in the mixtures (Figure 5). CR2 had a uniformity coefficient of 6.5, while CR1 had a coefficient of 22.8. These results indicate that the more uniform the GSD of the crushed rock, the higher the ksat of the crushed rock-bentonite mixture.

Figure 5. Saturated hydraulic conductivity (ksat) as a function of bentonite content for mixtures with CR1 and CR2. 3.4

Freeze-thaw tests results

The results of the freeze-thaw permeability tests are presented in Figure 6. These results indicate that the saturated hydraulic conductivities of mixtures containing CR1-B1 and CR1-B2 at a bentonite content of 6.5% increased by an order of magnitude after three to five freeze-thaw cycles, compared to their initial values. However, Boulanger Martel's (2015) results for the CR1-B3 mixture showed that the ksat at a bentonite content of 6.5% increased significantly after three to five cycles and reached 735 times its initial value (as shown in Figure 6). Based on these results, it can be observed that B3 was much more influenced by the freeze-thaw phenomenon than B1 and B2. Thus, the ksat of CR2-B1 mixtures at a bentonite content of 5.0%, 7.0%, and 9.0% were much more influenced by the freeze-thaw phenomenon than those of the CR1-B1 mixtures for the same bentonite content (Figure 7). It can be concluded that the grain-size distribution of the crushed rock has an influence on the resistance of crushed rock-bentonite mixtures on the effects of freeze-thaw cycles.

Table 6. Saturated hydraulic conductivity (ksat) values of the CR2- B1 mixtures Bentonite content (%) 5.0 7.0 9.0

eb (-)

ksat (cm/s)

5.85 4.19 3.27

7.7×10-6 8.7×10-7 3.6×10-7

Figure 6. Normalized ksat as a function of freeze-thaw for CR1-B1, CR1-B2 and CR1-B3.

4

Figure 7. Normalized ksat as function of freeze-thaw for CR1-B1 and CR2-B1 at bentonite contents ranging from 5.0 to 9.0%. 3.5

Prediction results

Figure 8 shows that all tested predictive models provided underestimated ksat values. The models of Chapuis (1990), Tripathi (2013), and Sivapullaiah (2000) are specific models used to predict the ksat of sand-bentonite mixtures. Generally, these models performed similarly and predicted ksat values one to two orders of magnitude lower than measured ksat. The KCM model was designed to compute the ksat of plastic soil (Mbonimpa et al. 2002), therefore, the KCM model underestimated the ksat of the tested crushed rock-bentonite mixtures by about five orders of magnitude. Overall, the tested models did not adequately predict the ksat of the crushed rock-bentonite mixtures. This is mostly attributed to the fact that these models were not designed nor validated to predict the ksat of such materials. Crushed rock-bentonite mixtures are composed of a solid crushed rock matrix filled by swelled bentonite. Such a structure results in a relatively low porosity material with a complex pore space in which water flow is governed by eb.

The results of this study indicate that the higher the bentonite content and the higher the uniformity coefficient , the lower the ksat. Such findings align with the results obtained by Renken (2006) and Boulanger-Martel (2015). This decrease in ksat values is explained by the increase in the proportion of soil occupied by bentonite. It indicate that the higher the uniformity coefficient, the lower the ksat.; these results are supported by several other studies (e.g., Chapuis1981; 1990a; Lundgren 1981). The results also demonstrated that the types of bentonites tested in this study did not affect ksat. However, compared to B1 and B2, B3 was shown to be significantly affected by freeze-thaw cycles. This is mostly attributed to the swelling properties of bentonites and differences in mineralogy (Boulanger-Martel et al. 2016). The use of crushed rock-bentonite mixtures as low permeability materials in mine waste confinement structures requires globally low ksat materials (ksat  10-7cm; Aubertin et al., 1995, 2002a). In terms of ksat, the results of this study suggest that most of the tested crushed rockbentonite mixtures represent promising construction materials for mine waste confinement structures. However, for the overall purpose of optimizing the hydraulic properties of crushed-rock bentonite mixtures for potential use as construction materials in mine wastes confinement structures, the CR1-B1 materials (and CR1-B2, to some extent) provided lower ksat values than the CR2-B1 mixtures at similar bentonite contents. The CR1-B1 and CR1-B2 mixtures were less impacted by freeze-thaw cycles than CR1-B3 and CR2-B1. Such results, suggest that, globally, CR1-B1 or CR1-B2 would represent better construction materials in cold climatic conditions. With the constantly increasing costs of powdered bentonite, optimizing the hydraulic properties of crushed rock-bentonite mixtures, and especially minimizing the bentonite content required to achieve a specific ksat is paramount for the use of such materials. In this respect, predictive ksat models are useful tools to help designers optimize the hydraulic properties of such materials. Several existing models for predicting the ksat of soil-bentonite mixtures and plastic soils have inconclusively been tested for crushed rock-bentonite mixtures. Based on the results of this study, predictive ksat models should consider the specific properties of crushed rock-bentonite mixtures – the properties of the solid matrix and the flow-controlling pore space occupied by bentonite. One prospective avenue to such development could be the introduction of a material function describing the void ratio of bentonite (e b) within crushed rock-bentonite mixtures in the KCM model (Mbonimpa et al., 2002). 5.

Figure 8. Predicted saturated hydraulic conductivity (ksat) as a function of measured saturated hydraulic conductivity.

DISCUSSION

CONCLUSION

This study aimed to characterize and optimize the hydraulic properties of crushed-rock bentonite mixtures for potential use as construction materials in mine wastes confinement structures. A series of permeability tests were conducted to evaluate how the type of bentonite, its proportion in the mixture, the grain size distribution of the crushed rock, and

the effects of freeze-thaw cycles impact the saturated hydraulic conductivity of crushed rock-bentonite mixtures. The results showed that, in general, crushed rockbentonite mixtures that contained higher quantities of bentonite and had higher uniformity coefficients exhibited lower measured ksat. The effects of freeze-thaw cycles on the ksat of the tested mixtures were minimal. Overall, the results suggest that crushed rock-bentonite mixtures are a promising alternative to natural fine-grained soils for the construction of mine wastes confinement structures. ACKNOWLEDGEMENTS This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC)-UQAT Industrial Research Chair on Mine Site Reclamation and by the Research Institute on Mines and the Environment (RIME UQAT-Polytechnique; http://www.irme.ca) and its industrial partners. The authors would also like to thank Justine Cheminal for her help during the preliminary steps of this work. REFERENCES Aubertin, M., Bussiere, B., & Chapuis, R. P. (1996). Hydraulic conductivity of homogenized tailings from hard rock mines. Canadian geotechnical journal, 33(3), 470-482. ASTM, D. (2007). Standard test method for particle-size analysis of soils. Boulanger-Martel, V. (2015). Performance d'une couverture avec effets de barrière capillaire faite de mélanges gravier-bentonite pour contrôler la migration d'oxygène en conditions nordiques, École Polytechnique de Montréal]. Boulanger-Martel, V., Bussière, B., & Côté, J. (2021). Resistance of a soapstone waste rock to freezethaw and wet-dry cycles: implications for use in a reclamation cover in the Canadian Arctic. Bulletin of Engineering Geology and the Environment, 80, 41-54 Boulanger-Martel, V., Bussière, B., Côté, J., & Mbonimpa, M. (2016). Influence of freeze–thaw cycles on the performance of covers with capillary barrier effects made of crushed rock–bentonite mixtures to control oxygen migration. Canadian Geotechnical Journal, 53(5), 753-764. Chapuis, R. P. (1990). Sand–bentonite liners: predicting permeability from laboratory tests. Canadian Geotechnical Journal, 27(1), 47-57. De normalisation du Québec, B. (2013). BNQ 2501-025. Sols–Analyse granulométrique des sols inorganiques. Kenney, T., Veen, W. V., Swallow, M. A., & Sungaila, M. (1992). Hydraulic conductivity of compacted bentonite–sand mixtures. Canadian Geotechnical Journal, 29(3), 364-374. Komine, H. (2010). Predicting hydraulic conductivity of sand–bentonite mixture backfill before and after swelling deformation for underground disposal of

radioactive wastes. Engineering Geology, 114(34), 123-134. Konrad, J.-M. (1989). Effect of freeze–thaw cycles on the freezing characteristics of a clayey silt at various overconsolidation ratios. Canadian Geotechnical Journal, 26(2), 217-226. Kraus, J. F., Benson, C. H., Erickson, A. E., & Chamberlain, E. J. (1997). Freeze-thaw cycling and hydraulic conductivity of bentonitic barriers. Journal of geotechnical and geoenvironmental engineering, 123(3), 229-238. Mbonimpa, M., Aubertin, M., Chapuis, R., & Bussière, B. (2002). Practical pedotransfer functions for estimating the saturated hydraulic conductivity. Geotechnical & Geological Engineering, 20(3), 235-259. Mitchell, J.K. (1993) Fundamentals of soil behavior. John Wiley & Sons Inc Ouyang, S., & Daemen, J. (1996). Performance of bentonite and bentonite/crushed rock borehole seals. Sealing of boreholes and underground excavations in rock, 65-95. Sällfors, G., & Öberg-Högsta, A.-L. (2002). Determination of hydraulic conductivity of sand-bentonite mixtures for engineering purposes. Geotechnical & Geological Engineering, 20, 65-80. Shepherd, R. G. (1989). Correlations of permeability and grain size. Groundwater, 27(5), 633-638. Sivapullaiah, P., Sridharan, A., & Stalin, V. (2000). Hydraulic conductivity of bentonite-sand mixtures. Canadian geotechnical journal, 37(2), 406-413. Soil, A. C. D.-o., & Rock. (2010). Standard test methods for measurement of hydraulic conductivity of saturated porous materials using a flexible wall permeameter. ASTM International. Standard, A. (2006). D2434-68, 2006. Standard test method for permeability of granular soils (Constant Head). ASTM International, West Conshohocken, PA. doi, 10, D0422. Studds, P.G., Stewart, D.I., Cousens, T.W. (1998) The effects of salt solutions on the properties of bentonite-sand mixtures. Clay Minerals 33, 651660. Tong, S., & Shackelford, C. (2016). Standardized hydraulic conductivity testing of compacted sand-bentonite mixtures. Geotechnical Testing Journal, 39(6), 1015-1029. Tripathi, K. (2013). Hydraulic conductivity prediction of saturated sand-bentonite mixtures. Geotechnical and Geological Engineering, 31, 581-591. Wong, L. C., & Haug, M. D. (1991). Cyclical closed-system freeze–thaw permeability testing of soil liner and cover materials. Canadian Geotechnical Journal, 28(6), 784-793.

Title – Preliminary Assessment of Antioxidant Depletion from a Polypropylene Coating on a Geosynthetic Clay Liner Samuel Makinde & Kerry Rowe Geoengineering Centre and Queen's-RMC, Department of Civil Engineering, Queen’s University, Kingston, Ontario, Canada ABSTRACT Multicomponent geosynthetic clay liners (MGCLs) often have a polypropylene coating about 200 g/m2 applied to a standard geosynthetic clay liner (GCL) to reduce the hydraulic conductivity and minimize premature hydration of the GCL. These MGCLs may be used in applications with design lives of 100 years or more. However, the service life of the coating is unknown. This paper described testing that is being conducted to evaluate the depletion of the antioxidants used to protect the polymer from thermal oxidative degradation. Samples of the impregnated coated geotextile (GTX) are immersed into a simulated municipal solid waste leachate aged at 65 oC, 75oC and 85oC. Based on the data available at the time of writing, the early depletion takes places rapidly within few days of oven-aging at these temperatures and the rate of antioxidant degradation at 75oC and 85oC temperatures appears to be similar but 2.5 times that of 65oC. There is need for more data to provide the Arrhenius relationship that will allow for the prediction of the time to antioxidant depletion at temperatures relevant to a particular landfill application, hence the critical stage in the service life of the coating component of a MGCL.

1

INTRODUCTION

Over the past four decades, GCLs have been used extensively in waste containment facilities as barrier systems against the leakage of harmful and toxic substances into groundwater and the environment (Egloffstein et al., 2013; Rowe 2012; Rowe 2020). GCL is manufactured by sandwiching bentonite between two geotextiles. Depending on the application, GCLs are used either as single liner or part of composite lining systems (Rowe 2020; MoECC 2012). When GCLs are in a geocomposite liner with a geomembrane (GMB), they minimize leakage of liquid and gas (Rowe 2020). The advantages of using a GCL include its self-healing feature due to the hydration of sodium bentonite in GCL, low hydraulic conductivity compared to a compacted clay liner (CCL) and ease of installation (Egloffstein et al., 2013). Problems that can arise with a GCL include root penetration, shrinkage, downslope erosion, and bentonite desiccation; all of which increase hydraulic conductivity and gas permeability with time (AbdelRazek and Rowe, 2016; Rowe, 2020). MGCLs were developed to address these problems. MGCLs are conventional GCLs bonded to a geofilm (Rowe, 2018). The geofilm can be polyethylene (PE) or polypropylene (PP). Geofilms may be applied as a molten plastic coating or a plastic film glued, or heat bonded to the GCL (Rowe and AbdelRazek, 2021). The coating in the former cannot be separated from the coated geotextile as it is embedded in the carrier geotextile while the laminate glued, or heat bonded to the GCL can be easily peeled from the rest of the GCL. There is ample evidence that MGCLs effectively address the problems with conventional GCLs. However, they are not well stabilized with antioxidants needed to achieve a long service life.

1.1

Background

MGCL and GMB are often used together as a composite liner for long term performance of MSW (Municipal Solid Waste) landfills, ponds for containing several types of fluids (e.g., brine ponds, sewage lagoons etc.) that must not escape to the environment, and in mining applications. The main issue with composite liner is leakage through holes in the GMB. Even though an intact GMB is practically impermeable to water, Giroud (2017) indicated that all liners leaks. The leakage depends on the number and size of holes (or wrinkles with holes) in the GMB before or after it has been covered with cover soil (Rowe 2018). The basic role of MGCL in a composite system is to reduce leakage through holes in the GMB (AbdelRazek and Rowe, 2016). The applications of MGCL are based on its installation, either the geofilm or coating is facing up or down (Egloffstein et al., 2013). If the geofilm is placed facing downward, the leakage through the composite liner will depend on the interface transmittivity between the nonwoven geotextile and GMB and the hydraulic conductivity of the bentonite in the GCL (Rowe 1998, 2004; Rowe et al., 2004: Rowe and Abdelatty, 2013). However, if the geofilm is installed facing up in a composite system with GMB, the leakage in the composite liner will be a function of GCL hydraulic conductivity and the interface transmittivity between the geofilm and the rest of the GCL (Rowe and AbdelRazek, 2021). In addition, Rowe and AbdelRazek (2021) reported that smooth-coated GCLs are preferred to textured-coated GCLs when the geofilm is in direct contact with the GMB in composite liners. At present there is no published data relating to the service life of the geofilm component of the MGCL. Thus, the aim of this paper is to investigate the oxidativeinductive degradation of the antioxidant in the propylene

coating of a multicomponent GCL at different temperatures and solutions of interests. 1.2

the specimen size (0.7-2.5mg) at 175oC based on ASTM D3895.

Objective

The objective of this study is to explore, for the first time, the performance of coated geotextile in municipal solid waste leachate (L3). 2

MATERIALS AND METHODS

Samples of coated carrier geotextiles were carefully separated from the coupons (size: 100 mm by 200 mm) of multicomponent GCLs. The coated carrier woven geotextile from the multicomponent GCL. The polypropylene coated carrier geotextile was used for the test because it is impracticable to separate the coating from the carrier woven geotextile without damage to the surface. Also, it is deeply impregnated in the coated GCL. The rate of depletion of these coated geotextiles were assessed using both Index and immersion tests. These samples were immersed in simulated municipal solid waste leachate at three different oven temperatures (65oC, 75oC and 85oC). 2.1

Materials Tested

The carrier woven geotextile of a lightly coated GCL was tested in this study. The GCL comprises bentonite sandwiched between coated woven carrier geotextile and nonwoven geotextile, thermally bonded and needle punched together. It has a nominal mass per unit area of coating is 200 g/m2. 2.2

Figure 1. HDPE (high density polyethylene) Ageing Stages (Hsuan and Koerner 1998; Zimmerman et al; 2019) 2.3

Immersion Test

The coupons of coated geotextiles were aged in four-litre glass jars placed in forced-air ovens set to 65oC, 75oC, and 85oC. These temperatures are necessary to speed up the aging process of the samples. Simulated municipal solid waste leachate, referred to as Leachate 3, was the medium of aging used for this test (Abdelaal et al. 2014; Reinert and Rowe, 2020). Specimens were taken from jars at regular intervals and tested periodically. The same index tests performed on the unaged/virgin samples were conducted on the aged samples to assess how a material changes over time and to allow an evaluation of the rate of degradation in engineering properties. Figure 2a and 2b show the pictures of the coated GCL when unaged (virgin) and aged in the leachate 3 solution respectively.

Index Test

Several environmental factors such as thermal stress, mechanical stress, temperature, moisture, ultraviolent radiation, atmospheric pollution, and microbiological activity can change the physical characteristics of materials. These changes in polyolefin geosynthetic materials can be measured over time with index tests to evaluate the corresponding effect on their engineering properties (Reinert and Rowe, 2020; Kay, Blond, and Mlynarek, 2004). Hsuan and Koerner (1998) and Zimmerman et al; 2019 described the service life of poly(alkene) or polyolefin in three distinct phases as illustrated in figure 1. The first phase (A) is the antioxidants depletion, followed by second phase (B) which is the induction time and the last phase (C) at which the polymer degrades. Differential scanning calorimeter (DSC) is employed to test for the oxidative induction time (OIT) for the chemical depletion of the antioxidant packages in the material. There are two types of OIT test- high pressure oxidative induction time (HP-OIT) and the standard oxidative induction time (Std-OIT). HP-OIT tests are done at high pressure ranging from 689-5516 kPa (Tikuisis, et al., 1993) and necessary to detect hindered light stabilizers (HALS) present in the antioxidant package (Zimmerman et al., 2019). Std-OIT tests were conducted on the virgin and aged samples of

Figure 2. (a) virgin sample and (b) Aged sample in leachate 3 solution

3

RESULTS AND DISCUSSION

Table 1 shows the summary of tests conducted on virgin coated carrier geotextile of the MGCL. Also, two peaks were expressed from the std-OIT tests done on the virgin samples as shown in table 1 as well as, aged materials. These number of peaks are the number of antioxidant packages expressed during the std-OIT experiment as shown in the thermogram in Figure 3. Table 1. Index Test on Virgin Coated Sample Property

Mean

Standard Deviation

Mass per unit area of 116.8 Coating (g/m2)

56.5

Std-OIT at Peak 1(min) Std-OIT at Peak 2(min) Maximum Force(kN/m) Elongation at maximum force(mm)

13.9 13.3 2.8 0.5

36.1 42.1 48.6 8.4

Ten virgin specimens are tested for the index property. Table 1 shows that the coated geotextiles have wider variability in properties than other geosynthetic materials due to the method of manufacture, needle punching method and level of quality control. Rowe (1993); Reinert and Rowe (2020).

During the aging process, the antioxidant package depletes with time. The Std-OIT data collected for the coated carrier geotextile aged in leachate 3 (L3) for over 70 days (about 2 and a half months) at three different temperatures (85oC, 75oC and 65oC). The decay curve is exponential and the OIT decay rate varies with incubation time (Silva, et al., 2021, Abdelaal et al; 2011 and Hsuan and Koerner, 1998). The Std-OIT results for the immersion in the leachate 3 solution are fitted using a three-parameter (first order) exponential decay (Silva, et al., 2021, Rowe and Abdelaal, 2016). The three parameters such as the initial OIT, depletion rate and residual OIT are used to estimate and describe the rate of change of the std-OIT as follows: [1]

𝑂𝐼𝑇𝑡 = 𝑎𝑒 −𝑠𝑡 + 𝑂𝐼𝑇𝑟

where 𝑂𝐼𝑇𝑡(min) is the OIT value at time t, 𝑎 (min) is the exponential fit parameter = 𝑂𝐼𝑇𝑜 – 𝑂𝐼𝑇𝑟), s (day-1) is the antioxidant depletion rate, t is the incubation time (month), and 𝑂𝐼𝑇𝑟(min) is the residual OIT value. Therefore Equation 1 becomes: [2]

𝑂𝐼𝑇𝑡 ∗ = 𝑂𝐼𝑇𝑜 ∗ 𝑒 −𝑠𝑡

Equation 2 is a two-parameter model. Taking the natural logarithm of both sides, Equation 2 is converted to a linear equation: [3]

ln(𝑂𝐼𝑇𝑡 ∗ ) = ln(𝑂𝐼𝑇𝑜 ∗ ) − 𝑠𝑡

Where, 𝑂𝐼𝑇𝑡 ∗ = 𝑂𝐼𝑇𝑡 − 𝑂𝐼𝑇𝑟 and 𝑂𝐼𝑇𝑜 ∗ = 𝑂𝐼𝑇𝑜 − 𝑂𝐼𝑇𝑟 60

85oC 75oC

Figure 3. Std-OIT Thermograph of virgin sample Figure 3 illustrates that the virgin material expresses a twopeak std-OIT thermograph. This reflects different components of antioxidant packages expressed in the virgin specimen. Some specimens have two peaks (Figure 3) but the majority of both virgin and aged specimens have one peak and correspond closely to the first peak (Figure 4).

Std-OIT(min)

50

65oC

40

30

20

10

0 0

20

40

60

80

Time(days) Figure 5. Plot of Std-OIT against aging time at different temperatures

Figure 4. Std-OIT Thermograph of Aged Specimen 3.1

Antioxidant Depletion Rate

Figures 6 7, 8 and 9 show the plot of ln(𝑂𝐼𝑇𝑡 ∗ ) against aging time at 85oC, 75oC and 65oC. The early depletion at temperatures of 85 oC and 75oC appears to be similar and presented in Figure 6. The antioxidant depletion rate in Figure 6 is about 0.1085 per day and 0.1044 per day for 85oC and 75oC respectively, while at 65oC, it is about 0.0385 per day (Figure 7). The time for the early depletion curve in temperature 85oC and

75oC is about 2.5 times greater than that of temperature 65oC. Figure 8 shows the rapid rate of antioxidant depletion of the coated geotextile aged in L3 at these temperatures (85oC, 75oC and 65oC). This fast chemical degradation rate may be associated to the thickness or mass per unit area of the coating on the carrier geotextile. The Std-OIT data of the coated geotextile might have reached its residual within 30 days. However, the wide range of Std-OIT values could suggest that the lightly coated geotextile may be poorly stabilized chemically and the uneven distribution of the coating impregnated in the carrier geotextile. 5

Ln(Std-OIT)

4

85oC 75oC

3

Chemical degradation occurs when the antioxidant starts depleting until it gets to stable state when the physical depletion sets in. The mechanical properties (such as tensile strength/stress and strain/apparent elongation) of the material are also affected (Zimmerman et al., 2019). 4

CONCLUSION

This is a preliminary assessment of the antioxidant depletion for a propylene coating in a multicomponent GCL at the time of writing this paper. Therefore, more tests are required to assess the performance of coatings multicomponent GCLs for MSW landfills. However, some conclusions can be made from this study tentatively: •

The residual Std-OIT for the coated carrier geotextile at 85oC and 75oC could have been within 30 days but it appears that at 65oC, the residual has not been attained yet. However, more data is needed to estimate the value.

•

The long-term assessment of this material involving lower temperatures (such as 55OC and 40OC) are necessary for better Arrhenius predictions of the service life.

•

The chemical aging of the material is not uniform due to high variability in the material, it is recommended that the thickness of the coating is increased and the antioxidant packages on the coating are well mixed and properly stabilized for uniform aging of the material.

2

1

0

0

20

40

60

Aging Time(days)

Figure 6. Semi-Log Plot of Std-OIT against Time for both 85oC and 75oC 5

Ln(Std-OIT)

4

65oC

3

This paper only reports the antioxidants depletion detected from the Std-OIT test. The HP-OIT is also tested and analyzed for functioning temperatures. Also, the mechanical properties of this coated GTX are being tested and monitored during stages II and III but will be discussed explicitly in the next paper.

2

1

0

-1

0

20

40

60

Aging Time(days)

Figure 7. Semi-Log Plot of Std-OIT against time at 65oC. 5

Ln(Std-OIT)

4

85oC 75oC

3

1

0

0

20

40

Aging Time(days)

This research is part of a broader study being conducted at Queen’s University with financial support from the Natural Sciences and Engineering Research Council and the inkind support of Terrafix Geosynthetics Inc. 5

65oC

2

-1

ACKNOWLEDGEMENTS

60

Figure 8. Semi-Log Plot of Std-OIT against Time at 85oC, 75oC and 65oC oven temperatures.

REFERENCES

Abdelaal, Fady B., R. Kerry Rowe, and M. Zahirul Islam. (2014). Effect of Leachate Composition on the LongTerm Performance of a HDPE Geomembrane, Geotextiles and Geomembranes, Elsevier, 42 (4): 348– 62. AbdelRazek, A., & Rowe, R. K. (2016). Interface Transmissivity of Multicomponent GCLs. In GeoChicago 2016 (pp. 719-729). ASTM D3895, 2014. Test Method for Oxidative-Induction Time of Polyolefins by Differential Scanning Calorimetry.

ASTM D 5035. Standard Test Method for Breaking force and elongation of textile fabrics (strip method), American Society for Testing and Materials, West Conshohocken, Pennsylvania, USA. [do not include year since standards are re-published every year). Barral, C., & Touze-Foltz, N. (2012). Flow rate measurement in undamaged multicomponent geosynthetic clay liners. Geosynthetics International, 19(6), 491-496. Egloffstein, T., Kalsow, J., von Maubeuge, K., & Ehrenberg, H. (2013). Multi-component geosynthetic clay liners: a product with new possibilities. In Current and Future Practices for the Testing of MultiComponent Geosynthetic Clay Liners. ASTM International. Giroud, J.P. (2017) – Design and performance of reservoirs lined with geomembranes. Széchy Lecture, February 2017, pp. 9-33. Kay, Dominique, Eric Blond, and Jacek Mlynarek. (2004). Geosynthetics Durability: A Polymer Chemistry Issue, 57th Canadian Geotechnical Conference/5th Joint CGS/IAH Conference, Quebec City, Quebec, 1: 1-14. Rowe, R. K. (1998). Geosynthetics and the minimization of contaminant migration through barrier systems beneath solid waste. Rowe, R.K., 2005. Long-term performance of contaminant barrier systems. The 45th Rankine Lecture, Geotechnique 55 (9), 631–678. Rowe, R. K., Islam, M. Z., & Hsuan, Y. G. (2008). Leachate chemical composition effects on OIT depletion in an HDPE geomembrane. Geosynthetics International, 15(2), 136-151. Hsuan, Y. G., and Koerner, R. M. (1998). Antioxidant depletion lifetime in high density polyethylene geomembranes. Journal of Geotechnical and Geoenvironmental Engineering, 124(6), 532–541. Reinert & Rowe (2020). Aging of Geotextiles used in Landfill Applications - an initial study. 4th Pan American Conference on Geosynthetic. 20th April 2020. Rio De Janeiro, Brazil. MoECC (Ministry of Environment and Climate Change). 2012. Landfill standards: A guideline on the regulatory and approval requirements for new or expanding landfilling sites. Toronto: Government of Ontario. Rowe, R.K., Quigley, R.M., Brachman, R.W.I., Booker, J.R., 2004. Barrier Systems for Waste Disposal Facilities. Taylor & Francis/Spon, London, U.K Rowe, R. K. (2012). Third Indian Geotechnical Society: Ferroco Terzaghi oration design and construction of barrier systems to minimize environmental impacts due to municipal solid waste leachate and gas. Indian Geotechnical Journal, 42(4), 223-256. Rowe, R. K., & Abdelatty, K. (2013). Leakage and contaminant transport through a single hole in the geomembrane component of a composite liner. Journal of geotechnical and geoenvironmental engineering, 139(3), 357-366. Rowe, R. K., & Hosney, M. S. (2015). Interface transmissivity and hydraulic conductivity of GCLs below

poured concrete. Geosynthetics International, 22(1), 48-69. Rowe, R. K. (2018). Environmental geotechnics: looking back, looking forward. Talian Geotechnical JournalRivista Italiana Di Geotecnica, 4, 8-40. Rowe, R. K. (2020). Geosynthetic clay liners: perceptions and misconceptions. Geotextiles and Geomembranes, 48(2), 137-156. Rowe, R.K., & Jabin, F. (2020). Factors affecting Multicomponent GCL-Geomembrane Interface Transmissivity for Landfills. Geosynthetics International, 1-54. Rowe, R. K., & AbdelRazek, A. Y. (2021). Performance of multicomponent GCLs in high salinity impoundment applications. Geotextiles and Geomembranes, 49(2), 358-368. Silva, R. E., Adbelaal, F. B., & Rowe, R. K. Antioxidant Depletion of a HDPE Geomembrane in ArsenicBearing Tailings. Tikuisis, T., Lam, P., & Cossar, M. (1993). High pressure oxidative induction time analysis by differential scanning calorimetry. In MOC/MOA and COC/COA of Geosynthetics, RM Kerner and YG Hsuan, Eds, GRI Conference Series, IFAI (pp. 191-201). Zimmerman, Z., Rowe, R.K. & Reinert, J. (2019). Ageing of a Geocomposite Drain used in landfills applicationsan initial study. Geo St. John’s 2019.

Sample development for fractured cementbased s/s research Hadi Matin Rouhani & Craig Lake Department of Civil and Resource Engineering – Dalhousie University, Halifax, Nova Scotia, Canada ABSTRACT Understanding the long-term contaminant migration performance of cement-based solidification/stabilization (S/S) systems from remediated sites is important for environmental professionals. Environmental loadings have the potential to induce long term microcracking of these systems, which introduces complex contaminant transport relationships between the matrix and the fractures. This paper describes the mix design of a S/S material for fractured system research. The mix design process describes preliminary strength tests on mortar cube size samples. A target mix design from this work is selected to develop a moisture density relationship for the soil-cement mix at different water contents (i.e. water-cement ratios). Results of flexible wall hydraulic conductivity testing of some of the water contents of the soil-cement mix are presented in the context of selection of a diffusion-dominated cement-based s/s mixture for future research. RÉSUMÉ Comprendre les performances de migration à long terme des contaminants des systèmes de solidification/stabilisation (S/S) à base de ciment à partir de sites assainis est important pour les professionnels de l'environnement. Les charges environnementales ont le potentiel d'induire une microfissuration à long terme de ces systèmes, ce qui introduit des relations complexes de transport de contaminants entre la matrice et les fractures. Cet article décrit la conception du mélange d'un matériau S/S pour la recherche sur les systèmes fracturés. Le processus de conception du mélange décrit des tests de résistance préliminaires sur des échantillons de la taille d'un cube de mortier. Une conception de mélange cible à partir de ce travail est sélectionnée pour développer une relation de densité d'humidité pour le mélange sol-ciment à différentes teneurs en eau (c'est-à-dire les rapports eau-ciment). Les résultats des tests de conductivité hydraulique à paroi flexible de certaines des teneurs en eau du mélange sol-ciment sont présentés dans le contexte de la sélection d'un mélange s/s à base de ciment dominé par la diffusion pour des recherches futures. 1

INTRODUCTION

The remediation of contaminated soils is both an environmental and an economic concern for many countries (Bates and Hills 2015; Hou and Li 2017; Song et al. 2019). Canada currently has about 24,000 potentially contaminated sites listed on the Federal Contaminated Sites Inventory (Government of Canada 2023). There are many different remediation technologies available to manage contaminated lands, each with advantages and disadvantages depending on the circumstance. Among these methods, solidification/stabilization (S/S) has been proven to be popular for decades (USEPA 2020). In S/S, the contaminated soil is mixed with a cementitious binder to make a final product that reduces the rate of contaminant migration from the material (ITRC 2011; Mulligan et al. 2001a, 2001b) and increases its strength (Bates and Hills 2015). Typical values of hydraulic conductivities of S/S treated materials are between 10-6 m/s and 10-10 m/s (Bates and Hills 2015). However, to ensure that contaminant migration is diffusion-controlled, a hydraulic conductivity of less than 10-9 m/s is required (Stegemann and Côté 1991). Typically required strengths for S/S materials will depend on the mix design employed for the soil improvement which often relates to the specific application. In S/S treatment applications a minimum strength of 0.3 MPa is generally required (Bates and Hills 2015).

There is limited research on the long-term performance of S/S materials (Glasser 1997; Klich et al. 1999; Li et al. 2014; Perera et al. 2005), particularly field performance (Bates and Hills 2015). Some (e.g. Shen et al. 2019) believe that S/S has lost its market in the recent decade due to its lack of predictability. Public perception is that the long-term performance of S/S systems can be adversely affected by fractures developed by mechanisms including chemical reactions, wet-dry (W/D), and freeze-thaw (F/T) cycling (Bone et al. 2004; EPRI 2003; ITRC 2011; Jolous Jamshidi and Lake 2014; Perera et al. 2005). Fracturing of S/S materials could result in increased rates of contaminant release from these materials into the surrounding environment, but the reality is that there is very little work on the performance of fractured S/S materials found in the literature. There are a variety of mathematical solutions to the equations governing the problem of contaminant migration through intact and fractured porous media (Neretnieks 1980; Rowe et al. 2004; Rowe and Booker 1989, 1990a, 1990b, 1991a, 1991b), yet little experimental work on fractured S/S materials. When dealing with low hydraulic conductivity porous media such as unfractured S/S materials, advection-dispersion aspects of the migration will be overshadowed by diffusion (Li et al. 2016; Shackelford 2014; Shackelford and Daniel 1991a, 1991b; Stegemann and Côté 1991; Willingham et al. 2004). However, with fractured systems, advection, dispersion and diffusion will all play a role in the contaminant

migration, but, unfortunately, there is a lack of research on contaminant migration from fractured S/S systems in the literature. The diffusive migration of contaminants from the intact portion of the S/S material, into the fracture, will be an important role in this understanding and hence it is critical that diffusive migration from S/S materials is better understood. In an ongoing research program at Dalhousie, the migration of contaminants from fractured soil-cement materials is being studied. The initial portion of this research involved developing a suitable soil-cement material for diffusion testing. The ideal properties of the soil-cement material for this research is that it exhibits a low hydraulic conductivity for the diffusion-controlled contaminant migration process to dominate. Such a low hydraulic conductivity sample also usually exhibits a very high strength (ACI 2003; Bone et al. 2004; ITRC 2011), which is not ideal for diffusion testing in this research as soil-cement samples must be sectioned (i.e. cut) in order to obtain the contaminant’s concentration profile along its depth (Goreham et al. 2012; Goreham and Lake 2013a, 2013b, 2018). This paper describes the procedure which was followed to find the appropriate mix proportions that provided a low hydraulic conductivity soil-cement material with adequate workability during mixing and strength of approximately 1 MPa to create a brittle material (i.e. a material that would be likely to fracture if subjected to sufficient environmental stresses). 2

MATERIALS AND METHODS

2.1

Materials

Percent Passing (%)

The soil used in this study was a glacial till obtained from a roadway cut, near Hubbards, Nova Scotia. Prior to all testing, the soil was screened on the 10 mm sieve. The grain size distribution of the soil is shown in Figure 1 (ASTM D6913/D6913M-17 and ASTM D7928-21). The soil had a specific gravity of 2.68, a liquid limit of 16%, a plastic limit of 13% and 42 percent passing the 75 μm sieve. The resulting Unified Soil Classification System (USCS) classification of the soil was silty sand (SM). The moisturedensity relationship for the soil sample was determined with ASTM D698-12R21. The soil’s maximum dry density (MDD) was 2050 kg/m3 and its optimum water content (OWC) was 10.1%. 100 75 50 25 0

2.2 2.2.1

1

0.1

0.01

0.001

0.0001

Particle Size (mm)

Figure 1- Grain size distribution of the soil sample

Mixing Method

The soil described above was initially air-dried for at least 7 days in the laboratory (~25% relative humidity). The required amount of cement (Portland cement Type 10) was added to the dry soil in a pan and mixed thoroughly using a large spoon until visual homogeneity was obtained. Then, the pre-determined amount of tap water was added to the mixture and thoroughly mixed. Details on sample preparation and curing are provided below for each of the specific tests performed. 2.2.2

Moisture-Density Relationship

The moisture-density test for the soil-cement was conducted according to ASTM D558/D558M-19 to assist with assessing workability of the resulting mixtures. 2.2.3

Compressive Strength Test

The Unconfined Compressive Strength (UCS) test is the most common test for measuring S/S-treated materials’ strength (ITRC 2011). However, to be able to test a wide range of mix proportions in a shorter time, compressive strength tests were conducted on 50-mm mortar cubes to obtain an idea of the relative strengths of the various mix designs. The mixtures were placed inside molds in three layers, generally in accordance with ASTM C109/C109M21. Drier samples were compacted by slight thumb pressure. Very wet samples were not compacted. The mixtures remained in the molds for 24 hrs to set, then cured at 21(±2) ºC for 7 days before being tested for strength. The rate of loading was 1 mm/min for all the tests performed in this study. 2.2.4

Hydraulic Conductivity Test

Standard proctor molds (ASTM D558/D558M-19) were used to prepare the hydraulic conductivity specimens. For the drier samples, compaction was conducted according to the ASTM D558 standard. When the high moisture content mixes did not allow compaction, 21 strokes of a tamping rod (Stegemann & Côté 1991) were applied to each layer. The highest moisture content sample was placed in the mold without using the tamping rod. Samples were cured for 2 weeks at 21±2 ºC. Hydraulic conductivity was performed in general accordance with ASTM D5048-16a “Method A – Constant Head”. An effective confining pressure of 138 kPa (20 psi) and a nominal hydraulic gradient of 28 were used during permeation with de-aired water. 2.3

10

Sample Preparation and Experimental Methods

Testing Plan

A two-tiered approach was developed to determine the ideal mix proportion for future fractured contaminant transport testing. Figure 2 shows a schematic of the process that was followed for this approach and Table 1 shows a listing of all the mix designs trialed in this testing plan.

The goal of Tier-1 was to determine the desired cement content. The main focus in this initial phase was on achieving an appropriate strength (i.e. ~ 1 MPa) to ensure a brittle material for future testing yet ductile enough for sectioning via sawing. After following the sample preparation techniques described in the previous section, the compressive strength of soil-cement specimens with cement contents of 7, 10, 15, 20, and 30 percent (with respect to the weight of the air-dried soil) was measured. The amount of water utilized in each mix is expressed as water-cement ratio in this Tier-1 (Table 1). After obtaining the desired cement content based on strength, the water content was refined in Tier-2 by performing a standard moisture-density test (ASTM D558/D558M-19) to obtain the maximum dry density (MDD) and optimum water content (OWC) of the soilcement mixture. Eight different moisture contents were then chosen for future strength and hydraulic conductivity testing; two below the OWC and six above the OWC. Sample preparation was as described in the previous section.

Tier-1 UCS tests on specimens with different cement contents

Determined the cement content based on desired strength (~1 MPa) obtained

Tier-2 Strength tests on specimens with the desired cement content but different water contents

Hydraulic conductivity tests on the specimens with desired cement content and different water contents

Determined the appropriate mix design

Figure 2- Two-tiered approach for the development of optimal mix design for future fractured contaminant transport testing. 3 3.1

RESULTS Tier 1

The results of the compressive strength tests on the 50 mm cube specimens are presented in Figures 3 and 4. Figure 3 presents strengths versus water-cement ratios, while Figure 4 presents the strength values versus water contents. Water content, in this paper, is defined as the ratio of the mass of water to the mass of dry soil (not the total mass of soil-cement mixture). As indicated, for cement contents of 7%, 10%, and 20%, increasing the watercement ratio (i.e. water content) results in an initial increase in the UCS followed by a decrease after achieving a maximum.

Table 1- Mix proportions of the soil-cement samples used for Tier-1 Specimen Designation C7W/C1 C7W/C1.5 C7W/C2 C7W/C2.5 C10W/C1 C10W/C1.5 C10W/C2 C10W/C2.5 C15W/C1 C20W/C0.5 C20W/C0.75 C20W/C1 C30W/C0.6

Cement Content (% with respect to the air-dried soil)

7 7 7 7 10 10 10 10 15 20 20 20 30

WaterCement Ratio 1 1.5 2 2.5 1 1.5 2 2.5 1 0.5 0.75 1 0.6

Water Content

(% of the airdried soil)

7 10.5 14 17.5 10 15 20 25 15 10 15 20 18

As shown in Figure 3, the soil-cement mixture with 10% cement content provided strengths above 1.0 MPa and below or equal to 2.8 MPa, for all water-cement ratios. As previously discussed, this 1 MPa nominal strength was deemed to generate a brittle enough specimen for future fractured contaminant transport research, yet not excessively strong to disrupt the cutting procedure which is a common part of contaminant transport testing. The soilcement mixtures with 7% cement and all water-cement ratios greater than 1.0 gave a strength above 1 MPa. This 7% cement content at a water-cement ratio of 1.0 is probably not sufficient to create bonds between the soil particles across the whole specimen’s structure. The soilcement mixture with 20% cement and a water-cement ratio of greater than 0.5 generated much high strengths (9.1 MPa, 4.2 MPa) but at a water-cement ratio of 0.5 it resulted in a strength of 0.9 MPa. In this case, the amount of water is likely not sufficient for adequate cement hydration and hence adequate bonding with the soil particles. The soilcement mixtures with cement contents of 15% and 30% produced high strengths of 7.1 MPa and 12.0 MPa for water-cement ratios of 1 and 0.6 respectively. These strengths would make them inappropriate for cutting specimens at the end of contaminant migration tests. The other point noted from this testing is that the sensitivity of strength developed for the 10% cement tests was less than that for the other tests, at least over the range of water-cement ratios tested. This was another factor in the determination of a cement content of 10% being chosen as the desired cement for Tier 2 evaluation. This would limit major variability in sample strengths if unintended mixing errors were unknowingly incorporated into the future testing program.

7% 10% 15% 20% 30%

10 8 6 4

Dry Density (Kg/m3)

Compressive Strength (MPa)

12

2 0

0

1

2

3

2100

Soil-cement

2050

Soil

2000 1950 1900 1850

0

Water-Cement Ratio

Compressive Strength (MPa)

Figure 3- Compressive strength of 50 mm cubes of soilcement mixtures with various cement contents versus the water-cement ratio 7% 10% 15% 20% 30%

10 8 6 4 2 0

0

10

20

30

Water Content (% of the dry soil)

Figure 4- Compressive strength of soil-cement mixtures with different cement contents versus the water content

3.2.1

15

20

25

30

Compressive Strength Results

Figure 6 displays the variation of the compressive strength with various water content of the soil-cement-water mixture for the samples for which the cement content is 10%. The compressive strength of samples with moisture contents of 15%, 20% and 25% (water-cement ratios of 1.5, 2 and 2.5 respectively) are almost equal to the values which were displayed in Figures 3 and 4. However, for the sample with a water content of 10% (i.e. the water-cement ratio of 1), the value which is shown in Figure 6 does not conform with the values which are reported in Figure 3 and Figure 4. This inconsistency can be attributed to the dryness of the sample, which makes it hard to assure that all the cubic samples are compacted with an equal amount of energy.

Tier 2 Moisture-Density Relationship

The results of the moisture-density relationship tests on the soil-cement samples with 10% cement content are shown in Figure 5. The MDD was approximately 2,027 kg/m3 and OWC was approximately 10%. The moisture-density relationship of the soil without cement is presented here for comparison. A comparison of the moisture-density relationship of the soil and the soil-cement mixture shows that mixing the soil with the cement has resulted in a slight decrease in the MDD. The type of change that adding cement will cause to the soil is not usually predictable (ACI 2003). The cement is known to have a flocculating effect on the soil particles which can result in a decrease in the mixture’s maximum dry density.

Compressive Strength (MPa)

3.2

10

Figure 5- Moisture-density relationship of the soil and the soil-cement with 10% cement 3.2.2

12

5

Water Content (% of the dry soil)

3 2 1 0

0

10

20

30

Water Content (% of the dry soil)

Figure 6- Compressive strength of the 50-mm cube soilcement specimens against the water content (% of the airdried soil) When moisture contents are below 12% (i.e. 8%, 10%, 11%), the UCS is below the 1 MPa limit. The main reason could be the lack of water for the cement particles to hydrate and make a strong cement paste for solidification. With moisture contents of 12% and above, the mixture’s strength is always above the 1 MPa limit set. However, since it is crucial for the sample not to obtain sufficient strength for cutting during diffusion testing, a moisture content of 13% was selected as a good option to explore further (at least based on strength).

3.2.3

Hydraulic Conductivity Test Results

Hydraulic Conductivity (m/s)

As previously mentioned, the hydraulic conductivity of a porous medium should be less than 10-9 m/s to ensure diffusion-controlled contaminant migration (Stegemann & Côté 1991). Figure 7 shows that the hydraulic conductivity of the soil-cement mixtures with 10% cement exceeds that limit for the samples with very low (8% and 10%) and very high (25%) moisture contents. For the 8% and 10% moisture contents, the relative amount of water molecules to cement particles is not enough for cement hydration and likely adequate workability. Thus, the samples are relatively porous. For the very high moisture content (25%), the mixture is completely liquid, such that it was not possible to compact, even with a tamping rod. After placing each layer of the raw material inside the mold in this case, the mold has been shaken slowly to get the air bubbles out of the mixture. The high amount of water has resulted in an increase in the total porosity of the sample. Therefore, higher hydraulic conductivities were observed. It is noticed when reviewing Figure 7 that the variability of hydraulic conductivity is not excessive at moisture contents between 11 and 20%, with all samples exhibiting hydraulic conductivity values around 1 x 10-10 m/s. Again, this lack of variability within a fairly large range of water content is desirable from an experimental standpoint in case inherent experimental errors are present. However, the soil-cement sample with a moisture content of 13%, which was desired strength-wise, exhibited a low hydraulic conductivity (1.08×10-10 m/s) which will ensure a diffusioncontrolled contaminant migration for the future testing program.

1.0E-07 1.0E-08 1.0E-09 1.0E-10 1.0E-11 1.0E-12 1.0E-13

0

5

10

15

20

25

30

Water Content (% of the dry soil)

Figure 7- The effect of moisture content on the hydraulic conductivity of soil-cement mixtures with 10% cement 4

CONCLUSIONS

A two-tier approach was presented to determine the appropriate soil-cement mixture proportions for a given soil to be used for fractured cement-based S/S research. Using a hydraulic conductivity of less than 10-9 m/s and a strength of approximately 1 MPa as the criteria, a mixture with 10% cement and 13% water content (to dry soil) was selected as the optimum mix design. This was determined through a series of trial and error mixes to determine compressive strength (Tier 1) and compressive strength and hydraulic conductivity (Tier 2). The chosen mixture was shown to

have a hydraulic conductivity of 1.1×10-10 m/s and a strength of 1.1 MPa. Future research will confirm these conclusions with additional testing. 5

ACKNOWLEDGMENTS

We would like to acknowledge the NSERC Discovery Grant program and the NSERC CREATE program (ASPIRE) for providing funding for this research. Thanks also go to Mr. Nick Wilson of Dexter Construction for providing the soil samples for the research and Mr. Dean Grijm and Mr. Jesse Keane for assistance in the laboratory. 6

REFERENCES

ACI. 2003. Report on Soil Cement, American Concrete Institute (ACI), Farmington Hills, Michigan, USA. ASTM C109/C109M-21. 2021. ‘Standard Test Method for Compressive Strength of Hydraulic Cement Mortars (Using 2-in. or [50 mm] Cube Specimens)’, Annual Book of ASTM Standards, Volume 04.01, ASTM International, West Conshohocken, Pennsylvania, USA. ASTM D5048-16a. 2016. ‘Standard Test Methods for Measurement of Hydraulic Conductivity of Saturated Porous Materials Using a Flexible Wall Permeameter’, Annual Book of ASTM Standards, Volume 04.08, ASTM International, West Conshohocken, Pennsylvania, USA. ASTM D558/D558M-19. 2019. ‘Standard Test Methods for Moisture-Density (Unit Weight) Relations of SoilCement Mixtures’, Annual Book of ASTM Standards, Volume 04.08, ASTM International, West Conshohocken, Pennsylvania, USA. ASTM D6913/D6913M-17. 2017. ‘Standard Test Methods for Particle Size Distribution (Gradation) of Soils Using Sieve Analysis’, Annual Book of ASTM Standards, Volume 04.08, ASTM International, West Conshohocken, Pennsylvania, USA. ASTM D698-12R21. 2021. ‘Standard Test Methods for Laboratory Compaction Characteristics of Soil Using Standard Effort (12,400 ft-lbf/ft3 (600 kN-m/m3))’, Annual Book of ASTM Standards, Volume 04.08, ASTM International, West Conshohocken, Pennsylvania, USA. ASTM D7928-21. 2021. ‘Standard Test Method for ParticleSize Distribution (Gradation) of Fine-Grained Soils Using the Sedimentation (Hydrometer) Analysis’, Annual Book of ASTM Standards, Volume 04.09, ASTM International, West Conshohocken, Pennsylvania, USA. Bates, E. and Hills, C. 2015. Stabilization and Solidification of Contaminated Soil and Waste: A Manual of Practice, Hygge Media. Bone, B.D., Bernard, L.H., Boardman, D.I., Carey, P.J., Hills, C.D., Jones, H.M., MacLeod, C.L., and Tyrer, M. 2004. Review of Scientific Literature on The Use of Stabilisation/Solidification for The Treatment of Contaminated Soil, Solid Waste and Sludges, Environment Agency, Bristol, UK.

EPRI. 2003. Evaluation of the Effectiveness of In-Situ Solidification/Stabilization at the Georgia Manufactured Gas Plant (MGP) Site, EPRI, Palo Alto, California, USA. Glasser, F.P. 1997. Fundamental aspects of cement solidification and stabilisation, Journal of Hazardous Materials, 52(2–3): 151–170. https://doi.org/10.1016/S0304-3894(96)01805-5. Goreham, V.C. and Lake, C.B. 2013a. Naphthalene diffusion and sorption coefficients for cement solidified/stabilized materials, 5th International Young Geotechnical Engineers’ Conference (IYGEC), Marnela-Vallee, France, 2: 98–101. Goreham, V.C. and Lake, C.B. 2013b. Influence of water on diffusion and porosity parameters of soil–cement materials, Canadian Geotechnical Journal, 50(4): 351– 358. https://doi.org/10.1139/cgj-2012-0224. Goreham, V.C. and Lake, C.B. 2018. Diffusion and sorption of volatile organic compounds through soil-cement materials, Environmental Geotechnics, 5(3): 134–145. https://doi.org/10.1680/envgeo.15.00010. Goreham, V.C., Lake, C.B. and Yuet, P.K. 2012. Characterizing porosity and diffusive properties of monolithic cement-based solidified/stabilized materials, Geotechnical Testing Journal, 35(4): 103394. https://doi.org/10.1520/GTJ103394. Government of Canada. 2023, January 3. Federal Contaminated Sites Inventory. Https://Www.TbsSct.Gc.ca/Fcsi-Rscf/Classification-Eng.Aspx. Hou, D. and Li, F. 2017. Complexities Surrounding China’s Soil Action Plan, Land Degradation & Development, 28(7): 2315–2320. https://doi.org/10.1002/ldr.2741. Jolous Jamshidi, R. and Lake, C.B. 2014. Hydraulic and strength properties of unexposed and freeze/thaw exposed cement-stabilized soils, Canadian Geotechnical Journal, 52(3): 283-294. https://doi.org/10.1139/cgj-2014-0100. Klich, I., Batchelor, B., Wilding, L.P., and Drees, L.R. 1999. Mineralogical alterations that affect the durability and metals containment of aged solidified and stabilized wastes, Cement and Concrete Research, 29(9): 1433– 1440. https://doi.org/10.1016/S0008-8846(99)00112-X. Li, J., Xue, Q., Wang, P., Li, Z.Z., and Liu, L. 2014. Effect of drying-wetting cycles on leaching behavior of cement solidified lead-contaminated soil, Chemosphere, 117:10–13. https://doi.org/10.1016/j.chemosphere.2014.05.045. Li, L., Chen, J., Huang, Y. and Dou, Z. 2016. Experimental investigation and numerical simulation of contaminant migration in the compacted clay containing artificial fractures. Environmental Earth Sciences, 75(134). https://doi.org/10.1007/s12665-015-5027-x. Mulligan, C.N., Yong, R.N., and Gibbs, B.F. 2001a. Remediation technologies for metal-contaminated soils and groundwater: an evaluation, Engineering Geology, 60(1–4): 193–207. https://doi.org/10.1016/S00137952(00)00101-0. Mulligan, C.N., Yong, R.N., and Gibbs, B.F. 2001b. An evaluation of technologies for the heavy metal remediation of dredged sediments, Journal of Hazardous Materials, 85(1–2): 145–163. https://doi.org/10.1016/S0304-3894(01)00226-6.

Neretnieks, I. 1980. Diffusion in the rock matrix: an important factor in radionuclide retardation, Journal of Geophysical Research, 85(B8): 4379–4397. https://doi.org/10.1029/JB085iB08p04379. Perera, A.S.R., Al-Tabbaa, A., and Johnson, D. 2005. ‘State of Practice Report- UK Stabilisation/ Solidification Treatment and Remediation- Part VI: Quality Assurance and Quality Control’ in Al-Tabbaa, A. and Stegemann, J.A. Stabilisation/ Solidification Treatment and Remediation: Advances in S/S for Waste and Contaminated Land, CRC Press, Boca Raton, Florida, USA. Rowe, R.K. and Booker, J.R. 1989. A semi-analytic model for contaminant migration in a regular two- or threedimensional fractured network: conservative contaminants, International Journal for Numerical and Analytical Methods in Geomechanics, 13: 531–550. https://doi.org/10.1002/nag.1610130506. Rowe, R.K. and Booker, J.R. 1990a. Contaminant migration in a regular two- or three-dimensional fractured network: reactive contaminants, International Journal for Numerical and Analytical Methods in Geomechanics, 14: 401–425. https://doi.org/10.1002/nag.1610140603. Rowe, R.K. and Booker, J.R. 1990b. Contaminant migration through fractured till into an underlying aquifer, Canadian Geotechnical Journal, 27: 484–495. https://doi.org/10.1139/t90-063. Rowe, R.K. and Booker, J.R. 1991a. Modelling of twodimensional contaminant migration in a layered and fractured zone beneath landfills, Canadian Geotechnical Journal, 28: 338–352. https://doi.org/10.1139/t91-045. Rowe, R.K. and Booker, J.R. 1991b. Pollutant migration through liner underlain by fractured soil, ASCE Journal of Geotechnical Engineering, 117(12): 1902–1919. https://doi.org/10.1061/(ASCE)07339410(1991)117:12(1902). Rowe, R.K., Quigley, R.M., Brachman, R.W.I., and Booker, J.R. 2004. Barrier Systems for Waste Disposal Facilities, 2nd ed., CRC Press, Boca Raton, Florida, USA. Shackelford, C.D. 2014. The ISSMGE Kerry Rowe Lecture: The role of diffusion in environmental geotechnics, Canadian Geotechnical Journal, 51(11): 1219–1242. https://doi.org/10.1139/cgj-2013-0277. Shackelford, C.D. and Daniel, D.E. 1991a. Diffusion in saturated soil. I: Background, ASCE Journal of Geotechnical Engineering, 117(3): 467–484. https://doi.org/10.1061/(ASCE)07339410(1991)117:3(467). Shackelford, C.D. and Daniel, D.E. 1991b. Diffusion in saturated soil. II: Results for compacted clay, ASCE Journal of Geotechnical Engineering, 117(3): 485–506. https://doi.org/10.1061/(ASCE)07339410(1991)117:3(485). Shen, Z., Jin, F., O’conner, D., and Hou, D. 2019. Solidification/Stabilization for soil remediation: an old technology with new vitality. Environmental Science and Technology, 53: 11615–11617. https://doi.org/10.1021/acs.est.9b04990.

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Wednesday, October 4, 2023

GROUND IMPROVEMENT

Preload settlement monitoring in alluvial sediments for a replacement water control structure Joel Hilderman & Gabrielle Bristo Klohn Crippen Berger, Saskatoon, Saskatchewan, Canada Nicole Zacharias Water Security Agency, Saskatoon, Saskatchewan, Canada ABSTRACT The Water Security Agency (WSA) of Saskatchewan commissioned an options evaluation and design for replacement of the Crooked Lake Water Control Structure. It will be located adjacent to the existing structure and founded on highly variable alluvial sediments that are susceptible to differential settlement. To address this risk, various options were considered to improve the foundation conditions and the selected option was a preload pile with vertical wick drains, piezometers, and settlement monitoring instrumentation. Preload pile construction was completed on December 21, 2022 and settlement data were collected during and after construction. The settlement monitoring results indicate a range of settlement between 191 mm to 309 mm, measured by the plate and pipe settlement gauges (PSGs) on June 20, 2023, and between 187 mm to 284 mm from the electronic settlement gauges (ESGs) on April 16, 2023. Questionable settlement monitoring data from the ESGs after April 16 eroded confidence in the ESG results after that date. This paper presents the settlement observations and a comparison of the two types of settlement instrumentation, addressing ease of installation and data collection, cost, reliability, and accuracy. Water Security Agency (WSA) de la Saskatchewan a commandé l’évaluation des options et la conception pour le remplacement de l’ouvrage de contrôle de l’eau du Lac Crooked. Il sera situé à côté de la structure existante et fondé sur des sédiments alluviaux très variables qui sont sensibles au tassement différentiel. Pour faire face à ces risques, diverses options ont été envisagées pour améliorer l’état des fondations et l’option choisie consiste en un tas de précharge avec des drains à mèche verticale, des piézomètres et des instruments de surveillance du tassement. La construction de pieux de préchargement a été achevée le 21 décembre 2022 et les données sur les tassements ont été recueillies pendant et après la construction. Les derniers résultats de la surveillance du tassement indiquent une plage de tassement comprise entre 190 mm et 309 mm, mesurée par les jauges de tassement des plaques et des tuyaux (PSG) le 20 juin 2023, et entre 187 mm et 284 mm des jauges de règlement électroniques (ESG) le 16 avril 2023. Les données douteuses de surveillance des règlements des GSE après le 16 avril ont érodé la confiance dans les résultats ESG après cette date. Ce document présente les observations de tassement et une comparaison des deux types d’instrumentation de tassement, abordant la facilité d’installation et de collecte de données, le coût, la fiabilité et l’exactitude.

1 1.1

INTRODUCTION Background

The Crooked Lake Water Control Structure, is a 9-bay, stoplog controlled, reinforced concrete outlet structure that regulates the lake level and discharge into the Qu’Appelle River from Crooked Lake, which is located approximately 135 km east of Regina, Saskatchewan. Typical flow rates through the structure range from 1.5 m³/s to 16 m³/s, and typical lake levels range from approximately 1 m to 2 m above the base of the structure. The existing structure, which is owned and operated by the Water Security Agency (WSA) is approximately 80 years old and has exceeded its serviceable life. Due to the age of the structure and evolution in the safe and sustainable operating requirements, some issues and concerns have been identified, most notably the structural integrity of the concrete, current operability of the stoplogs slots, ability to access and operate the outlet structure during floods, and

the absence of a fish passage structure. As a result, WSA is replacing the outlet structure. WSA commissioned Klohn Crippen Berger (KCB) to design the replacement water control structure. The design included a siting study and options evaluation, heritage and environmental impacts assessment, and feasibility and detailed design. The preferred option for the replacement control structure was adjacent to the existing structure. The existing structure would be demolished and a nature-like fishway constructed in its place. The foundation for the replacement water control structure consists of interbedded alluvial sediments of silt, sand and clay with highly variable geotechnical characteristics. Experience from similar sites, including other water control

structures in the Qu’Appelle Valley in southern Saskatchewan, have shown that these foundation soils can be susceptible to long term settlement issues, particularly differential settlement, which can impede gate operation of the water control structure. KCB estimated that the structure could experience up to 140 mm of consolidation, with the potential for 125 mm of differential settlement due to drastically different vertical loads over the footprint of the structure. In addition, because of the braided channel deposition of the alluvial sediments, the materials characteristics can vary significantly over a relatively short distance, which can also lead to significant variability in the rate and amount of consolidation under a given load. To mitigate these effects, several foundation improvement options were assessed. The selected design consisted of a 5 m high preload pile with a network of wick drains, pore pressure monitoring instrumentation and settlement monitoring instrumentation. 1.2

Study Purpose

The design required assurance that the expected rate and amount of pre-construction consolidation met the objectives and was consistent with expected soil behavior. WSA and KCB considered two potential options for monitoring the amount of settlement: conventional plate and pipe settlement gauges and electronic settlement gauges. WSA has had mixed success with the electronic settlement gauges, but recognizing the potential benefits, elected to trial both types of settlement gauges as a case study to support monitoring decisions on future construction projects. This paper presents a description of the two settlement monitoring types, the installation and monitoring methodologies, a comparison of the monitoring results and conclusions on the reliability and adoptability of the two monitoring technologies. 2 2.1

METHODOLOGY Soil Characterization and Settlement Modelling

The proposed replacement structure consists of an earthen embankment on either side of a 5-bay, gated water control structure. It was anticipated that the proposed embankment will add approximately 4 m of new fill above the existing ground level. The concrete structure itself will also impart a vertical load on the foundation soils, but less than the adjacent embankment fill. A stratigraphic model of the site was developed from historic and recent geotechnical investigations. The alluvial sediments observed consisted of sand, silt and clay. Geotechnical properties of the soils were estimated from field observations, in-situ testing (standard penetration tests), and laboratory testing (index testing, triaxial shear tests and oedometer consolidation). In addition, standpipe piezometers provided estimates of the pre-construction

water table and slug tests were conducted to estimate hydraulic conductivity of screened units. KCB conducted settlement modelling using Settle3DTM (RocScience 2009). The model considered the spatial variability of the alluvial sediments but also considered a range of potential material properties for individual soil units, based on observed variability. The greatest loads from the replacement structure will be beneath the side walls and the earth embankment on either side of the structure. Settlement in these regions is expected to range from 85 to 140 mm, while settlement under the raft foundation of the structure is expected to be 15 mm or less, resulting in a potential differential settlement of 125 mm. The settlement model was calibrated using a backcalculation of differential settlement observed in similar alluvial settlements at the Buffalo Pound outlet structure. KCB used the settlement model to evaluate settlement and potential beneficial impacts of foundation improvement options, including preloading (various heights, with and without wick drains), driven piles, controlled modulus columns, and lighter weight fill (e.g., EPS blocks). This information was summarized along with estimated costs for these options to select a preferred option. 2.2

Foundation Improvement Design

Based on the estimated effectiveness and costs of the foundation improvement options, WSA selected a design that consisted of a 5 m high preload pile with vertical wick drains installed to a depth of 6 m below existing ground. The wick drains were incorporated to expedite dissipation of excess pore pressures due to the potential for an aggressive construction schedule and limited preloading period. Nonwoven geotextile fabric was used as a separation layer between foundation soils and fill material. To facilitate drainage of the foundation soils, a layer of drainage rock was installed above the geotextile fabric and compacted. Prior to the installation of embankment fill, a second geotextile layer was installed above the drainage rock layer. The preload pile was installed in lifts of 500 mm and compacted. 2.3

Monitoring Instrumentation

The geotechnical instrumentation installed to monitor preloading included vibrating wire piezometers (VWPs) to monitor pore pressures, and plate and pipe settlement gauges (PSGs) and electronic settlement gauges (ESGs) to monitor settlement of the foundation beneath the preload pile. One of these methods is a conventional plate and pipe settlement gauge that is raised in stages along with the preload pile, and is surveyed during and after construction. The second method is an electronic settlement gauge that measures pneumatic head differences between the sensor and a reservoir to detect changes in elevation of the sensor.

2.4

Vibrating Wire Piezometers

Four VWPs were installed at two locations (two sensors in each borehole) under the preload pile. The VWPs were installed after the wick drains but before the preload fill was placed. The VWPs were installed using hollow stem augers and were grouted in place using a cementbentonite grout. The two shallow VWPs were installed between 3 m and 4 m below ground surface in unconfined sand layers. The two deeper VWPs were installed at approximately 9 m and 13 m below ground surface within clay layers. The VWP leads were laid out in a 0.3 m deep trench that extended outside of the preload footprint to an instrumentation pedestal where they were connected to a datalogger. The trench was backfilled with sand and lightly tamped. 2.5

pressure fluctuations acting on the open glycol systems and also to correct measurements if the glycol level in the reservoir should drop during the period of monitoring. The four monitoring ESGs within the preload footprint were installed after the wick drains but before the preload fill was placed. The ESG glycol lines and VWP leads were laid out in two 0.3 m deep trenches that extended to the instrumentation pedestal. The glycol lines were connected to the glycol reservoir and the VWP leads were connected to a multi-channel datalogger. The glycol reservoir and datalogger were housed within a lockable, weatherproof steel enclosure (See Figure 2). The glycol reservoir is filled to a fixed level and topped off with a thin layer of mineral oil to reduce evaporation losses. The trenches were backfilled with sand and lightly tamped.

Electronic Settlement Gauges

The electronic settlement gauges (ESGs) recommended for this project consist of a vibrating wire liquid settlement system. Each gauge consists of a steel plate (0.4 m x 0.4 m). Attached to this plate is a VWP sensor (different VWP from those described in Section 2.4) that measures the pressure within an open loop glycol circulation system consisting of two 6 mm diameter polyethylene tubes (See Figure 2). The dual glycol lines are covered by a sturdy, plastic sheathing and the lines extend from the settlement plate to a glycol reservoir that is installed in a weatherproof enclosure outside the area of settlement. The sensor measures the head differential between the sensor and the reservoir (RST 2023).

Figure 2. ESG Reservoir and Datalogger 2.6

Plate and Pipe Settlement Gauges

Plate and pipe settlement gauges (PSGs) rely on an initial survey of the plate and ongoing survey of the top of pipe during and after pile construction. While the technology is simpler, the installation can be more time consuming and requires additional care during construction to avoid damage to the instruments. Figure 1. ESG Settlement Plate

Five ESGs (RST Model SSVW-105 Liquid Settlement Sensors) were installed, four to monitor settlement under the preload pile and one reference ESG. The reference ESG was installed in a location adjacent to the preload pile in a location that would not be subjected to loads and settlement (during or after construction). The purpose of this reference ESG is to provide a correction to the readings from the measurements ESGs for barometric

Four PSGs were installed within the footprint of the preload pile, each of which was within 1 m of one of the ESGs. The steel plates of the PSGs were 0.6 m by 0.6 m and 10 mm thick, and were installed within a trench 0.3 m deep. Each plate was levelled and surveyed, and the first 1.5 m long section of 48 mm diameter steel pipe was threaded to the receiver on the plate. The trench above the plate was backfilled while ensuring that the steel pipe remained vertical and the top of pipe was then surveyed. Additional lengths of steel pipe were added in 1.5 m long sections as the preload pile was built up, surveying the top of pipe after

it is installed and before the next section of pipe is added. After each section of steel pipe was added and surveyed, an 89 mm diameter, 1.5 m long PVC pipe was attached. The PVC pipe was intended to prevent the preload pile fill from imposing any friction related effects on the steel pipe. Once the preload pile was built to full height, a 219 mm diameter protective steel casing was installed over the PSG pipe, and a lockable cover lid was installed on the steel casing. 2.7

Preload Construction

After the settlement monitoring instrumentation had been installed, the preload pile was constructed, starting on November 24, 2022. The design of the preload pile consisted of a layer of non-woven geotextile across the footprint, to provide separation between the foundation soils and the base drainage layer. A 0.4 m thick layer of drainage gravel was then placed, followed by a second layer of non-woven geotextile to provide separation and filtration between the drainage gravel and the fill material above. The preload fill was placed to an approximate height of 5 m. The preload pile was designed to be left in place for at least 6 months to achieve 90% of expected consolidation of the foundation soils. 2.8

Instrumentation Monitoring and Surveys

Prior to placement of any fill above the instrumentation (e.g., drainage gravel or preload fill), initial ESG and VWP readings were collected for baseline readings. Once construction of the preload pile started, ESG and VWP readings were collected daily to monitor the changes during construction. Survey of the tops of pipes on the PSGs were collected before and after each new section of pipe was added. Following completion of the preload pile, instrumentation readings and survey were collected at approximately 4 to 6 week intervals. 3 3.1

INSTALLATION SETBACKS Repair of ESG tubing

The ESG glycol circulation system consists of a pair of 6 mm polyethylene tubes, sheathed in a sturdy, flexible plastic casing for each ESG sensor. The entire system requires care when handling to avoid kinks or cuts to the glycol line. The plastic sheathing provides some protection against damage and is effective when handled with care; however, the point at which the tubing exits the sheathing is particularly susceptible to damage (see arrow in Figure 3).

Figure 3. Electronic Settlement Gauge Circulation System (from RST 2023)

During the installation of the ESG system and connection of the glycol tubes to the glycol reservoir, the glycol lines had to be pushed through the steel pipe of the instrument pedestal (see Figure 2). During this process the brass connectors encountered resistance to sliding once the pedestal pipe was filled with other glycol lines (5 sets of glycol lines, one for each sensor, had to be pushed through the same pipe) and the polyethylene tubes at this weak point folded and kinked. As the installation was being completed in winter conditions (temperature was between -10°C and -16°C), the tubing was less flexible and cracks developed at the kinks. The leaks were identified and the polyethylene was repaired by cutting out the kinked portion and installing new tubing connectors. Prior to reconnecting, the tube was refilled with glycol prior. Following the repair, air bubbles were immediately observed in the glycol lines. The air bubbles were trapped at the brass connections and after several days had not floated up past the connectors and into the reservoir as desired. As these air bubbles could restrict flow of glycol through the system and could influence the glycol pressure measurements, the system needed to be de-aerated. This was achieved using a de-aerator pump that circulates deaerated glycol into the system, while removing glycol with entrained air bubbles out of the system. The de-aerator pump was provided by Clifton Associates, as the ESG manufacturer was unable to provide at the time of installation. The glycol system was successfully deaerated. The glycol lines were monitored for any further evidence of bubbles over the subsequent construction and monitoring period, but none appeared. 3.2

Replacement of damaged PSG pipe

During the construction of the preload embankment, one of the pipe settlement gauge pipes located on the northwest

3.3

Missed Survey of PSG pipe

During the placement of the final section of pipe on the PSGs, the contractor removed a shorter section of pipe that had been placed on each of the PSGs four days earlier and replaced with a longer section of pipe. However, the surveyor did not collect sufficient data during this process and four days of settlement were lost. It should be noted that the surveyor at the time of the incident was a crossshift who did not fully understand the importance of the PSG survey data or the methodology of calculating the settlement and therefore did not understand how this change in the installation process would result in future problems for settlement calculation. This missing information can and will be recovered at the end of the settlement period by excavating down to the connection of the fourth and fifth sections of pipe and measuring the length of the section 5 pipe, and back calculating the elevation of the Section 4 pipe when Section 5 was added. In the meantime, this data gap has been infilled with settlement data from the ESGs. This is obviously not an acceptable solution for independent evaluation of the two technologies but will be remedied in the summer of 2023.

4

RESULTS

4.1

Piezometer Data

The VWP plots ( 454.50

Construction

Post-Construction

454.00 VWP-001A VWP-001B

453.50

VWP-002A VWP-002B

453.00

PHREATIC SURFACE ELEVATION (m)

side of the preload embankment was struck and damaged while a truck driver was reversing into position. The damaged pipe was assessed and the area surrounding the damaged pipe was excavated. Given that the preload pile had already been built to a height of approximately 4 m above ground, the pipe was well supported and the damage seemed to be limited to the upper-most connection. However, it is possible that the striking of the pipe might have slightly shifted the lower portion of pipe. The damaged sections of steel and PVC pipes were removed and replaced, and the fill was replaced. The top of the pipe was resurveyed after the repair. Following this incident, mounds of fill were placed around the PSG pipes to alert truck drivers of potential obstructions.

Atmospheric Pressure 452.50

452.00

451.50

451.00

450.50

450.00

449.50 11/15/2022

12/15/2022

1/14/2023

2/13/2023

3/15/2023

4/14/2023

Data

Figure 4) indicate increases in porewater pressure of approximately 0.3 m to 0.6 m during the construction of the preload pile. After construction was completed, the excess pore pressures quickly began to dissipate, dropping by 0.2 m to 0.5 m, generally close to the preconstruction pore pressures. This response was initially believed to be entirely due to the development and dissipation of excess pore pressure; however, after checking barometric pressures from the nearest Environment Canada climate station at Broadview, Sask., the rise and fall of measured pore water level during and after construction appears to be mostly attributed to a barometric response measured by the piezometers. The responses do suggest some additional pore pressure response to loading, most notably in the two deeper VWPs installed in clay layers (VWP-001A and 002A). This additional pore pressure response from loading appears to be approximately 0.3 m to 0.4 m for VWP-001A and 002A; however, the precision and confidence is low without site-specific barometric data and baseline data to calculate the barometric efficiency of the VWP-monitored formation. Each of the instruments experienced a significant increase in measured pore pressure starting on April 12, with pore pressures rising between 1.50 m and 1.76 m, before gradually decreasing, starting May 15. This spring rise in pore pressure is consistent with the spring melt

5/14/2023

and rise in lake and river levels, which also raises the water table in the alluvial sediments adjacent to the river. This is followed by a gradual decrease in the lake and river level, and corresponding decrease in the groundwater level in adjacent sediments. According to WSA’s online data, Crooked Lake experienced a 1.86 m lake level rise in the spring of 2023 (WSA 2023). The pore pressure response measured by VWP-001A was unexpected. The VWP observed experienced barometric fluctuations, but also began to rise in early December, increasing by approximately 1.5 m over the next four months. This rise in the monitored pore pressure is not believed to reflect actual conditions in the monitored formation, but may be due to instrument error or problems with the installation of the VWP. The basis for this opinion is the fact that the piezometer still measured the same 1.5 m pore pressure increase due to the rising water table in April – May and the shape of the response from VWP-001A was very similar to that of VWP-002B. If this clay unit had already been experiencing 1.5 m of excess pore pressure, it should not

have further increased when the surrounding water table rose. 4.2

Settlement Monitoring Data

ESG readings were referenced to a baseline reading from 06:00 on November 24, 2022, which was just prior to fill being placed above the sensors. Similarly, the survey of the PSGs was referenced to an initial survey of the PSG plates on November 10, 2022, prior to fill being placed. The ESG and PSG plots during the construction and postconstruction period are presented in Figure 5. The plots indicate total settlement (measured by the PSGs) of between 180 mm and 293 mm. For reference, the Settle3D model predicted preload settlement of between 115 mm and 305 mm; however, this was final settlement after the preload had been removed and elastic rebound had occurred.

454.50

14.00

Construction

Post-Construction

454.00

13.50

VWP-001A VWP-001B

453.50

13.00

VWP-002A VWP-002B

12.50

Atmospheric Pressure 452.50

12.00

452.00

11.50

451.50

11.00

451.00

10.50

450.50

10.00

450.00

9.50

449.50 11/15/2022

12/15/2022

1/14/2023

2/13/2023

3/15/2023

4/14/2023

Data

Figure 4. Piezometer Data

5/14/2023

6/13/2023

7/13/2023

9.00

8/12/2023

ATMOSPHERIC PRESSURE (m)

PHREATIC SURFACE ELEVATION (m)

453.00

0.050

Construction

ESG 1

Post-construction

ESG 2

0.000

ESG 3 ESG 4 PSG 1

VERTICAL DISPLACEMENT (m)

-0.050

PSG 2

Lake, river and groundwater levels begin to rise on Apr.11

-0.100

PSG 3 PSG 4

Sensor signal lost

-0.150

-0.200

-0.250

-0.300

-0.350 2022-11-15

2022-12-15

2023-01-14

2023-02-13

Figure 5. Settlement Monitoring Plots

2023-03-15 2023-04-14 Date

2023-05-14

2023-06-13

2023-07-13

2023-08-12

The settlement monitoring plots indicate that the rate of settlement was highest during placement of preload fill. The rate of settlement decreased post-construction but was still showing an appreciable rate of settlement in April. Until April 16, the comparison of ESG to PSG data was relatively close, with differences ranging from -10 mm (PSGs showing greater settlement during construction) to +31 mm (ESGs showing greater settlement during postconstruction). These differences equate to as much as 13% of the total settlement. The ESG plots show a rebound in the measured settlement that begins on April 16, 2023. The timing of this reversal of settlement readings coincides precisely with the rise in river levels and groundwater levels shown in Figure 4. However, there is not believed to be any physical process by which the foundation of the preload pile would have actually experienced settlement rebound at that time, and the plate and pipe settlement gauges showed continued settlement over this period. Therefore, the settlement rebound measured by the ESGs is attributed to an issue with either the four settlement monitoring ESGs or the reference ESG. Given the nearly perfect parallel shapes and synchronicity of the four ESG plots, it seems more likely that the error is in the reference gauge measurements. On May 1, 2023, KCB observed that the reference ESG was submerged under 0.37 m of water due to a high lake level flooding the area. The ESG manufacturer was contacted to advise on how saturation of the backfill, submergence and ponding above the EST might affect the readings but they were unable to provide an explanation at the time of writing. Another issue that occurred with the ESG system was that two of the sensor signals were lost on May 28, 2023. Given that the ESG system was already providing unreasonable settlement values and that the preload period was at its end, no attempt was made to troubleshoot the issue.

Figure 6. Flooding of ESG Reference Station in Spring 5 5.1

COMPARISON INSTRUMENTATION

OF

SETTLEMENT

Ease of Installation

Evaluation of the ease of installation of ESGs vs PSGs depends on one’s vantage point. For the instrumentation technician installing the instruments, the PSGs are very simple to install. However, the primary challenge for this type of instrument is that the pipe penetrates up through the placed fill and needs to be avoided and worked around by the contractor during every stage of the construction. Even for this relatively simple preload pile construction, an incident occurred and one of the pipes was damaged, requiring repairs. In addition, a surveyor is required throughout construction and post-construction. The surveyor and contractor both need to understand the importance of collecting survey data during construction or critical information can be lost, as described in Section 3.3. Use of PSGs in more complex construction (e.g., more construction equipment operating, more infrastructure being constructed) would introduce greater risk of damage to the pipes. In addition, for projects where a strict target density is required, there would be greater challenges to achieve this density near the PSG pipe without contacting the pipe with compaction equipment. Conversely, the ESGs are much simpler for the construction contractor. After the initial installation and backfill of the trenched cables and glycol lines, the contractor can proceed to build above the instruments without concern about damage to the gauges. The installation of ESGs is more challenging than PSGs for the instrumentation technicians. The connection of the pressure sensor cables to the datalogger is the same as a typical VWP lead, which is relatively straightforward for experienced geotechnical technicians. However, the connection of the glycol lines can be difficult as evidenced by the setbacks discussed in Section 3.1. In particular, the polyethylene tubing is more brittle and difficult to work with in cold conditions, requiring additional precautions to avoid damage (described below in Section 6). In addition, the experience from this project proved that it can be difficult to source a de-aerator pump to remove air from the glycol system. For a quantitative evaluation of ease of construction, one could estimate the effort for each installation and determine the cost based on hourly rates for contractors and instrumentation technicians. However, for Crooked Lake Preload, the ease of installation was not considered to be a strong determining factor in the comparison of the two technologies. 5.2

Ease of Data Collection

Data collection for the ESGs is relatively simple. The ESG data is downloaded from the ESG datalogger by an instrumentation technician in less than 5 minutes and

requires limited technical knowledge and no specialized equipment (aside from a laptop and connection cable). It is also possible to set up the ESG system to transmit the collected data via cellular telemetry, if the additional cost for the transmitting equipment can be justified. This would facilitate even simpler data collection, although it should be noted that these telemetry systems require more care and maintenance than a simple datalogger. The ESG system can be set up to collect data at any desired frequency and trips to site are only required at a frequency to provide comfort that the system is functioning or to assess the progress of settlement to make decisions on the preload duration. Conversely, each PSG has to be surveyed by a qualified surveyor using RTK GPS survey equipment. Collection of the survey data is relatively fast (less than one hour for this site), but still slower than collection of ESG data. Settlement data points from the PSGs are limited by the frequency at which the surveyor travels to the site to collect readings. Depending on how remote the site is, it may not be practical to collect daily or weekly readings postconstruction. So in this respect, the ESGs are superior in terms of the ease of data collection and also the amount of data that can be collected. 5.3

Cost

The cost for the ESG equipment was approximately double the cost for the PSGs. However, once the additional survey time required during construction and data collection afterwards is considered, the overall cost of the two methods is comparable for this project with a small scale and short timeline. The potential for cost savings with the ESG system would increase with more instruments or for long-term installations. 5.4

Reliability

Both systems are generally reliable under ideal conditions, however, there are pros and cons to each system. The ESGs use a datalogger that contains a battery. If at any point, the battery fails, it would need to be replaced. During the time that the datalogger is non-functional, no data would be stored on the datalogger. Additionally, the ESG system works based on hydraulic head and as such changes in temperature and atmospheric pressure can impact the readings. The reference gauge is intended to correct for these fluctuations; however, as described in Section Error! Reference source not found., if the reference gauge provides inaccurate readings, it can be a source of potential error in the settlement readings. In addition, the signal was lost from two of the five ESG sensors near the end of the preload monitoring period. It is possible that this issue might have been easily corrected (e.g., loose connections at the datalogger), but it may have been due to damage to buried cables somewhere in the system. By that time, the ESG readings were already inaccurate (as described in Section 4.2) and further data

collection was not required, so no attempt was made to troubleshoot the issue, but it represents another potential risk in the system. On the other hand, the settlement readings from the PSG system can be impacted if any of the pipes are damaged during the construction process. Once the PSGs are installed, they can be vandalized which could potentially impact the survey. However, in general, once the PSGs are fully constructed, the primary concern with accuracy is related to the reliability of the surveyor collecting the survey of the PSGs. Based on the ESG plots in Figure 5 and the lack of certainty over the cause of the rebound after April 14 and the signal disruption for two of the sensors at the end of the monitoring period, for this particular project, the PSGs were more reliable. 5.5

Accuracy

The two systems are believed to have similar ranges of accuracy under ideal conditions. The ESG manufacturer does not provide an accuracy range for the technology. The surveyor estimated the accuracy of surveys at ±5 mm. 6

CONCLUSION AND RECOMMENDATIONS

The Crooked Lake Control Structure Preload construction afforded an opportunity to compare two different types of settlement monitoring technologies: plate and pipe settlement gauges and electronic settlement gauges. The experience gained from this project suggested that there are benefits and disadvantages to each of the two types. Setbacks were encountered with the installation of both systems. However, ultimately the evaluation of the two systems was determined by the confidence in the results. The settlement data from the ESGs and PSGs compared reasonably well during construction and post-construction up to April 16, 2023. At that point, the ESGs showed a reversal of settlement which had no basis in physical processes and was attributed to problems with the reference gauge data. Without any other accurate means of correcting the data (no onsite barometric monitoring), the reliability of the ESG system was lost and ultimately the reported settlement data will be based on the surveyed PSGs. Several lessons were learned from this project. For future installations of ESGs, the following recommendations are made: •

During installation of the ESGs, the weak point in the system (where the glycol tubes exit the sheathing, see Section 3.1) should be protected by wrapping the tubes and brass connectors in a temporary sheathing of electrical tape or similar. This temporary sheathing will protect the exposed polyethylene glycol tubes against nicks and cuts

and will provide this portion of the glycol line with improved rigidity for feeding through pipes or holes (e.g., in weatherproof enclosures). •

•

•

A barometric pressure monitoring device should be installed with the ESG system. These devices are readily available from various manufacturers and are relatively inexpensive (approximately $500). The barometric measuring devices have self-contained data loggers, are easy to use and can be installed in the same weatherproof enclosure as the glycol reservoir. This would serve as a backup means of correcting the settlement sensor data, if the reference gauge fails or as a check on accuracy. In the case of this project, it would have also been useful for correcting VWP readings. It may not be enough to select a location for the reference gauge that is simply clear of proposed construction activity or fill placement. In the case of this project, it is possible that post installation saturation of the fill around the sensor (and ponding of water above the sensor) may have affected the readings. Until a satisfactory explanation can be provided for the ESG responses observed, it is recommended that ESG plates be installed in a location where they will not be subjected to post-installation saturation. Further to the point above, freeze / thaw impacts may have affected the shallow ground in the reference gauge location. Insulating the ground surface at the location of the ESG reference gauge may have prevented this impact and is recommended for future ESG installations.

The challenges that occurred with the PSGs during this project were a result of poor execution of installation instructions. The following recommendations are made: •

Despite instructions to protect the PSGs, one of the pipes was struck and damaged. The design did not specify how this protection was to be provided. The contractor relied on procedural protection in the form of spotters for construction equipment, but this still resulted in an incident when the spotter was not paying close enough attention. Physical protection measures (e.g., protective berms, concrete blocks/barriers, etc.) will also not provide guaranteed protection but can be used together with procedural protection measures to improve the chances of success.

•

The gap in survey data was attributed to the contractor and surveyor not fully understanding the purpose of the PSGs and how the survey factors into the settlement calculation. For future installations it is recommended that the engineer explain the process to the contractor and the surveyor, and re-explain to any new field staff.

The selection of a settlement monitoring technology depends on a number of factors including complexity and permanence of the construction, available budget for instrumentation, duration of the construction and monitoring period, location of the site, supervision and security of the site, access to specialized field personnel (e.g., surveyors or instrumentation technicians), among other factors. For projects of relatively simple complexity, similar to this the Crooked Lake Control Structure preload pile, the PSGs are considered to be an adequate method of monitoring. However, this project afforded the owner, WSA, an opportunity to evaluate the technologies and will help in future decisions on other projects about the most suitable method of monitoring to use. As a result of challenges experienced with both technologies, the project has also provided lessons learned, which can be applied to either of the monitoring methods. REFERENCES RocScience. 2009. "Settle3D." Settle3D Settlement and Consolidation Analysis Software. RocScience. RST. 2023. Vibrating Wire Liquid Settlement System. Accessed 05 05, 2023. https://rstinstruments.com/product/vibrating-wireliquid-settlement-system/. WSA. 2023. Qu'Appelle River Watershed. Accessed 08 17, 2023. Qu’Appelle River Watershed - Water Security Agency (wsask.ca) .

On the Use of Recycled Waste Materials in Ballasted Railway Infrastructures: A Review Jana Jarjour & Mohamed Meguid Department of Civil Engineering– McGill University, Montreal, Québec, Canada ABSTRACT The use of waste materials in railway infrastructure is becoming increasingly important due to the need for more sustainable and cost-effective transportation systems. This paper reviews the application and performance of recycled materials in different parts of ballasted railway tracks, including sleepers, ballast, sub-ballast, and interface zones. The study confirms the feasibility of using recycled materials such as rubber, plastic, and glass to improve the performance and properties of railway infrastructure. The findings suggest that the use of waste materials can be cost-effective and efficient in improving the long-term performance and stability of the ballasted track. It is important to note that each type of waste material has a different effect on the performance of the different parts of the railway track. Overall, the study supports the development of more environmentally friendly and cost-effective ballasted tracks and expands the understanding of the performance of railway tracks maintained using waste materials. RÉSUMÉ L'utilisation de matériaux de déchets dans l'infrastructure ferroviaire devient de plus en plus importante pour des solutions durables et rentables. Cette étude examine l'utilisation de matériaux recyclés tels que le caoutchouc, le plastique et le verre dans différents composants de voies ferrées sur ballast. Les résultats suggèrent que l'utilisation de matériaux de déchets peut améliorer les performances et la stabilité à long terme de la voie sur ballast de manière rentable et efficace. Cependant, chaque type de matériau de déchet a un effet différent sur la performance des différentes parties de la voie ferrée. En somme, cette étude soutient le développement de voies sur ballast respectueuses de l'environnement et rentables, et améliore la compréhension de la performance des voies ferrées entretenues à l'aide de matériaux de déchets.

1

INTRODUCTION

Railway systems are critical components of modern infrastructure, providing an efficient means of moving people and goods that promote economic growth while reducing greenhouse gas emissions. From a construction perspective, railway tracks can be categorized into two main types: slab track (ballastless) railways and traditional ballasted track railways (Indraratna et al. 2011). Traditional ballasted track railways are more commonly used around the world because of their ease of maintenance and low construction costs (Indraratna et al. 2018). These railway tracks typically consist of rails and their fastening system, sleepers, ballast, capping (sub-ballast), and subgrade (Indraratna et al. 2018), as shown in Figure 1. However, over time, the materials that make up these railway tracks lose their functionality. Sleepers start to deteriorate (Ferdous and Manalo 2014), and the ballast and sub-ballast materials, which require a large volume of raw aggregates, start to deform and degrade (Indraratna et al. 2005). Replacement or maintenance of these parts is typically expensive and can have a negative impact on the environment due to the waste disposal involved in the process (Swärd 2006). The world generates approximately 2 billion tons of solid waste annually, and this is expected to increase by around 70% by 2050 (The world bank 2022). Waste material is defined as any discarded or abandoned material resulting from personal use or industrial activity (Pickin et al. 2020). The use of recycled materials in railway

infrastructure is an effective approach to creating a more sustainable world, where basic human needs can be met, and surrounding ecosystems can be restored (Muench and Van Dam 2014). Therefore, researchers have been studying the feasibility of embedding recycled materials as a sustainable alternative to the current raw materials for use in the construction of railway systems. While using recycled materials in engineering practices is not new, its use in ballasted railways infrastructure is relatively recent. Extensive research has been conducted on the use of recycled waste materials, such as plastics (Arulrajah et al. 2020), crushed glass (Naeini et al. 2021a), steel furnace slag (Delgado et al. 2019), and rubber from waste tires (Sol-Sánchez et al. 2020) in railway construction. The literature can be divided into two main approaches: 1) using different types of recycled materials in a specific layer of the railway structure (e.g. sleepers) (Ferdous and Manalo 2014), and 2) focusing on a specific recycled waste material type, such as recycled rubbers, and examining its potential applications in railway structure (Indraratna et al. 2022).   Despite the extensive research that has been conducted to date, a thorough comparison across the wide range of recycled materials used in different layers of ballasted track railways is absent in the literature. This paper aims to fill this gap by comparing studies that evaluate the use of recycled waste material in ballasted railway infrastructure. The paper also aims to demonstrate the feasibility and effectiveness of using recycled materials

in ballasted railway infrastructure and summarizes several recycled waste materials that can potentially improve traditional ballasted railway layers. The remainder of this paper provides an overview of the functions of different parts of traditional ballasted railways and the use of conventional materials in this practice, along with their corresponding challenges. The impact of incorporating recycled waste material is evaluated in terms of performance in four different parts of the ballasted railway, including sleepers, ballast, sub-ballast, and the interface between these components. Finally, the paper concludes with a summary and conclusions drawn from this study.

rails Sleeper ballast Sub-ballast subgrade

Figure 1. Typical ballasted railway system. 2

TRADITIONAL BALLASTED RAILWAY TRACKS

The traditional ballasted railway track system comprises of two main components - the superstructure (rails, fastening system, and sleepers) and substructure (ballast, subballast, and sub-grade) (Figure 1). The rails, made of parallel steel beams transfer vertical, lateral, and any accelerating or braking forces to the supporting sleepers. Steel fasteners are used to secure the rails to sleepers. Sleepers, evenly spaced along the rail length, transfer and distribute rail loads to the infrastructure (Selig and Waters 1994). The ballast layer supports the load imposed on rails and sleepers from the train, transmits the load to the subgrade, and maintains acceptable settlement (Indraratna et al. 2005). The sub-ballast, which lies between the subgrade and the ballast, has four primary functions, including reducing transmitted traffic stress, protecting the subgrade from frost, block subgrade and ballast interpenetration, and preventing slurry formation inside the subgrade (Selig and Waters 1994, Indraratna et al. 2011). Finally, the subgrade serves as the foundation for the track and its components (Li and Selig 1994). Elastic materials such as rubber, rail pads, under sleeper pads (USP), and under ballast mats (UBM) are used in the transition between each section to improve vertical stiffness, correct gradual track deformations, and reduce vibration (Navaratnarajah et al. 2016). Materials such as timber, concrete, and steel are used to manufacture railway sleepers (Manalo et al. 2010). Aggregates used in the ballast layer are considered of good quality if synthesized from crushed, angular, clean, strong stones, and rocks, and uniformly graded. The aggregates in the sub-ballast layer are usually comprised

a) b) c) Figure 2. Typical deterioration patterns of sleepers: a) timber; b) concrete; c) steel,(Ferdous and Manalo 2014). of locally available well-graded crushed rock or sand-gravel mixtures (Selig and Waters 1994). Resilient elements such as USPs and UBMs are made of materials such as rubber, polyurethane elastomers (PE), high-density polyethylene (HDPE), thermoplastic polyester elastomer (TPE), and ethylene vinyl acetate (EVA) (Johansson et al. 2008). 2.1

Limitations of the application of conventional materials in railways

There are problems associated with the performance of ballasted railway components. For instance, replacing timber sleepers costs over $500 million annually the US (Qiao et al. 1998) due to deterioration or failure to meet the minimum required performance. In Australia, around 200 million timber sleepers are introduced yearly in rail tracks, costing over $1.3 billion, and 90% of these sleepers will be replaced by 2030 (Ferdous and Manalo 2014). Traditional sleepers made of concrete and steel emit high levels of toxic carbon dioxide gas and are not capable of resisting mechanical, biological, and chemical degradation, see Figure 2. Degraded and fouled ballast has undesirable impacts on ballast performance, creating a disposal problem due to the accumulation of fouled ballast from the replacement process (VAIVARS 2010). Transition zones require high-quality elastic components under sleepers and ballast layers, which can lead to a significant increase in railway construction costs (Sol-Sánchez et al. 2014). To address these challenges, researchers have explored alternative materials such as recycled waste to replace traditional materials for better performance, lower cost, and environmental benefits. Globally, there is an annual generation of 1 billion rubber tires, 359 million tons of plastics, 130 million tons of glass, 1.3 billion tons of fly ash, and 3 billion tons of construction and demolition waste, which could be recycled for environmentally friendly rail track construction (Ferdous et al. 2021). 3

INTEGRATION OF RECYCLED MATERIALS IN BALLASTED RAILWAY TRACKS

This section reviews major research findings to present the performance and impact of using recycled wastes in ballasted railway infrastructure. And it might provide a comparison with conventional materials or their standard requirements. Findings demonstrate the feasibility of using recycled materials because of improving efficiently the performance of different parts of ballasted railway tracks. This part of the paper specifically examines the use of recycled materials in the sleepers, ballast, and sub-ballast

of the railway structure, as well as the transition zones between each of these components. 3.1 3.1.1

Sleepers Recycled plastic sleepers

Commercial sleepers made of recycled plastic are manufactured using 100% recycled plastic or reinforced recycled plastic with fibers (Axion n.d., Intergrico n.d., sicut n.d., TieTek n.d.). These two types of commercial sleepers are categorized as Type-1 sleepers by some researchers and are found to have several advantages over traditional sleepers, such as simple to drill and cut, durable, able to consume waste materials, affordable, and tough (Ghorbani and Erden 2013, Ferdous and Manalo 2014). However, they also have some disadvantages, including low strength and stiffness, limited design flexibility, low anchorage capacity, sensitivity to temperature and creep, and slight fire resistance. Table 1 presents a comparison of some properties of softwood, concrete, steel, and commercially recycled plastic sleepers, indicating that recycled plastic sleepers have good properties compared to traditional ones in terms of cost, service life, and weight. Commercial sleepers with no reinforcing fibers have limitations in terms of strength, stiffness, and dynamics compared to reinforced recycled plastic sleepers with fillers and fibers (Ferdous et al. 2021). Recent studies have explored the use of composite materials for railway sleepers by combining recycled plastic with other materials, such as fillers, fibers, and manufacturing byproducts. For example, a study evaluated a composite mixture of 40% of municipal plastic wastes and recycled high-density polyethylene (RHDPE) with 60% coal-ash filler, which exhibited higher bending strength (33 MPa) and compressive strength (47 MPa) than the minimum standards for plastic sleepers (ISO 2014, Ju et al. 2020). Another study proposed a composite sleeper made of 3% E-glass fiber, 26% iron slag, and 70% plastic waste mixture, which outperformed commercially available composite sleepers in terms of modulus of elasticity (2.14 GPa), flexural strength (21.45 MPa), rail seat compression (26.42 MPa), and screw withdrawal capacity (50 KN) (Khalil 2018). These studies demonstrate the potential of composite materials to enhance the mechanical properties of railway sleepers. 3.1.2

Recycled rubber, slags, and aggregates added to concrete sleepers

Several studies have extensively evaluated the performance of waste materials, such as waste rubber, slags, and recycled aggregates when added to concrete sleepers or pre-stressed concrete. These composite concrete sleepers are known as eco-friendly concrete sleepers and have both environmental benefits and can meet international standards while showing better structural performance than traditional sleepers. Table 2 presents the benefits and drawbacks of using recycled rubber and recycled concrete aggregates in concrete sleepers, adopted from the findings of previous studies

Table 1. Properties comparison of traditional sleepers (softwood, concrete, and steel) to recycled plastic sleepers (Manalo et al. 2010). Properties

Softwood

Concrete

Steel

Service life (Years) Weight (Kg) Replacement of sleepers Ballast interaction Availability Cost

10-20

40-60

40-50

Recycled Plastic Sleepers 40-50

60-70 Easy

285 Difficult

70-80 Difficult

45-75 Easy

Good

Very good

Poor

Good

High Low

High Very high

High High

Low Low

(Hameed and Shashikala 2016, Koh et al. 2016, GonzalezCorominas et al. 2017, Kaewunruen et al. 2018, Dolci et al. 2020). Kaewunruen et al. (2018) conducted tests according to BS EN -2390 including Compressive strength test, splitting tensile strength test, and 4-point bending test to determine the optimal mixture of micro-scale crumb rubber and silica fume in concrete sleepers. Results showed that replacing the fine aggregates with rubber improve tensile and flexural strengths, damping properties, and electrical resistivity but lowers the compressive strength. The optimum percentage of rubber is found to be 5% (Kaewunruen et al. 2018). Hameed & Shashikala (2016) showed that replacing 15% of fine aggregate (volume fraction) with recycled rubber in concrete increased impact load resistance by over 50% when compared to conventional concrete sleepers and pre-stressed sleepers. Koh et al. (2016) developed a composite pre-stressed concrete sleeper that meets the Korea Railway Standard by replacing 30% of Portland cement with blast furnace slag and replacing all fine aggregates with electric arc furnace oxidizing slag. Gonzalez-Corominas et al. (2017) tested pre-stressed concrete sleepers with recycled concrete aggregates from rejected sleepers, meeting the Table 2. Benefits and demerits of adding some waste materials on the properties of concrete sleepers. Recycled waste material

Recycled Rubber

Recycled concrete aggregates

Benefits on properties of concrete sleepers -Increase thermal resistance -Increase damping ratio (vibration absorbent) -Protect from the effect of freeze-cycle -Increase flexural and tensile strength -Prevent high sulphate content -Decrease density -Increase compressive strength

Demerits on properties of concrete sleepers

-Flammable material Slightly lower compressive strength

-Decrease absorption porosity -Decrease workability

water and

Spanish pre-stressed sleeper specifications and exhibiting superior structural performance. Dolci et al. (2020) conducted a Life Cycle Assessment comparing a novel sleeper with an outer shell of rubber powder and plastic to a conventional pre-stressed reinforced concrete sleeper, demonstrating reduced vibration, cost, and increased lifespan. 3.2 3.2.1

Ballast Recycled ballast aggregates

Damaged ballast can be cleaned and reused in railway tracks as a sustainable alternative to raw ballast aggregates. Nevertheless, damaged ballast cannot be solely used, it must be used with other materials, such as geosynthetics or fresh ballast (Indraratna et al. 2005, 2010, Desbrousses and Meguid 2023). Geosynthetics have proven to be effective in stabilizing and reinforcing recycled ballast, reducing vertical settlement, particle breakage, and lateral strain in wet and dry conditions (Indraratna et al. 2005). Field tests in Australia have shown that reinforced recycled ballast performs similarly to fresh ballast without geosynthetics, reducing maintenance costs (Indraratna et al. 2010). In terms of mixing recycled ballast with raw ballast, the results of a large-scale direct shear test showed that an optimum percentage of less than 30% of recycled ballast should be used to maintain similar shear strength and deformation as fresh ballast (Jia et al. 2019). 3.2.2

Recycled rubber in ballast layer

Various forms of recycled rubber, including particulate rubber and tire-derived aggregate (TDA) have been used in the ballast layer. According to ASTM D6270 (ASTM D6270 2012), particulate rubber is classified into ground rubber (0.5 mm to 2mm) and granulated rubber (0.5 mm to 12 mm), while TDA is categorized into tire chips (12 mm to 50 mm) and tire shreds (12 mm to 300 mm), as shown in Figure 3.

Table 3 summarizes the impact of adding recycled rubber of different sizes on the performance of the ballast layer, with adequate rubber content improving damping capacity, shear resistance, ductility, energy dissipation under cyclic loading, and abrasion resistance, while reducing ballast particle breakage and settlement. TDA is a cost-effective and eco-friendly rubber product derived from used tires that can be recycled for construction purposes (Chan and Johan 2016, Esmaeili et al. 2016, Fathali et al. 2017, 2019). Chan & Johan (2016) and Fathali et al. (2017) conducted direct shear and ballast box tests to assess the impact of TDA on shear resistance, damping, abrasion resistance, and particle breakage. Results showed that 10% TDA content in the ballast mixture increased damping, shear resistance, abrasion resistance, and ductility, while reducing stiffness, particle breakage, and settlement (Chan and Johan 2016, Esmaeili et al. 2016, Fathali et al. 2017, 2019).

Figure 3. Scrap tire classifications in accordance to ASTM D 6270, 2012, (ASTM D6270 2012). Multiple studies examined the potential additive of particulate rubber to the ballast layer (Sol-Sánchez et al. 2015, Arachchige et al. 2021). Sol-Sánchez et al. 2015 conducted a laboratory test to determine the optimal percentage of inclusion of granulated rubber (8 mm to 16 mm in size) and found that 10% volume of rubber could reduce ballast degradation and stiffness while increasing

Table 3. Effect of recycled rubber of different sizes on the performance of the ballast layer. Recycled Waste Material

Particle sizes

Recycled rubber

8 mm to 16 mm 25mm to 300 mm

Effect of the material on performance of the ballast layer

the

-Decrease in Particle breakage, stiffness and settlement. -Increase in energy dissipation under cyclic loading -Increase in damping. -Decrease in stiffness, particle breakage and abrasion.

Optimum rubber content / Rubber type

Reference

10% Particulate rubber

(SolSánchez et al. 2015)

/

10% / TDA

(Esmaeili et al. 2016)

10 and 20mm

-Increase of shear resistance and ductility

_

20 mm to 60 mm

-Decrease in particle breakage by 47%, settlement by 6% and transmitted vibrations. -Slight reduction in shear strength.

(Chan and Johan 2016)

10% / TDA

(Fathali et al. 2017, 2019)

9.5mm to 19 mm

-Decrease particle breakage by 70%, dilation and modulus degradation. -Increase in energy dissipation under cyclic loading

10% Particulate rubber

/

(Arachchige et al. 2021)

the capacity of the ballast layer to dissipate energy, thereby reducing track settlement. Similarly, Arachchige et al. (2021) found that adding 10% rubber granules obtained from scrap tires to the ballast layer increased energy dissipation and reduced the dilation, modulus degradation, and breakage of ballast aggregates. These studies ALSO suggest that incorporating recycled rubber materials in the ballast layer could lead to reduced maintenance costs and several socio-environmental benefits. 3.2.3

Steel Slag in ballast layer

Studies have evaluated the effect of using steel slag in the ballast layer, with favorable results such as higher lateral resistance and shear strength; lower maximum contact stresses and particle breakage; and similar behavior in permanent deformation compared to conventional materials (Esmaeili et al. 2017, 2019, Delgado et al. 2019, Jing et al. 2020). Additionally, steel slag, in the range of 1225 mm aggregate sizes, is resistant to abrasion in all weather conditions and can be used as a substitute for ballast in railway tracks if annual traffic is less than 3 million gross tons (Hasheminezhad et al. 2016). Steel slag's heavy weight provides high resistance to lateral movement (Kaya 2004)., and its use has shown to increase the lateral resistance of tracks by 27% (Esmaeili et al. 2017). Furthermore, field tests have indicated higher rail support modulus and lower maximum contact stress when using steel slag instead of limestone ballast (Esmaeili et al. 2019). Finally, a recent study suggests that an optimal mixture of stone ballast and steel slag is 75% steel slag, providing high values of elasticity modulus and friction angle(Jing et al. 2020). 3.3

Sub-ballast

3.3.1

Rubber wastes inclusion in the sub-ballast layer

The use of granulated scrap tires (TDA) and tire cells has been investigated for their performance impact in the subballast layer. Mixing recycled waste rubber with raw subballast mitigates the environmental threat posed by the accumulation of scrap tires (Martínez Fernández et al. 2018). In Spain, a research group analysed the efficiency of using unbound granulated scrap tires of less than 20 mm mixed with coarse aggregates as a substitute for traditional granular sub-ballast (Hidalgo Signes et al. 2015, Hidalgo-

Signes et al. 2016, Martínez Fernández et al. 2018). Similarly, a recent study investigated the properties of the TDA-sub-ballast mixtures by considering the effects of TDA shape and size (Zhang et al. 2022). Findings of these studies showed that the TDA-sub-ballast mixtures improve resistance to aggregates degradation, damping capacity, and increase the resilient strain, internal friction angle, and energy dissipation. However, the stiffness, shear dilatancy, cohesion, bearing capacity, and resilience modulus decrease as rubber content increases. The optimal rubber content has been limited to 5%. In terms of the effect of the shape of the granules, adding TDA chips increases the strength of the sub-ballast while adding TDA granules has the opposite effect (Zhang et al. 2022). It is also concluded that TDA chips significantly improve the properties of the mixtures, and the optimal TDA chips and granules contents were estimated to be approximately 20% and 5%, respectively signes(Hidalgo Signes et al. 2017, Zhang et al. 2022). Another application of recycled rubber in the upper subballast layer is the use of waste rubber tire cells (Indraratna et al. 2018). Laboratory and numerical work were performed to investigate the performance of the sub-ballast material reinforced with tire cells under a heavy haul loading condition (Indraratna et al. 2017, 2018). Results showed that the use of tire cells in the sub-ballast layer increased of granular materials confinement as well as modulus and bearing capacity. The sub-ballast layer stiffness was increased by more than 50%, and ballast breaking was reduced by 68.5%. Stress transmitted to the subgrade was reduced, leading to a reduction in the vertical settlement of a track by approximately 10-12 mm compared to the sample without tire cells (Indraratna et al. 2017, 2018). 3.3.2

Recycled plastic, glass, and demolition wastes in sub-ballast layer

Recent evaluations investigated using recycled crushed aggregates (RCA) blended with recycled plastics (RP) or recovered glass (RG) as an alternative to conventional sub-ballast granules (Mohammadinia et al. 2020). Table 4 summarizes the impact of adding these materials to the sub-ballast layer and the optimal mixture. Index and lab tests were conducted on different percentages of RCA/RP and RCA/RG mixtures to determine geotechnical parameters and dynamic characteristics (Naeini et al.

Table 4. Impact of increasing RP or RG content on the performance of the sub-ballast layer. Waste Materials Recycled Concrete Aggregates (RCA)

Effect of increase in RP & RG content

Optimum mixture RCA95+RP5

-Resilient modulus decreases Recycled -Energy absorption capacity, stiffness, and strength Plastic (RP) increase -Ductility increases RCA80+RG20 Recovered -Shear strength, energy dissipation capacity, peak Glass (RG) friction, resilient modulus, and young’s modulus decrease.

References (Naeini et al. 2019, 2021b, Arulrajah et al. 2020, Mohammadinia et al. 2020) (Naeini et al. 2019, 2021a)

2019, 2021a, Arulrajah et al. 2020, Mohammadinia et al. 2020). Results showed that the energy absorption capacity increases when RP content increases in the RCA. RCA/RP blends have acceptable permanent strain and resilient modulus. The optimum blend was found to be RCA95/RP5 (Mohammadinia et al. 2020, Naeini et al. 2021b). The presence of RG increases ductility and particle breakage but reduces shear strength and resiliency modulus. The optimal RCA/RG mixture was 80% RCA and 20% RG (RCA80/RG20) for higher levels of stiffness and strength than conventional materials (Naeini et al. 2019, 2021a). 3.4

Transition zones (Sleepers -Ballast/Ballast-Subballast)

Recycled waste rubber in different forms has been used at the transition zones between the ballasted railway layers. Transition zones are the interfaces of sleepers – ballast (USP) and ballast - sub-ballast (UBM). The effectiveness and suitability of recycled rubber to achieve different track requirements and improve railway general performance has been demonstrated in the literature. It is found that waste rubber is valuable when used at transition zones since it contributes in vibration attenuation, particle breakage reduction, damping capacity elevation, as well axial and lateral strain reduction of the ballast layer (Ho et al. 2013, Sol-Sánchez et al. 2014, Ngo et al. 2019). 4 •

•

•

•

SUMMARY AND CONCLUSIONS Recycled plastics generally proved to be efficient materials to be add to sleepers since it made sleepers having longer service life, light weight, and low cost; meanwhile meeting international standard’s requirements. Also, it is found that reinforcing fibres enhanced the performance of recycled plastic sleepers. Recycled rubber improves the flexural and tensile strength of concrete sleepers but reduces its compressive strength. It also enhances thermal resistance and damping capacity, but it is a flammable material. Meanwhile, recycled aggregates increase the compressive strength of prestressed concrete sleepers but decrease their density, porosity, and workability. Recycled ballast, rubber, and steel slag are used in the ballast layer. Recycled ballast gives similar performance to fresh ballast when used with geosynthetics or mixed with fresh ballast. Steel furnace slag has advantages over conventional ballast, including higher lateral resistance and damping capacity. Adding 10% rubber particles to ballast aggregates is found to be optimal rubber content, as it increases damping and resistance while decreasing stiffness, particle breakage, and settlement. It is generally found that as rubber content increases in the sub-ballast layer, degradation, energy dissipation, confinement, and damping resistance increase but vertical settelement, shear strength, and resilient modulus decrease. Optimum content of

•

•

5

rubber, added to the sub-ballast layer, was found to be 5% and 20% of TDA-granules and TDA-Chips, respectively. Recycled plastics and recovered glass can be used in sub-ballast layer when mixed with recycled crushed aggregates. Increasing the recycled plastic content improves energy absorption capacity after cyclic loading. Adding more recovered glass increases the ductility of RCA but reduces shear strength parameters and resiliency modulus. The optimal blends were found to be RCA95/RP5 and RCA80/RG20. Recycled rubber in all its forms could be valuable when used as under sleeper pads and under ballast mats. REFERENCES

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Performance evaluation of standard grouted anchors versus expanded anchors Mudasser M A Noor, Hicham Salem Scientific Applied Concepts Ltd., Ottawa, Ontario, Canada Hamid Batenipour, Kiewit Engineering Group Canada ULC, Oakville, Ontario, Canada Troy Skinner, Marathon Underground Construction Corporation, Ottawa, Ontario, Canada ABSTRACT Foundations and tiebacks with expanded elements have been used in the past, namely soil anchors, driven, vibrated and bored piles. This paper presents a test program conducted at a site in Ottawa, Ontario, Canada, comparing the performance of conventional tiebacks (strand anchor with gravity grouting) to shorter tiebacks with an expanded anchor. The conventional tiebacks (strand anchors) were 150 mm in diameter and were constructed to a depth of 28 m:18.0 m of unbonded length and 10 m bonded in dense sands with gravity grout. Tiebacks anchored with expanded elements were installed in a 228 mm diameter hole to a depth of 7 m and were expanded in loose to compact sands. The expanded element was initially 1.2 m long and about 0.95 m long after expansion. The remainder of the tieback was unbonded. The results of the tension tests conducted on both types of tiebacks showed that the tiebacks with expanded elements provided on average about three times the resistance measured for conventional anchors, even in less competent soils. RÉSUMÉ Des fondations et des tirants avec des éléments expansés ont été utilisés dans le passé, y-inclus des ancrages au sol, des pieux enfoncés, vibrés et forés. Cet article présente un programme d'essais mené sur un site à Ottawa, Ontario, Canada, comparant les performances d'un tirant conventionnel (ancrage à torons avec méthode d'injection par gravité) et d'un tirant plus court avec un ancrage expansé. Les tirants conventionnels (ancrages à torons) avaient un diamètre de 150 mm et ont été construits jusqu’à une profondeur de 28 m : 18,0 m de longueur non liée et 10 m liés dans des sables denses avec un coulis gravitaire. Des tirants ancrés avec des éléments expansés ont été installés dans un trou de 228 mm de diamètre et ont été expansés à une profondeur de 7 m dans des sables meubles à compacts. La partie expansée mesurait initialement 1,2 m de long et environ 0,95 m de long après l'expansion. Le reste du raccord n'était pas lié. Les résultats des essais de traction effectués sur les deux types de tirants ont montré que les tirants avec éléments expansés fournissaient en moyenne environ trois fois la résistance mesurée pour les ancrages conventionnels, même dans des sols moins denses. Keywords: tieback, expanded tiebacks, anchors 1

INTRODUCTION

A conventional grouted ground anchor is a structural element installed in soil or rock that is used to transmit an applied tensile load into the ground. A ground anchor is installed within the bottom part of grout-filled drilled hole bonding a rod or one or more strands to the soil. The rod or strands are extended to the surface to form a “tieback”. The basic components of a grouted anchor consist of the following: • • •

Anchor Free stressing (unbonded length) Bonded length

Ground anchors and anchored systems have been used extensively in the past and are becoming increasingly more common and cost effective through improvement in design methods, construction techniques, and on-site testing (verification and validation). The support of excavation (SOE) for temporary conditions is generally stated to be for “short term” (generally 18 to 24 months); however, delays

in construction schedules and unforeseen site conditions often result in longer implementation periods. The benefits of anchored walls for SOE and retaining walls over sloped excavations and gravity retaining walls include unobstructed work area for excavations, reduced clearance requirement, ability to withstand large horizontal wall pressures without increasing wall cross-sections, elimination of the need for deep foundation support, elimination of the need for select backfill, and reduced construction time etc. (Sabatini et al., 1999). As will be discussed herein, of the efficacy of ground anchors varies based on the anchor type, some of which are more effective than others in specific soil conditions. In this paper, two types of anchors are directly compared in terms of performance in loose to dense sandy soils. 2

GROUND ANCHORS

The schematic in Figure 1 shows the main components of a standard grouted ground anchor. As discussed by

Sabatini et al., (1999), the following three main variations of the grouted ground anchor are commonly used across North America: • • •

Straight shaft gravity-grouted anchor Straight shaft pressure-grouted anchor Post grouted anchor

A variation, though not commonly used in Canada is the underreamed ground anchor. The schematics (Sabatini et al., 1999) illustrate the different variations of grouted anchors.

2015). The EB is installed in a bored pile/anchor and then, injected with grout, producing an expanded element. EB technology has been used successfully to increase the resistance of bored piles, anchors, and tiebacks in different soils. The expansion process compacts the surrounding soil and increases the toe size, thereby increasing the resistance of the pile/anchor in bearing and tension. Many studies have documented the increased resistance of piles using expander body (Herrera and Arce, 2016; Terceros A et al., 2022; Terceros and Terceros, 2015). The EB is supplied in different sizes and different expanded diameters to match the intended application and soil type. The general diameter of the commonly used EBs, prior to expansion, ranges between 110 mm and 145 mm. The length of the units can be 1,200 mm and 1,500 mm. The full expansion of an EB leads to a final diameter of the EB to range between 400 mm to 800 mm depending on the EB model, with a corresponding shortening of the expanded element by up to about 300 mm. The expansion of an EB unit is illustrated in the photographic sequence shown in Figure 3 (after Terceros et al., 2015).

Figure 1. Components of ground anchor (Sabatini et al., 1999)

Figure 3. Expansion steps of the EB (Terceros and Terceros, 2015). The grouting process of the liquid-tight EB takes place under controlled conditions without leakage; enabling measuring the gradual increase in EB volume and required inflation pressure, which can be correlated to resistance. All relevant parameters such as the flowrate, pressure and volume of grout can be recorded with a data acquisition system or manually using analog sensors. The applied grouting pressure reflects the soil resistance during expansion of the EB and is the measure of soil stiffness and strength at the time of inflation. The grouting record is obtained for each inflated EB and offers complete means of quality control (Terceros and Terceros, 2015). Figure 2. Main types of ground anchors (Sabatini et al., 1999) 3

EXPANDED ANCHORS

The concept of expanded anchor, also known as the expander body (EB), was first invented by the Swedish Engineer Bo Skogberg during the 1980’s (Berggren et al., 1988) and later developed and evolved in Bolivia by Mario H Terceros. The EB consists of a folded steel “balloon” that is installed at the tip of a deep foundation element (pile) or a tieback (Fellenius et al., 2018; Terceros and Terceros,

4

TEST PROGRAM

A test program was conducted at a site in Ottawa, Ontario, Canada, comparing the performance of a conventional tieback (strand anchor with gravity grouting) and a shorter tieback with an expanded anchor (EB). The objective of the test program was to establish the most economical and feasible tiebacks to be used for a temporary SOE. It should be noted that the test was intended to verify the ultimate load (failure) of the tiebacks. All test anchors were less than 5 m apart.

5

SUBSURFACE CONDITIONS

The location of the test anchors was selected by the Geotechnical Engineer (Kiewit) to be representative of the excavation site. Based on the borehole investigation and site observations, the following is a representative soil profile at the location of the anchor testing: Three meters of loose to compact silt and sand fill, followed by loose to compact silty sand extending to a depth of 12.5 m. Below lies a layer of very stiff silty clay with some sand extending to 17.8 m, followed by compact to very dense sand and gravel extending beyond 25.0 m depth (the depth of exploration). The groundwater table was encountered at a depth of 6.1 m. 6

EXPANDED ANCHOR INSTALLATION DETAILS

Two test anchors were installed on June 20, 2022, by Marathon Underground Construction (Marathon). One of the test EB units was sized for an 800 mm expanded diameter and 1.2 m initial length (EB812). The second test EB was sized for a 600 mm expanded diameter and 1.2 m initial length (EB612). The hole was drilled by reverse circulation with combined air and water. A 230 mm o.d. temporary steel casing was pushed in place to a depth of 7 m while the hole was drilled. It should be noted that the assembly above the EB anchor was wrapped with a polyethylene sheet to prevent bonding. The preassembled typical EB setup shown in Figure 4 was then lowered in the bored hole. Thereafter, hole was tremie-grouted around the EB assembly and the casing was withdrawn.

connected to the coupling on the capping plate. A purging valve mounted on the grouting manifold was used to purge the assembly (expelled air) until grout reached the cap. The purge valve was then closed, and grout was pumped under pressure until the EB anchor was fully inflated (about 325 to 350 liters). As no flowmeter was used, the pumped volume was estimated from the level of grout pumped out of the mixing drum at the grout plant. The grout pressure during pumping was noted and plotted against the cumulative pumped grout volume as shown in Figure 5. Similarly, pressure grout was injected into the EB612 assembly, and the EB anchor was fully inflated (about 225 litre). The grout pressure during the pumping was plotted against the cumulative pumped grout volume as shown in Figure 6.

Figure 5. EB812 grouting data

The EB assembly consisted of the EB anchor, a Williams 46 mm threaded tieback rod, Grade 150 ksi, and a 100 mm o.d. steel pipe welded to the top plate of the EB, which would serve as the pressure grouting conduit. The rod was secured with a matching nut welded to the bottom EB plate and contained within the grouting pipe. The top 600 mm of the rod was wrapped in plastic sheathing for bond breaking. A steel cap with a mounted grouting assembly coupling was welded at the top of the grouting pipe which was about 500 mm above the top of the tieback rod to allow for upward movement of the rod during inflation of the EB body. Figure 6. EB612 grouting data 7

Figure 4. Expander Body Anchors (EB Anchors) Four days after the initial installation, pressure grout was injected into the EB812 assembly through a sacrificial valve

CONVENTIONAL ANCHOR INSTALLATION DETAILS

Three gravity grout test anchors were installed in May 2022 and two additional gravity grouted test anchors were installed in June 2022. Each anchor was installed with 7 strands of 0.6-inch diameter, low relaxation, grade 270 ksi, conforming to ASTM A416. Details of the anchor installation are provided in Table 1. Note that all anchors were grouted by tremie method except anchor TA04, which was not post-grouted.

Table 1. Conventional anchor details

8

Test Anchor

Anchor diameter (mm)

Number Unbonded of length (m) strands

Bonded length (m)

Total length (m)

TA01

150

12

12.5

9.0

21.5

TA02

150

12

14.0

9.0

23.0

TA03

150

12

14.0

9.0

23.0

TA04

150

12

18.0

10.0

28.0

TA05

250

12

18.0

10.0

28.0

CONVENTIONAL ANCHOR TEST RESULTS

All conventional anchors were tested by Marathon in accordance with the PTI DC35.1-14 recommendations. The design load (DL) in this case was specified at 720 kN. Note that the test were with cyclic loading (performance test); however, none of them reached the required test load of 1.33xDL due to excessive movements observed during the test. Tabulated and graphical results of all the five anchor tests are summarized in Table 2. The proof test load provided in Table 2 are the maximum sustained load prior to failure. Table 2. Conventional anchor test results Test Anchor

Maximum test load (kN)

Notes

TA01

150

Anchor pulled out about 300mm at 38% DL

TA02

150

Anchor pulled out about 200 mm at 45%DL

TA03

150

Anchor pulled out about 150 mm at 33%DL

TA04

150

Cyclic loading conducted, creep failure at 75% DL

TA05

250

Cyclic loading conducted, creep failure at 75% DL

Figure 8. Test results for Anchors TA04, and TA05 9

EB ANCHOR TEST SETUP AND DETAILS

A reaction beam with center access for the anchor rod projection was placed by Marathon, centered over the anchor location. A 300-ton hollow hydraulic cylinder (provided by Marathon) and a 200-ton Geokon Model 3000 load cell (provided by SACL) were used to apply and measure the load. Two Novotechnik TRS electronic displacement transducers (DT) were used to monitor the vertical displacement of the anchor head at opposite ends of the top bearing plate. Displacement measurements were referenced to two tripods, one on each side of the reaction beam. Figure 9 shows a photo of the test setup at the anchor head.

Graphical test results for Anchors TA01, TA02, and TA03 are shown in Figure 7.

Figure 9. Pile head and test instrumentation setup 10

Figure 7. Test results for Anchors TA01, TA02, and TA03 Graphical test results for Anchors TA04, and TA05 are shown in Figure 8

EB ANCHOR TEST PROCEDURE

While anchor testing is conventionally performed per the Post Tensioning Institute (PTI) guidelines, the EB anchors were tested to failure in accordance with ASTM Standard D3689-07 (2022), Procedure A. A target test load of 1,400 kN, which is close to the yield strength of the rod, was used to set the load increments. As such, 20 equal increments of 70 kN, each sustained for 4 minutes, until a failure load was reached. The anchor was then unloaded in 5 decrements.

11

EB ANCHOR TEST RESULTS

EB812 A practical load limit of the anchor was encountered at about 1,350 kN at which the load could not be sustained without continuous pumping. The anchor was unloaded after a total movement of about 59.4 mm in five equal decrements sustained for 4 minutes each. The net displacement was about 41.5 mm after full unloading (about 17.9 mm elastic rebound). The measured loadmovement data is shown in Figure 10. A second cycle of continuous loading to about 950 kN was initiated after unloading. Preselection of the 950 kN load was done in accordance with the PTI testing procedure (1.33 times design load) to check for creep movements. No creep movement was observed.

Figure 10. Applied load vs displacement – EB812 EB612 This anchor was loaded in 17 equal increments of 70 kN, each sustained for 4 minutes, until a total load of about 1,200 kN was reached, at which point higher loads could not be sustained. The anchor was unloaded in 5 decrements each sustained for 4 minutes. A total movement of about 53.2 mm was recorded before unloading. The net displacement was about 36.6 mm after full unloading (about 16.6 mm elastic rebound). The measured load-movement data is shown in Figure 11.

12

COMPARISION OF TEST RESULTS

As shown clearly from the test results, conventional anchors at this site performed with very soft behavior marked by excessive movement and substantial creep during early loading stages. While the deeper conventional anchors (TA04 and TA05) sustained somewhat higher loading than the shallower ones (TA01, TA02, and TA03), there is clear evidence that post grouting of the anchors did not have any effect on anchor performance. This can be seen in the test results shown in Figure 8 since Anchor TA04 was not post grouted. A direct comparison of the performance of the EB anchors versus the conventional anchors is difficult since the main criterion for anchor performance is stability. The conventional anchors pulled out prematurely and an accurate test of their stiffness (creep test) at lower loads was not possible since they rely mainly on friction which deteriorates with large displacement. The performance of the expanded anchors was more robust throughout the test. At certain stages of the loading, EB anchors appeared to show slight strain hardening (see Figure 10 and Figure 11). Furthermore, even after pullout failure, reloading to lower load levels produced near zero creep showing even stiffer response than the original loading stage to the same load. This is evident in comparing Run 1 and Run 2 in the EB812 test results shown in Figure 10; keeping in mind that the maximum load in Run 2 (950 kN) was sustained for four minutes with no creep movement. A photo of the extracted test EB is shown in Figure 12.

Figure 12. Exhumed EB anchor 13 Figure 11. Applied load vs displacement – EB612

DISCUSSION AND RECOMMENDATIONS

There were signs of sand boils rising into casings of piles drilled at the site after the testing, which could explain a possible loss of density in the bond zone. Possibly, the deeper sands where the conventional anchors were embedded had changed into a loose state due to the

drilling process. Unfortunately, there were no detailed investigations to assess the conditions causing the loose behavior. Regardless of the deep sand conditions, the EB test anchors were purposefully installed in shallow loose to compact sands with the objective of improving the soils to a denser consistency around the anchors and achieving higher performance without deep drilling. With the effect of the inflation process preloading the anchor reaction, it is anticipated that movements would be small when the actual tieback load is applied. Furthermore, since a large portion of the EB anchor resistance is developed in bearing, an increase in resistance with anchor movement can be expected as the bearing sands are further densified. Hence, the EB anchors can be expected to show larger stiffness when reloaded. As opposed to post grouting in conventional anchors, EB inflation is contained, and the grout bulb is near symmetrically shaped (see Figure 12) optimizing the anchor efficiency. In contrast, free postgrouting in loose sands is unpredictable and may find paths of least resistance to migrate away from the anchor system with little or no benefit to the performance of the anchor. This characteristic gives EB anchors a significant advantage in excavation support by reducing the risk of brittle pullout failures and fatigue from cyclic loading. In conclusion, the EB anchors at this site showed three to five times the capacity of much deeper conventional anchors with substantial bonded length varying between 8 and 10 m. These anchors have also been successfully used in marine clays where conventional anchors are practically not feasible. Expanded anchors (EBs) are versatile and should be considered as a viable alternative in many difficult soil conditions. 14 ACKNOWLEDGEMENTS The authors wish to extend their gratitude to Dr. Bengt H. Fellenius for his valuable input and guidance. 15

REFERENCES

Berggren, B., Sellgren, E., and Wetterling, S. (1988). Expanderkroppar. Anvisningar för dimensionering, utförande och kontroll (Expander Body. Instructions for design, installation and control). Swedish Commission on Pile Research, Report 79. Fellenius, B.H., Massarsch K.R., Terceros M.H., and Terceros, M.A., (2018). A study of the augmenting effect of equipping piles with an Expander Body. Proc. of DFI-EFFC International Conference on Deep Foundations and Ground Improvement, Rome, June 6 - 8, 2018, pp. 114123. Terceros, M. A., and Terceros, M. H. (2016). Recent Advances In The Expander Body Technology. Sabatini, P. J., Pass, D. G. and Bachus, R C. (1999) "Ground anchors and anchored systems" Office of Bridge Technology, Federal Highways Administration, p. 4-15

ASTM D3689-07, (2022) Standard Test Methods for Deep Foundation Elements Under Static Axial Tensile Load. ASTM International Terceros M. H, and Terceros M. A. (2015). The use of the expander body with full displacement piles in medium dense sandy soils. Fourth Geo-China International Conference 2016, pp. 142-151. Terceros M.A., Terceros, M. H., and Marinucci, A. (2022) Foundation support and underpinning of an existing hotel with expander bodies to increase axial resistance. Australian Geomechanics Society, May 1 – 5, 2022, pp 3387

Deep Soil Mix Application in High Temperature Environment Kirill Bobko, Sachin Patel Klohn Crippen Berger, Calgary, Alberta, Canada

ABSTRACT The application of Deep Soil Mixing (DSM) was studied as a method of ground improvement for the foundation of a nearshore embankment and seepage control berm that is subjected to a high temperature environment up to 140ᵒC. The effect of binder factor in Deep Soil Mix design was studied with binders including Type G oil well cement, a blend of fly ash and Portland cement, and a blend of ground granulated blast furnace slag (GBFS) and Portland cement. Total of 83 unconfined compressive strength (UCS) tests were performed after curing specimens at temperatures of 30°C, 50°C, 90°C and 140°C. The study focuses on evaluating the mechanical properties of the resulting soil-cement mixtures. RÉSUMÉ L'application du Deep Soil Mixing (DSM) a été étudiée comme méthode d'amélioration du sol pour la fondation d'un remblai près du rivage et d'une berme de contrôle des infiltrations soumise à un environnement à haute température jusqu'à 140ᵒC. L'effet du facteur de liant dans la conception de Deep Soil Mix a été étudié avec des liants comprenant du ciment pour puits de pétrole de type G, un mélange de cendres volantes et de ciment Portland, et un mélange de laitier de haut fourneau granulé broyé (GBFS) et de ciment Portland. Au total, 83 tests de résistance à la compression non confinée (UCS) ont été effectués après durcissement des éprouvettes à des températures de 30°C, 50°C, 90°C et 140°C. L'étude porte sur l'évaluation des propriétés mécaniques des mélanges sol-ciment obtenus.

1

STUDY AREA

Application of this Deep Soil Mix (DSM) technique was studied for foundation of a near-shore embankment and seepage control berm of mining facility. DSM was designed to reduce to acceptable level seismic-induced deformations of Embankment foundation and seepage control berm that is subjected to a high temperature environment up to 140°C. Suggestions included conducting a laboratory mix testing program to understand the achievable engineering properties for the DSM for the soils (and temperature ranges) likely to be encountered in-situ. The DSM ground improvement is relatively narrow with the purpose of providing a stable foundation below the filters and protection against erosion of the Quaternary Sediments. This DSM was planned therefore to be constructed through the Quaternary Sediments and bear on competent bedrock (Middle Argillics) as shown on Figure 1. The Seepage Control Berm (SCB) and Central Downstream Berm (CDB) are located downstream of the Harbor Waste Platform (HWP) where loose in-situ marine deposits are expected to liquefy during an earthquake event and potentially result in displacements within the Seepage Barrier embankment. Numerical modeling conducted during the interim feasibility phase of the Project

showed that seismic-induced deformations in the Secondary Embankment can be reduced to acceptable levels when the marine deposits and Upper Argillic units within foundations for the embankment are treated in a grid pattern using the Deep Soil Mixing (DSM) technique.

Qm

Rockfill

Qa

DSM Argillic/Volcanic

Qa Upper Argillic/Va

Figure 1. Schematic Design of DSM application for Rockfill This paper describes laboratory work performed as part of DSM design phase. At the time this paper is published no field work was implemented and no filed tests on DSM were conducted. 1.1

Literature Review and Case Histories

Literature review of DSM application for different case histories demonstrated that UCS strength falls in the range of 0.5 to 8.0 MPa. Laboratory program was conducted based on available case histories outlined in Table 1.

Table 1. Binder factor for selected case histories Binder Type UCS at 28 and α days and type (kg/m3) of sample

Project

Soil type

Auckland Motorway Project1 (New Zealand)

soft highly plastic silts and clays with sand lenses and variable organic contents

Yodo River Dyke2 (Japan)

100 sandy layer overlying a clay (slag layer cement)

350 (P. cement)

~ 4.5 MPa (cored)

0.5 - 4.5 MPa

Oriental Hotel, 200 sandy layer Kobe Port2 overlying a clay (slag (Japan) layer cement)

4 - 6 MPa (lab specimens)

Oil storage tanks3 (USA)

1.7 - 3.1 MPa (field samples)

soft clay overlying dense sand

237 (P. cement)

New Orleans levee test section3 (USA)

soft to medium 200 fat clay with lenses of sands (P. cement) and silts

Test section3 (Japan)

silty clay

Bridge foundation for the Shinkansen train2 (Japan)

alluvium layer: soft soil (c = 8 kPa) clay, sand

~ 1.3 MPa (lab samples)

100 (P. cement)

~ 1 MPa (cored samples)

620 (soft soil) 280 (clay) 150 (sand)

from cored samples: 1 MPa in soft soil 5 - 8 MPa in clay ~ 5MPa in sand

1Thorp

et al. (2015) and Terashi (2013) 3Swedish Deep Stabilization Research Centre (2005) 2Kitazume

2

SOIL CHARACTERIZATION

The DSM grid will comprise a lattice structure composed of overlapping DSM columns through the Quaternary Marine Sand (Qm) and Mixed Marine Deposits (Qa), and the Upper Argillic deposits, terminating in the Argillic rock foundation. Four boreholes were drilled to obtain sufficient Qm and Qa material for use in the laboratory testing program. Depth of boreholes varied from 50 m to 65 m. Upper Argillic (U. Arg) material was obtained from within the existing pit. Qm and Qa samples collected from the investigation were transferred to laboratory, where soil moisture contents were measured, and the samples were processed into a composite Qm-Qa material. This Qm-Qa soil was then transferred to other laboratories for use in the DSM laboratory test program.

Figure 2. UDR1200 (ARDS D11) drill rig on site. 3

TESTS

A staged approach to the laboratory test program was developed to progress the feasibility design phase through to detailed design. 3.1

Criteria and Objectives

The objectives of the laboratory testing were to: 1. Test various cementitious binders with the materials likely to be encountered on site. The three nominated binders for this testing program were: • API Certified Class G oil-well cement (OWC) manufactured by LaFarge, Calgary; • A blend of ground granulated blast furnace slag (GGBFS) with Portland cement manufactured by Sunstate Cement Ltd; and • A blend of fly ash (FA) with Portland cement manufactured by LaFarge, Canada. 2. Test each soil-cement mixture when cured at the upper and lower-bound temperatures expected in-situ, as follows: • Marine and alluvial Quaternary deposits (Qm-Qa) soil-cement cured at 30°C and 90°C; and • Upper Argillics (U.Arg) soil-cement cured at 50°C and 140°C. 3.2

Mixing Procedure

A review of case histories was carried out to support the development of the mix design for the DSM. Based on the case history review, the interim feasibility design UCS value of 2.1 MPa (in the field), was considered within the range of documented strengths in case histories with soil conditions similar to site. Based on these case histories, a binder factor (defined as the ratio of weight of dry binder to volume of soil to be treated), α, of between 200 kg/m3 and about 350 kg/m3 would be required to provide a DSM strength in the range of 2.1 MPa. For the laboratory program a mix design (Mix A) considered to be towards the upper bound limits for this site was adopted using a binder factor of 400 kg/m3, and minimum slurry water to cement ratio of 0.8. Mix A was designed in accordance with FHWA (2013).

3.3

High Temperature Treatment Apparatus and Testing Method

DSM soil-cement specimens were batched using the Japanese laboratory test method (Kitazume et al. 2013). Soil samples were moisture conditioned to the in-situ moisture content (39 % for Qm/Qa and 30 % for U. Arg.) with deionized water prior to mixing with cement. Qa and Qm soil specimens were combined into one specimen and later referred as Qa/Qm. Soil-cement specimens were then cured for 14, 28 and >120 days, at the required temperatures before testing. Generally, three samples were prepared for each tested combination of temperature, soil type, binder, and test period. Example of tested DSM specimen is illustrated on Figure 4 Batched samples were conditioned to required age in atmospheric curing tanks for temperatures up to 90°C. High temperature treatment (140°C) required special apparatus designed and manufactured for this laboratory program. Metal cylindric molds were fabricated to accommodate three vertically spaced specimens. DSM mix was placed in acrylic molds stacked vertically and separated by porous stones. Then column was loaded into curing cylindrical cell and sealed with metal cap as illustrated on Figure 3. The cells were pressurized to 450kPa and cured at 140°C to desired age. Laboratory testing comprised UCS with measurement of strain in accordance with ASTM D2166. a

Figure 4. Tested Upper Argillic DSM specimen cured at 140°C during 28 days. 4

RESULTS

A total of 83 successful UCS tests were completed, including validation tests. A summary of the UCS test results is provided in Figure 5. The relationship between Peak Stress and Young’s Modulus is presented in Figure 6. Due to the cost of the Oil Well Cement (OWC) compared to GGBFS and Fly Ash, once initial test results indicated comparative strengths could be achieved with the GGBFS and Fly Ash blends, further long-term testing of the OWC was not continued.

b

Figure 5 . UCS Results Summary

Figure 3. a) High temperature pressurized treatment chamber b) Loading process of specimens in acrylic molds

A summary of the laboratory test results, including UCS and calculated Young’s Modulus is presented in Figure 5 to Figure 9. Graphs show Young’s Modulus relationships based on the various binders, materials and temperatures tested.

Figure 6 . Peak Stress v Young’s Modulus (based on temperature).

Figure 8. Peak Stress v Young’s Modulus (based on binder)

Figure 9. Strain Measurement Comparison Figure 7 . Peak Stress vs Young’s Modulus (based on material).

•

• • • • Figure 10. UCS Summary – Average trend over time 5

DISCUSSIONS AND CONCLUSIONS

Based on the observations made during laboratory program, the following conclusions are made: • Fly Ash demonstrated optimal performance and was a preferred binder for this DSM design. • A binder factor (α) of 400 kg/m3 is adopted for costing purposes. • A minimum laboratory UCS strength of 2 MPa is adopted for the DSM elements. This value should be factored in accordance with the required standards for design purposes. • The strength stiffness relationship E=200 UCS (based on Figure 9) is used for modelling and design purposes at the feasibility stage. • 90°C GGBFS mix demonstrated strength reduction over the period of 140 days, while similar mix cured at 140°C showed upward trend for the given curing time. Further long-term strength testing >365-days is recommended to assess whether the on-going strength degradation observed at 140-days in the GGBFS continues. • UCS tests at short-term duration cures (between 7 and 28-day) show significant variation in both the magnitude of change in UCS and trend (increase or decrease). The use of 7 or 28-day test results as an indication for final strength requires additional long-term UCS test data. Laboratory test program was designed with a broad scope to test different binders, materials and curing temperatures. Usually, three tests were carried out for each combination of binder, temperature and material and therefore the sample size for each combination is relatively small. Although small sample sizes, the small spread of test results, particularly within the Qm/Qa materials suggests high degree of repeatability of testing and confidence in the results. The validation testing carried out at subcontracted laboratory also confirm the validity of the test results. Given the small sample size statistical analyses of the results has not been carried out. The following observations can be made from the UCS test data:

•

•

Overall properties of the composite materials appear to be controlled principally by the native material type (U.Arg or Qm/Qa) and temperature rather than binder type. U.Arg composite materials are in general, stiffer, stronger and more brittle than the Qm/Qa material irrespective of binder. Materials cured at lower temperatures, particularly the Qm/Qa are stiffer, stronger and more brittle than higher temperature cured materials. There are distinctly lower long-term strengths for Qm/Qa material cured at 90°C compared to same material cured at 30°C. There is a convergence of long-term strengths at 140days for both the Fly Ash blend and GGBFS, at 30°C and 90°C but, the strength of the GGBFS at 30°C appears to still be decreasing at 140-days, whilst the Fly Ash blend appears to have stabilised. There is a distinct change in bulk density for the Qm/Qa material (for all three binder types) cured at 90 °C compared to 30 °C. The change in bulk density appears to be a result in swelling (volume change) of the specimens during curing. The cause of the changes is not known at this point, but may be a result of deleterious mineralogy such as reactive (swelling) clays, pyrite (or other iron sulphide minerals). DSM laboratory test program has not achieved the target interim feasibility UCS of 4.2 MPa. A long-term laboratory minimum peak strength of 3 MPa, corresponding to an in-field strength of 1.5 MPa has been achieved during the laboratory tests.

REFERENCES FHWA 2013. Deep Mixing for Embankment and Foundation Support, Design Manual. Publication No. FWHA-HRT-13-046. FHWA 2017. Ground Modification Methods, Reference Manual, Vol. I and II. Publication No. FWHA-NHI-16027. Kitazume, Masaki, and Masaaki Terashi. 2013. The deep mixing method, Vol. I and II. CRC Press/Balkema, Leiden, Netherlands. Nguyen T.V., Rayamajhi D., Boulanger R.W., Ashford S.A., Lu J., Elgamal A., and Shao L. 2013. Design of DSM Grids for Liquefaction Remediation. Journal of Geotechnical and Geoenvironmental Engineering, ASCE, 139(11): 1923-1933. Swedish Deep Stabilization Research Centre. 2005. Deep Mix 05. International Conference on Deep Mixing Best Practice and Recent Advances, Stockholm, Sweden. Thorp Y.F., Chin C.Y. and Jia M. 2015. Design and Construction of Ground Improvement for Liquefiable Soils at Bridge Abutments for the Lincoln Road Interchange Auckland Motorway Project. 6th International Conference on Earthquake Geotechnical Engineering, Christchurch, New Zealand

Exploring the thermo-mechanical characteristics of modified rammed Earth treated with pulp mill fly ash and hydrated lime Sarbesh Sharma, Sumi Siddiqua & Chinchu Cherian Faculty of Applied Science, School of Engineering – University of British Columbia, Kelowna, British Columbia, Canada V1V 1V7

Abstract Rammed Earth (RE) is an ancient construction technology with properties such as low embodied carbon and nature-based material. RE has been in practice for centuries but is still not widely approved as a standardized building material due to misconceptions about its strength and lack of structural design and building codes. This paper explores the mechanical and thermal properties of hydrated lime (HL) and pulp mill fly ash (PFA) treated RE materials. The two optimum RE design mixes with 5% and 10% PC treated with HL and PFA achieved up to 85% and 200% improvement in compressive strength, respectively, compared to the control specimen with 10% PC. The freeze-thaw durability and thermal conductivity results of treated RE specimens showed the significance of PFA and HL binders in reducing cement content and enhancing the strength of RE without compromising the weather durability and thermal characteristics. Abstraite Rammed Earth (RE) est une technologie de construction ancienne avec des propriétés telles qu'un faible taux de carbone incorporé et un matériau à base de nature. L'ER est pratiqué depuis des siècles mais n'est toujours pas largement approuvé en tant que matériau de construction standardisé en raison d'idées fausses sur sa résistance et de l'absence de conception structurelle et de codes de construction. Cet article explore les propriétés mécaniques et thermiques des matériaux RE traités à la chaux hydratée (HL) et aux cendres volantes d'usine de pâte à papier (PFA). Les deux mélanges de conception RE optimaux avec 5 % et 10 % de PC traités avec HL et PFA ont permis d'améliorer jusqu'à 85 % et 200 % de la résistance à la compression, respectivement, par rapport à l'échantillon témoin avec 10 % de PC. Les résultats de durabilité au gel-dégel et de conductivité thermique des échantillons de RE traités ont montré l'importance des liants PFA et HL dans la réduction de la teneur en ciment et l'amélioration de la résistance du RE sans compromettre la durabilité aux intempéries et les caractéristiques thermiques.

1

Introduction

The countries face a significant challenge and mounting pressure to fulfill their commitment to achieving net zero emissions while managing the substantial infrastructure demand due to rapid urbanization and population growth. The construction industry plays a significant role in carbon emissions, accounting for 33% of global GHG emissions (Sizirici et al. 2021). The construction industry is a primary stakeholder in Canada, contributing 7.5% to the country's GDP (Canadian Construction Association,2023).To address this issue, seeking low-cost and easily obtainable construction materials that contribute to a multidisciplinary approach towards resource utilization, waste reduction, and green construction is necessary. Rammed Earth (RE) is a technology with a long history and still relevant in the present and future that utilize local and natural materials for sustainable building construction. Rammed Earth is a traditional building method that dates back to 7500 BC and utilizes on-site materials to construct

monolithic structures with a temporary framework made of local soil (Berge, 2009). After the arrival of burnt brick, RE fell out of favour. It has regained popularity in modern construction (Siddiqua & Barreto, 2018). RE technology offers various benefits, such as minimal manufacturing impact, increased structural durability, aesthetic appeal, thermal mass, sound and fire resistance, and low maintenance costs (Burroughs, 2008; Maniatidis et al. 2003; Windstorm & Schmidt, 2013). RE is a lowcarbon construction technique that meets both sustainable and energy-saving standards. Stabilized Rammed Earth combines modern practices and technologies with an ancient building method (Morel et al. 2007). The stabilization process enhances RE's mechanical performance and durability, with lime and cement commonly used as stabilizing agents. Another essential aspect of the current research is to expand the use of RE in the construction sector and work on its wider acceptance. Developing RE that can give higher strength is necessary to bring it as an alternative sustainable construction technology to replace cement and concrete. The construction industry uses 10% - 20%

2

Materials and Methods:

2.1 Soil The soil used in this experiment was collected from a local quarry in Kelowna, BC, Canada. The oven-dried soil was passed through a 2 mm sieve and later passed through a 425 mm sieve to separate into coarse and fine fractions. The model soil was prepared, consisting of coarse (>425 mm) and fine ( Mesh", the user can select among some pre-defined objects. To open the "Add" menu, the user can press the keyboard shortcut "Shift+A" as well (Figure 1).

Figure 1: The Add menu and the basic pre-defined Mesh options in Blender. Navigating the 3D modelling space in Blender can be initially confusing, and as a result, an introduction to the various navigation tools can be beneficial. By default, every added shape is positioned on the Cursor, and numerous operations can be performed on each shape. Table 1 containing the different navigation and transformation tools, along with their corresponding keyboard shortcuts, is available for reference. Table 1: Default navigation and transformation tools in Blender. Function Camera Move Camera Rotate Camera Zoom Object Center Object Select Object Move Object Rotate Object Scale

Command Shift + Middle Mouse Button Middle Mouse Button Ctrl + Middle Mouse Button “.” On Keyboard “W” On Keyboard “G” On Keyboard “R” On Keyboard “S” On Keyboard

The modelling of the shear box can be initiated by a Cube mesh. After adding a new Cube mesh and setting the model dimensions (press “N” on keyboard to set the dimensions manually), we can start manipulating the object in the “Edit Mode" (press Tab on the keyboard to enter the edit mode). While in Edit Mode, different faces, edges, or vertices of the object could be individually manipulated and different tools of transformation are available. To make a shear box, one can go to Face Selection Mode (“3” on the keyboard), select the top face of the Cube (Figure 2a), and select the “Insert Faces” tool (Figure 2b). Then, by selecting the inserted face and using the “Extrude Region” tool, the box-like appearance is created (Figure 2c). For a more realistic look, one can also employ the Bevel tool on certain edges to reduce the sharpness of the angles (Figure 2d).

4.2

Texturing and UV mapping

To achieve the objective of creating AR/VR models that are engaging, presentable, and realistic, it is necessary to incorporate colors and textures into the 3D models. To accomplish this, one shall access the texturing environment in Blender called Shader Editor window (press Shift+F3). By selecting each part of the model, new material can be assigned. Blender's node-based editor, called the Shader Editor or Compositor, allows the user to create material networks using texture nodes. This provides more control over how textures are applied and blended (Figure 4).

Figure 2: After setting the desired dimension, the top face is selected in the Edit mode (a). Then, using the Insert Faces tool, a face is added (b). With the Extrude region tool, the box-like appearance is created (c). For a more realistic look, the Bevel tool is applied to certain edges (d). Using the several loop cuts and extruding, two metal plates with ridges are created to be placed on top and bottom of the sample (Figure 3a, b). The lid is also created by inserting a face on top of a cube; however, the face is moved up instead of being extruded to make the pyramidlike effect (Figure 3c, d). Figure 4: Material network and texture nodes in Blender. Color map, Roughness map, and Normal map are added to this material. In this paper, AmbientCG website is used for textures. This website provides high quality textures up to 8K under the Creative Commons CC0 1.0 Universal License, meaning near limitless freedom to use the materials, models, and other assets. A list of different textures and their sources is provided in Table 2. Table 2: Different textures and their sources used for the direct shear test model. Texture Cell Plates Sand

Figure 3: After creating several loop cuts using the Loop Cut tool (a), the selected faces are extruded upwards (b) to create ridged metal plates. For the lid, the inserted face (c) is moved upward using the Move tool, to make a pyramidlike effect (d). The soil sample consists of two cubes with a height map, more explained in the following section.

Source ambientCG.com/a/Metal004 ambientCG.com/a/Metal010 ambientCG.com/a/Gravel036L

From time to time, the user might notice that some textures may not properly be placed on the 3D model in the initial attempt. In this situation, UV mapping is needed which is the process of assigning a 2D texture map to the surface of a 3D object. In Blender, UV mapping involves defining a 2D coordinate system on the surface of a 3D object, which determines how textures and images will be applied to the object surface. Blender does that by unwrapping the 3D model's surface into a 2D plane, available through "UV Editing" tab. In this tab, each face of

the 3D model can be manipulated like a flat image and adjusted to the right orientation and size of the texture. A visual explanation of UV mapping and its necessity on a simple case is presented in Figure 5.

Figure 6: Color map (a), Normal map (b), and Roughness map (c) for sand in direct shear test model. After properly UV mapping on one of these maps, the other maps are automatically adjusted as well.

Figure 5: The first attempt for adding texture to a material might not always be successful (a,b). The reason is that in the first attempt, Blender would try to unwrap the faces of the model on the texture by itself, which may not be what we have in mind. We need to assign the faces manually through UV mapping (c) to get a nicely textured Polytechnique logo cube (d). Textures downloaded from AmbientCG are also accompanied with normal and roughness maps. Normal maps and roughness maps are two types of texture maps that can be used to enhance the appearance of a 3D model's surface without modifying the geometry of the mesh. A normal map (Figure 6b) is a type of texture map that encodes surface normals in the RGB channels of an image (LearnOpenGL, 2015). By applying a normal map to a 3D model's surface, it can create the illusion of surface details and depth that aren't actually present in the mesh. This can give the model a more realistic and textured appearance. Roughness maps (Figure 6c), on the other hand, control the roughness or smoothness of a surface. A roughness map typically encodes the surface roughness information in grayscale, with black areas indicating a completely smooth surface, and white areas indicating a completely rough surface. Applying a roughness map to a surface can help create a more realistic material appearance by controlling the way that light interacts with the surface. By using normal maps and roughness maps, highly detailed and realistic models can be created without needing to add additional geometry to the mesh.

Another map, which is less often used, is a displacement map. When a displacement map is applied to a 3D object, it deforms the surface geometry based on the grayscale values. This allows for intricate surface details, such as bumps, wrinkles, or textures, to be added while altering the underlying geometry of the object. As the mesh for the 3D object becomes finer, the amount of surface detail increases in expense of an increase in the size of file. To give the sand object a more grainy-look, displacement map is also utilized. After subdividing the sand block a few times, a “Displace” modifier is added to the object and the displacement map is used as texture (Figure 7a). For the proper placement of the displacement map on the object, the “Coordinates” dropdown must change to “UV”. In this way, the displacement map would perfectly align with color, normal, and roughness maps previously applied to the object. Once the modifier is set as desired, it must be applied so that the effects are visible in the output file (Figure 7b).

Figure 7: A Displacement modifier is added to the sand object, and the displacement map is uploaded as texture (a). After achieving desired results, the modifier is applied (b). The textured sand object on top of the metal plate is shown in (c).

4.3

Animating and export

To animate an object in Blender, the desired stages of the animation need to be keyframed. Firstly, the object is selected in the 3D Viewport, and the desired frame is chosen from the Timeline window. Then, the object's properties in that frame are fixed by pressing the "I" key on the keyboard, which triggers the insertion of a keyframe. By selecting another frame and inserting a new keyframe with different properties, such as changes in location, rotation, or scale, a smooth transition is created between the two keyframes. By adding sufficient steps and keyframes, the 3D models can be brought to life. During the direct shear test, the lower part of the device is pushed under a horizontal force (shown by the red arrow) and loaded under a vertical force (blue arrow). Three stages of loading is animated in 250 frames, with arrows increasing in size representing greater force at each stage, and overall increase in the volume of sand representing a dense sample (Figure 8).

animated object, such graph exists and can be tampered with.

Figure 9: Scale graph in Z direction for the top sand object. The X axis shows the number of frame (time) and the Y axis shows the scale value. 4.4

Figure 8: Different stages of the animation: a) Frame 50; b) Frame 120; c) Frame 190; d) Frame 240 The Graph Editor window in Blender provides a comprehensive overview of the keyframes and transitions in the form of graphs, allowing the user to analyze and refine the animation. By selecting this window, the user gains access to a visual representation of the object's properties over time. By adjusting and fine-tuning the position of the keyframes and manipulating the transition curve, the user can precisely control the timing and smoothness of transitions between keyframes. This level of control enables the user to create more refined and polished animations, enhancing the overall quality and realism. This process allows for precise control and manipulation of objects, resulting in dynamic and engaging animations. In Figure, the graph related to the top sand object is presented. By comparing the change in sand size presented in Figure 8 (the orange outline) and the graph, the different stages of the animation are better comprehended. For each changing property of every

Sketchfab: A publishing platform

Sketchfab is a popular online platform that serves as a hub for publishing and discovering 3D content. It provides a user-friendly interface for creators to showcase their 3D models and animations to a wide audience. With a large community of users and a vast collection of millions of models, Sketchfab offers an ideal platform for sharing and promoting 3D models. It also provides additional features and tools to enhance the presentation of 3D content, including annotations, material editing, and post processing filters. The platform supports interactive viewing, allowing users to rotate, zoom, and explore models from different angles. Furthermore, Sketchfab offers embedding options, enabling the user to showcase 3D models on websites, blogs, or social media platforms. Sketchfab has a well integration with Blender, providing support for native .blend format. Hence, the created model can directly be uploaded to Sketchfab without any exporting measures. To make the models more accessible and compatible with different software and devices, they are automatically converted by Sketchfab into various formats such as glTF, GLB, and USDZ. These formats are widely supported and can be easily viewed on different platforms, including web browsers and mobile devices (Figure 10). Sketchfab viewer also supports VR mode. With the use of tools such as Google Cardboard, users can view the models in a VR environment as well by turning their smartphones into VR headsets (Figure 11).

allows students to visualize the processes involved in each test and grasp the underlying principles more effectively. 6

Figure 10: Windows 3D Viewer can easily play GLB models downloaded from Sketchfab (a). The USDZ format is specifically designed for AR applications on iOS devices (Schechter, 2020). Devices running on iOS 12 and up can view 3D models in AR directly from Safari or other compatible apps, without the need for additional software or plugins (b).

The integration of AR/VR models in geotechnical engineering education presents a promising solution to the challenges associated with laboratory testing in related courses. The limitations of time, resources, and student engagement can be addressed through the use of AR/VR models. By leveraging tools like Blender, AR/VR models can be developed to simulate soil mechanics laboratory testing, providing students with a unique opportunity to interact with virtual objects and soil samples. The immersive nature of AR/VR technology allows students to observe 3D representations (digital twins) of soil samples subjected to various loading conditions, thereby enhancing their understanding of laboratory testing principles. In conclusion, the integration of AR/VR models developed with Blender into soil mechanics education has the potential to revolutionize the way students learn and comprehend laboratory testing principles. By providing an immersive and interactive learning environment, AR/VR models empower students to explore and engage with virtual objects, enhancing their understanding and paving the way for future advancements in engineering education. 7

Figure 11: Triaxial test device in Sketchfab VR mode. 5

RESULTS

By following the outlined procedure, several demonstrative models have been created, encompassing various laboratory tests in the field of soil mechanics. These models include the hydrometer test, direct shear test for loose sand, and multiple triaxial loading tests (Table 3). Each model provides a visual representation of the respective test, allowing students to engage with the concepts and gain a deeper understanding. Table 3: Some created and uploaded digital twins for soil mechanics laboratory. Model Triaxial test - CU Loading Triaxial test - CD Loading Triaxial test - Mohr Failure Direct Shear Test - Loose Sand Direct Shear Test - Dense Sand Hydrometer Test

Sketchfab Link skfb.ly/oHEJU skfb.ly/oHELG skfb.ly/oHEFR skfb.ly/oHEED skfb.ly/oHEKn skfb.ly/oHrMI

The availability of a growing inventory of 3D models would ensure enhancing students' learning experiences. Using these models, students can observe and interact with virtual representations of the laboratory tests, bridging the gap between theoretical knowledge and practical application. The immersive nature of AR/VR technology

CONCLUSION

REFERENCES

Azuma, R.T. 1997. A Survey of Augmented Reality. Presence: Teleoperators and Virtual Environments, 6(4): 355-385. doi:10.1162/pres.1997.6.4.355. Feiner, S., MacIntyre, B., Höllerer, T., and Webster, A. 1997. A touring machine: Prototyping 3D mobile augmented reality systems for exploring the urban environment. Personal Technologies, 1(4): 208-217. Hu, X., Goh, Y.M., and Lin, A. 2021. Educational impact of an Augmented Reality (AR) application for teaching structural systems to non-engineering students. Advanced Engineering Informatics, 50: 101436. LearnOpenGL. 2015. Normal mapping. Available from https://learnopengl.com/Advanced-Lighting/NormalMapping. Meža, S., Turk, Ž., and Dolenc, M. 2015. Measuring the potential of augmented reality in civil engineering. Advances in Engineering Software, 90: 1-10. Milgram, P., Takemura, H., Utsumi, A., and Kishino, F. 1994. Augmented reality: A class of displays on the reality-virtuality continuum. Telemanipulator and Telepresence Technologies, 2351. Poh, E., Liong, K., and Lee, J.S.A. Mixed Reality for Engineering Design Review Using Finite Element Analysis. In 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMARAdjunct). 17-21 Oct. 2022 2022. pp. 657-662. Scaravetti, D., and Doroszewski, D. 2019. Augmented Reality experiment in higher education, for complex system appropriation in mechanical design. Procedia CIRP, 84: 197-202.

Schechter, S. 2020. Everything You Need to Know About USDZ Files. Available from https://www.marxentlabs.com/usdz-files/. Schnabel, M.A. 2009. Framing Mixed Realities. In Mixed Reality Applications in Architecture, Design, and Construction. Springer Netherlands. pp. 3-11. Soliman, M., Pesyridis, A., Dalaymani-Zad, D., Gronfula, M., and Kourmpetis, M. 2021. The Application of Virtual Reality in Engineering Education [online]. Applied Sciences 11(6) [cited]. Vásquez-Carbonell, M. 2022. A Systematic Literature Review of Augmented Reality in Engineering Education: Hardware, Software, Student Motivation & Development Recommendations. Digital Education Review,(41): 249-267. Wang, S., Gui, Y., Liu, X., Xiong, D., and Tian, Z. 2021. Application of VR Technology in Civil Engineering Teaching in Colleges and Universities. Academic Journal of Architecture and Geotechnical Engineering, 3(2): 7-13.

Enhanced Wick-drain in Dewatering of Iron Mine Tailing - Large Scale Test Reza Mahmoudipour Englobe, Toronto, Ontario, Canada Geotechnical Department, Toronto, Ontario, Iran

ABSTRACT In geotechnical engineering, two conventional methods, electro-osmosis and wick drains (also known as prefabricated vertical drains or PVD), are commonly employed to mechanically enhance fine-grained saturated soils. This research aims to combine these two methods to develop a novel approach. Various configurations were examined, and comparisons were made between the new product and each method individually to identify the optimal configuration, pattern, and degree of improvement. Samples were collected from the thickener at the Golgohar iron ore mine and arranged into seven large-scale testing pools. Square and triangular patterns, along with constant and alternating poles, were investigated in this study. The results demonstrated that the combination of these technologies improved the mechanical properties, with the most significant enhancement in undrained shear strength observed in pools employing an enhanced Wick drain featuring constant poles and a triangular pattern. RÉSUMÉ En génie géotechnique, deux méthodes classiques, l'électro-osmose et les drains à mèche (également appelés drains verticaux préfabriqués ou PVD), sont généralement utilisées pour améliorer mécaniquement les sols fins saturés. Cette recherche vise à combiner ces deux méthodes pour créer un nouvel approche. Différentes configurations ont été examinées et des comparaisons ont été établies entre le nouveau produit et chaque méthode individuellement afin d'identifier la configuration, le motif et le degré d'amélioration optimaux. Des échantillons ont été prélevés dans l'épaississeur de la mine de fer de Golgohar et disposés dans sept bassins d'essai à grande échelle. Des motifs carrés et triangulaires, ainsi que des pôles constants et alternés, ont été étudiés dans le cadre de cette étude. Les résultats ont démontré que la combinaison de ces technologies améliorait les propriétés mécaniques, avec une amélioration significative de la résistance au cisaillement non drainé observée dans les bassins utilisant un Wick drains amélioré avec des pôles constants et un motif triangulaire.

1

INTRODUCTION

The installation of prefabricated vertical drains, commonly known as wick drains, is a widely employed technique in fine-grained and saturated soils. Wick drains serve to reduce drainage distances by facilitating water flow through the shortest path within the saturated layer. Typically, wick drains are used in conjunction with surcharge methods, such as embankments, pools, heavy construction machinery, nailing, and electro-osmosis. Electro-osmosis is another prevalent method used for remediation in finegrained and saturated soils. By combining these two methods, improved and expedited outcomes can be achieved. Since 2000, electrokinetic geosynthetics (EKG) have emerged as a composite of these two methods. Extensive research and numerous publications have been dedicated to exploring the potential of EKGs. These conductive geosynthetics offer the combined advantages of wick drains and electro-osmosis. In this study, a similar concept is utilized, but with the development of a new product. This product combines electrodes with conventional wick drains, which can even be created using scrap metals, making it a more sustainable option. Notably, this product has been patented in Iran.

2

PREVIOUS RESEARCH

Lemont-Black et al. (2005) investigated the use of EKG for in-situ sewage sludge dewatering. They observed that the removal of topwater led to improved electro-osmotic dewatering, resulting in increased solid content and shear strength. Fourie (2006) utilized EKG for the dewatering of wet cohesive fill to achieve a specific shear strength. He identified challenges associated with the electro-osmosis technique, such as electrode corrosion, loss of soilelectrode contact, excessive energy consumption, and logistical water collection issues. However, he believed that EKG addressed the corrosion problem, making it a viable solution for various applications. Fourie et al. (2007) employed EKG for in-situ dewatering of sand mine tailings and achieved a reduction in water content from 158% to 75% with low energy consumption (0.9 kWh/dry tonne). They attributed this success to the use of a low voltage gradient (0.11 V/cm) and suggested further investigations on different tailings. Lemont-Black (2007) emphasized that diamond mine tailings were suitable candidates for electro-osmotic dewatering, as reduced moisture content facilitated easier conveyor transportation. Additionally, Hall et al. (2008) demonstrated that EK filtration bags could effectively dewater materials, making them easier to handle manually

or with machinery, which is particularly advantageous in mine tailings. Shenbaga et al. (2011) conducted experiments on consolidation using EKG and found that drained water primarily collected at the cathode. They determined that a voltage gradient of 120 V/m yielded optimal electro-osmotic consolidation results. Similarly, Karunaratne (2011) compared wick drains and EKG for soft clay consolidation and concluded that EKG significantly accelerated the consolidation rate. Colin et al. (2011) discussed various factors influencing EKG applications, such as legal requirements, climate change, carbon footprint reduction, water reclamation, and waste reduction and reuse. Glendinning et al. (2015) highlighted the significance of EKG in dewatering sludges and tailings, emphasizing its potential for active geosynthetic applications. Zou et al. (2017) utilized an electrically conductive wick drain combined with an automated power supply to dewater hydraulically filled sludge ground, resulting in an average moisture content decrease from 62% to 39%. Visigalli et al. (2017) demonstrated that electro-osmotic dewatering reduced moisture content by up to 42.9% in different types of sludge. Tang et al. (2017) investigated electro-osmosis on marine soil and found that a higher voltage of 12V resulted in an average water content decrease of 9.3% more than at 6V. They also observed greater water content reduction on the anode side, with the middle water content closer to the anode side. Fu et al. (2017) studied drainage effects during intermittent and reverse electrifying periods in electroosmosis. They found that the drainage effect during a 1hour power-off mode was equivalent to that during a 0.20.25-hour electrifying reverse period. Bourges-Gastaud et al. (2017) evaluated the effectiveness of electrokinetic geocomposite on sand tailings and achieved a solids content increase from 45% to approximately 70%. This transformation from fluid fine tailings (FFT) to mature fine tailings (MFT) reduced the risk of leaks, failure, and migration of contaminants. 3

Figure 1. (a) Patented drainage media (wick drain and electrode) (b) original CeTeau’s wick drain. The core of the drainage media consists of flexible polypropylene with high water flow capacity along the grooves, while the geotextile is made from durable, nonwoven polypropylene (Soil retention filter O90= 80x10-6 m³/s). For the modification, two flat steel bars were incorporated on both sides of the core within the wick drain, enabling the utilization of both techniques in a single product. The width of both the modified and original wick drain is approximately 4 inches, with a thickness of ≥3mm. In this study, six pools with dimensions of 3.88m x 2.96m and a depth of 2.3m were utilized. These pools were filled with iron tailings obtained from the thickener in the form of slurry up to a depth of 1.8m. The dry tailings underwent soil classification testing, resulting in a classification of ML (Silt). The particle distribution is presented in Figure 2.

PREPARATION OF SAMPLES AND NOVEL DRAINAGE MEDIA

In this study, a newly patented drainage media was utilized, combining electrodes and Wick drains to harness the advantages of both methods simultaneously. To achieve this, CeTeau's wick drain was selected and modified to accommodate the electrodes. Figure 1 illustrates a comparison between the patented drainage media (wick drain and electrode) and the original CeTeau's wick drain.

Figure 2: Unified Soil Classification Curve of the Tailing Utilized in this Study After the pools were filled, the new drainage product (electrode and Wick drain) and electrodes were manually installed in the pools following the pattern depicted in Figure 3. Subsequently, a one-day setting time was allotted, and any excess water accumulated on the surface was removed using a siphon. The arrangement of the pools and electrodes was as follows:

7

4.

Figure 3. The pattern of treatment in the pools. In the provided image, it is evident that pools #1, 2, 3, and 4 feature a square pattern, while pools #5 and 6 exhibit a triangular pattern. Figure 2 illustrates the blue electrodes as cathodes and the red electrodes as anodes. Notably, the poles in pools #2, 4, and 6 were alternated daily. Furthermore, pools #1 and 2 were equipped solely with electrodes, whereas the other pools utilized a combination of Wick drains and electrodes. This installation enables a comprehensive comparison of the pattern's impact, alternating poles versus constant poles, as well as the performance of the new product with electrodes alone.

Wick drain

Rectangular

N/A

TEST PROCEDURE

At the beginning of the test, the slurry had an initial solid content of approximately 50%. Coagulant was present in the tailing samples, which were obtained from the thickener. After a one-day setting time, the solid portion of the tailing settled, and the clear water on top was siphoned out. As the test progressed, the slurry transformed into a paste-like consistency. Each day, soil samples were collected from the surface near each electrode at an equal distance, ensuring comparability. It is important to note that after the initial days of the test, there was no free water on the surface to be siphoned as most of it had evaporated. The moisture content measurements over time are depicted in Figures 5 and 6. The samples were taken from a consistent radius around similar electrodes to ensure comparability, although some fluctuations are observable in the graphs. Similar fluctuations were also reported by Barooti et al. (2019), whereas Lemond-Black et al. (2005) observed a smoother trend in their study.

A DC rectifier with a voltage of 48V served as the power supply, with an electrode spacing of 1m, resulting in a voltage gradient of 48V/m. Figure 4 provides a depiction of how the new drainage product was installed in pool #4, with red cables connecting the anodes and blue cables connecting the cathodes to the electrical power source. Fig. 4 shows the pattern of electrodes in the pools, and Table 1 shows the configuration of each pool.

Figure 5. Moisture content in Anode-Pool #1, 3, and 5

Figure 4: Installation of the New Drainage Product in Pool #4 Table 1. Configuration of pools Pool#

Type of treatment

Pattern

Pole Change?

1

Electro-osmosis

Rectangular

Yes

2

Electro-osmosis

Rectangular

No

3

Enhanced

Rectangular

Yes

4

Enhanced

Rectangular

No

5

Enhanced

triangular

Yes

6

Enhanced

triangular

No

Figure 6. Moisture content in Cathode-Pool #1, 3, and 5.

Figure 10. Moisture content in depth-Pool #2, 4 and 6 Figure 7. (a) Moisture content in Anode-Pool #2, 4, and 6,

5.

Figure 8. Moisture content in Cathode-Pool #2, 4, and 6.

In pools #1, 3, and 5, where the poles were alternated, there was a greater reduction in moisture content measured adjacent to the electrodes on the surface compared to pools with constant poles. However, the overall decrease was only around 5%, and the moisture content on the anode and cathode sides remained relatively unchanged. Figures 7 and 8 illustrate that, in general, the moisture content increased with depth. It is important to note that the results in these figures show lower total moisture in pools #2, 4, and 6, which is contrary to the surface moisture content results. This discrepancy can be attributed to the fact that the samples taken from around the electrodes on the surface were consistently wet, indicating the effectiveness of the combination of electrodes and wick drains in bringing water to the surface. However, relying solely on the moisture content of surface samples taken near each electrode may not accurately represent the overall conditions. Therefore, studying moisture content at different depths between two electrodes is crucial for drawing accurate conclusions. The reduction in moisture content was more pronounced in pool #6 across different depths (triangle pattern). In pools with alternating poles, the square pattern yielded better results (pools #3 and 5). Lemont-Black et al. (2005) conducted a comparison between a rectangular pattern and a hexagonal pattern, demonstrating that the hexagonal pattern led to a greater overall reduction in moisture content.

In each pool, two test pits were excavated to measure the moisture content at various depths. Samples were collected at intervals of 20cm along the test pits. The excavation of the test pits was done at the center of the electrodes. The moisture content results at different depths are presented in Figures 7 and 8. The terms "red" and "blue" are used to differentiate between anode and cathode. However, in the pools where the poles alternated (i.e., pools 1, 3, and 5), there was no distinction between the poles.

6.

Figure 9. Moisture content in depth-Pool #1, 3, and 5

ANALYSIS OF THE RESULTS

CONCLUSION

Wick drains, and electrodes were combined to create a new product that utilized both techniques simultaneously for dewatering purposes. The soil under investigation consisted of iron mine tailings classified as silt (ML). Near the electrodes, the moisture content in the pools where the direct current alternated was reduced by approximately 5%. The maximum reductions were observed in pool #1, with a reduction of 70%, and pool #3, with a reduction of 40%, where constant poles were utilized. Upon analyzing the moisture content at different depths between the electrodes, it became apparent that the moisture content increased with depth. Interestingly, in pools #2, 4, and 6, which employed constant poles, a more

significant reduction in moisture content was observed at various depths compared to the surface near the electrodes. This finding contradicts the observations made near the electrodes on the surface, where water accumulated in the case of constant poles. The results indicated that in pools utilizing constant poles, the total moisture content reduction was more pronounced in the middle of the electrodes. The most favourable outcomes were achieved in pool #6, where the combined method of using both electrodes and wick drains was implemented alongside constant poles. 7.

REFERENCES

Barooti, A., Ardakani, A., Mahmoudipour, M. (2019). Evaluation of the effect of voltage variation on the electroosmosis dewatering of silty soil using prefabricated vertical drains. International Journal of Geotechnical Engineering, (October 2019), DOI: 10.1080/19386362.2019.1677400 Bourgès-Gastaud, S., Dolez, P., Blond, E., Touze-Foltz, N. (2017). Dewatering of oil sands tailings with an electrokinetic geocomposite. Minerals Engineering, 100, 177–186. DOI: 10.1016/j.mineng.2016.11.002 Fourie, A.B., Johns, D.G., Jones, C.F. (2007). Dewatering of mine tailings using electrokinetic geosynthetics. Canadian Geotechnical Journal, 44(2), 160–172. DOI: 10.1139/t06-112 Fu, H., Fang, Z., Wang, J., Chai, J., Cai, Y. (2017). Experimental Comparison of Electro-Osmotic Consolidation of Wenzhou Dredged Clay Sediment Using Intermittent Current and Polarity Reversal. Marine Georesources & Geotechnology, 0618(June). DOI: 10.1080/1064119X.2017.1326992 Hall, J., Glendinning, S., Lamont-Black, J., Jones, C. (2008). Dewatering of Waste Slurries Using Electrokinetic Geosynthetics (EKG) Filter Bags. In EuroGeo4 (pp. 1–8). Jones, C.J.F.P., Lamont-Black, J. (2015). The Use of Electrokinetic Geosynthetics to Improve Soft Soils. Ground Improvement Case Histories. Elsevier Ltd. DOI: 10.1016/B978-0-08-100191-2.00013-7 Karunaratne, G.P. (2011). Prefabricated and electrical vertical drains for consolidation of soft clay. Geotextiles and Geomembranes, 29(4), 391–401. Elsevier Ltd. DOI: 10.1016/j.geotexmem.2010.12.005 Lamont-Black, J., Glendinning, S., Jones, C., Huntley, D., Smith, R. (2005). The development of in-situ dewatering of lagooned sewage sludge using electrokinetic geosynthetics. 10th European Biosolids and Biowaste Conference, 6(November), 1–8. Penman, A. (2006). Discussion of "Harnessing the power: Opportunities for electrokinetic dewatering of mine tailings." Geotechnical News, 24(3), 52–53.

Tang, X., Xue, Z., Yang, Q., Li, T., VanSeveren, M. (2017). Water content and shear strength evaluation of marine soil after electro-osmosis experiments. Drying Technology, 35(14), 1696–1710. Taylor & Francis. DOI: 10.1080/07373937.2016.1270299 Visigalli, S., Turolla, A., Gronchi, P., Canziani, R. (2017). Performance of electro-osmotic dewatering on different types of sewage sludge. Zou, W.L., Zhuang, Y.F., Wang, X.Q., Vanapalli, S.K., Huang, Y.L., Liu, F.F. (2018). Electro-osmotic consolidation of marine hydraulically filled sludge ground using electrically conductive wick drain combined with automated power supply. Marine Georesources and Geotechnology, 36(1), 100–107. DOI: 10.1080/1064119X.2017.1312721

The importance of data quantity in machine learning: how small is too small? Beverly Yang1, Andrew Tsai2, Amichai Mitelman3, Rita Tsai2 & Davide Elmo1 1NBK Institute of Mining Engineering – University of British Columbia, Vancouver, BC, Canada 2Equilibrium Mining, Vancouver, BC, Canada 3Ariel University, Ariel, Israel ABSTRACT The past decade has seen rock engineering become more data-driven, resulting in increased use of machine learning (ML). ML is a type of artificial intelligence that involves the development of mathematical models, resulting in a computer system capable of making predictions with minimal human involvement. Such a powerful tool can help rock engineers efficiently uncover complex relationships between data and has been used to predict rock mass properties, mining and tunnelling hazards, and slope stability. However, the success and reliability of ML models are directly linked to the quality and quantity of data available. ML models for rock engineering applications are generally trained using either poor-quality or limited data. This inherently leads to poor and unreliable results with potential real-life adverse impacts. Both data quality and quantity pose significant challenges in rock engineering due to the subjective nature of many commonly used rock engineering parameters (resulting in poor-quality data) and the limited data available in the early stages of the design process. While there has been an increased awareness of the importance of data quality for ML among rock engineers, there has yet to be a comparably increased awareness of the importance of data quantity. Many rock engineering research articles focused on ML are training their ML models on only a few hundred data points, with some as few as 80 data points. However, these results can be misleading due to the stochastic nature of ML models and how the data is shuffled before data splitting, resulting in unreliable models. Using synthetic data and surrogate models, this paper aims to demonstrate the importance of data quantity in ML, and it recommends using caution when using small datasets for ML.

1

INTRODUCTION

Digitalization in rock engineering has led to an increase in the use of data-driven methods to make rock engineering more accurate and efficient. One increasingly popular datadriven method is machine learning (ML), a type of artificial intelligence where mathematical models help a computer system uncover relationships between data, with the end goal of having this computer system learn without direct human instruction (Azure, 2021). ML has the potential to revolutionize rock engineering by increasing efficiency and sustainability, all while lowering costs (Global Mining Guidelines Group, 2019). However, ML models' success and reliability depend on the quality and quantity of data used during the training process. The quality and quantity of data available for ML in rock engineering can pose significant challenges due to the subjectivity and empirical nature of the data collection process in rock engineering and the limited resources to adequately describe the spatial and temporal variability of rock parameters. While there has been increased awareness of the importance of ML data quality among rock engineers, the importance of data quantity has continued to be overlooked. Unlike other disciplines, it can be difficult in rock engineering to acquire enough data for ML; many of the data rock engineers work with are non-standardized, non-digitized, and confidential, making it challenging to obtain the amount of data commonly found in other disciplines, such as computer science. Recent examples of articles focused on the use of ML in rock engineering are

training their ML models on only a few hundred data points (Gholami et al., 2013; Shen & Jimenez, 2018; Koopialipoor et al., 2022; Mahmoodzadeh et al., 2022; Chen & Zhang, 2022; Fathipou-Azar, 2023; etc), with some as few as 80 data points (Hu et al., 2022). This is in stark contrast to the datasets used for ML in other industries, which are on the scale of hundreds of thousands and are constantly updating (e.g., NASA Prediction of Worldwide Energy Resources). Using small datasets for ML can lead to unreliable results, with significant variability in how the model performs depending on how the data was randomly split into its training and test datasets. The goals of this paper are twofold: the first is to highlight the importance of data quantity in ML by demonstrating the impact of how the data is randomly shuffled into the training and test datasets, and the second is to provide a workflow to help rock engineers determine if they have enough data to use ML reliably. We have intentionally refrained from stating a minimum number of data points needed for a reliable ML model as it depends on a variety of factors; the aim is to equip rock engineers with the knowledge of the importance of data quantity in ML and a methodology to help them determine if they have enough data to reliably use ML. 2

BACKGROUND

A typical supervised machine learning problem involves splitting the data into its training and test datasets (referred to as the train/test split in this paper); the training dataset

is used to train the ML model, while the test dataset is used to evaluate the performance of the trained ML model on new, unseen data. This is performed to better understand how well the ML model will generalize (i.e., how well it will perform on new and unseen data (Google, 2023)). In scikitlearn - a Python package used for machine learning - the data is randomly shuffled before this split. The random state parameter in the train_test_split function controls the randomization of the data shuffling. The train/test split allows us to obtain training and test scores; the training score represents how well the trained ML model performs on its training dataset, and the test score represents how well the trained ML model performs on the test dataset. Because the ML model has never seen the test dataset before, the ML model should not perform better on the test dataset than the training dataset. The training and test scores allow us to evaluate how well the ML model performs. Overfitting and underfitting are two terms used to describe when a model can’t capture the relationship between the input and output variables. Overfitting occurs when the model is too complex and starts to fit the noise in the training data and can be identified when the training score is significantly better than the test score (i.e., the model performs better on the training data than the test data), while underfitting occurs when the model is too simple and can be identified when both the training and test scores are low (i.e., the ML model performs poorly on both the training and test data) (IBM, 2023a; IBM, 2023b). Overfitting is a common problem with smaller datasets; however, another less commonly discussed problem when using smaller datasets for ML is when the model performs better on the test data than the training data. This can be found in Chen & Zhang (2022) and is a function of how the data was shuffled before a train/test split rather than the ML model performing well. The ML model is likely unreliable if this occurs and this problem is explored in further detail in the remainder of the paper. A more robust method of evaluating the performance of an ML model is by including a validation dataset. In this case, the training dataset is further split into a training and validation dataset; the ML model is trained on this smaller training dataset and then tuned or refined on the validation set. Performing a single train/validation split runs the risk of misrepresenting the test data, especially with smaller datasets, so it is often recommended that cross-validation is performed, where the training set is divided into k number of folds (5 and 10 folds are commonly used) and each fold is used as a validation dataset. A validation score is determined for each fold, and the validation scores for all folds are then averaged to determine an average validation score. A validation set was not used in our ML modelling to keep the methodology simple.

3

METHODOLOGY

A synthetic dataset generated from an SWedge probabilistic analysis (Rocscience, 2023) was used for the ML modelling. A synthetic dataset was used because the authors could not find any publicly available rock

engineering dataset large enough for our analyses. While outside the scope of this paper, the lack of open-source datasets highlights the importance of data (and code) sharing as rock engineering begins to widely adopt datadriven methods such as ML. The importance of data and code sharing for ML applications in rock engineering has also been highlighted in a recent editorial (Rock Mechanics and Rock Engineering, 2023) and discussion (Yang and Elmo, 2023) in Rock Mechanics and Rock Engineering. To address the issue of data quantity in rock engineering, which is an integral aspect of developing reliable and practical ML models, we recommend that rock engineering encourages open-source data sharing between academics and industry partners. The data and code used in this paper can be found at the following link https://github.com/beverlyyang/GeoSaskatoon2023. An outline of the methodology used in our modelling is shown below in Figure 1 and discussed in further detail in the subsequent subsections.

Figure 1. Methodology for the ML modelling in this paper. 3.1

Data Generation

A synthetic dataset generated from an SWedge probabilistic analysis (Rocscience, 2023) used for evaluating structurally-induced slope instability was used for this ML modelling exercise. A Latin Hypercube sampling method with a pseudo-random number generator (a seed of 1 was used) was employed to generate 5,000 data points. Surrogate modelling was employed, whereby the inputs of a numerical program (in this case, SWedge) are used as inputs into an ML model, and the output of the numerical program is the output or target of the ML model (Furtney et al., 2022; Mitelman et al., 2023). A simplified wedge problem was used in the probabilistic analysis, where only the orientation of the slope face and the orientation and friction angle of the two joints were considered to determine the safety factor. A tension crack was not considered, and the joints were assumed to be dry. A Mohr-Coulomb failure criterion was used to estimate the strength of the joints, with the cohesion set to zero. Table 1 below shows the ranges and distributions of the input

parameters of the SWedge and ML model. Figure 2 shows the stereonet with the slope face and two joints. Table 1. Distribution of input parameters to generate data. Input Parameter

Slope

Joint 1

Joint 2

Joint 1

Joint 2

Dip (°) Dip direction (°)

Mean Min Max

Standard Deviation

Distribution

60

50

70

2

Normal

180

170 190

2

Normal

Dip (°)

40

-

-

-

Dip direction (°)

130

-

-

-

Dip (°)

55

-

-

-

Dip direction (°)

230

-

-

-

Fisher K = 40

Cohesion (MPa)

0

-

-

-

-

Friction angle (deg)

30

20

40

2

Normal

Cohesion (MPa)

0

-

-

-

-

Friction angle (°)

30

20

40

2

Normal

Fisher K = 40

Figure 2. Stereonet of the wedge problem used for the data generation from SWedge (Rocscience, 2023). 3.2

ML Modelling

A Random Forest (RF) algorithm was used for the ML modelling as they are considered robust models adept at handling small datasets with minimal to no data preparation. As a result, data standardization and hyperparameter tuning were not performed during our ML modelling. Minimal data preparation was performed by

removing duplicates or invalid wedges (8 data points). All of the ML modelling was performed in Python using scikitlearn’s packages. Using the synthetic dataset generated in Section 3.1, RF models were developed using varying dataset sizes, ranging from 100 to 4950 data points, with a step of 50. 80% of each dataset was used for training, while the remaining 20% was used for testing, and the subsequent training and test scores were determined for each dataset. For each model, four different random_state values in the train_test_split function in scikit-learn were examined: 0, 1, 42, and 123. A learning curve (i.e., dataset size vs test score) for each random_state value was plotted to examine the impact of the random_state value on the model results. The performance metrics used for this analysis are R2 and root mean square error (RMSE). 4

RESULTS

Figures 3 and 4 on the following page show the test R2 and RMSE plotted against the total dataset size for the four different random_states examined, respectively. Note that the y-axis on Figure 3 was cut-off at R2 = 0 due to the large negative R2 values occurring at smaller dataset sizes. For dataset sizes less than 2,000 data points, varying the random_state parameter in the train_test_split (i.e., varying how the data is randomly shuffled before data splitting) can result in significantly different ML model results, despite the same dataset being used, making these ML models unreliable. The difference in the test R 2 and RMSE begin to plateau at around 1,750 – 2,000 data points, meaning that there is enough data such that how the data is randomly shuffled prior to data splitting does not significantly impact the ML model results. For this specific example, this can be considered the amount of data rock engineers should aim to have before implementing ML, as this generally implies that both the training and test datasets are representative of the problem at hand. Note that this range of dataset sizes is not universally applicable, and its purpose is to demonstrate the variability of ML model results with smaller datasets.. To get a better understanding of potential overfitting, underfitting, and the model performing better on the test set than the training set, the difference between the test and train RMSE was plotted against the dataset size (Figure 5). RMSE was used over R2 as the results in Figure 3 demonstrate that R2 may not be the best performance metric for this specific application due to the large negative values. While the topic of choosing an appropriate performance metric is outside the scope of this paper (further details can be found in Yang et al., 2022), this analysis provides an excellent example highlighting the pitfalls of only using R2 to assess ML model performance.

Figure 3. Learning curves for four different train/test splits using R2 as the model performance metric.

Figure 4. Learning curves for four different train/test splits using RMSE as the model performance metric.

Figure 5. Difference in train and test RMSE for different dataset sizes and train/test splits. As expected, overfitting can be found in smaller datasets (less than 750 data points). Note that an arbitrary cutoff (test – train RMSE ≥ 0.75) was used in Figure 5 to denote overfitting; this value was chosen for the specific problem at hand, and a lower or higher value can be used in other applications. However, it is also important to note

the area highlighted in Figure 5 demonstrating dataset sizes where the ML model performs better on the test data than the training data for specific random_state values. In this particular example, this occurs in dataset sizes of less than 1,000 data points, which are dataset sizes commonly found in rock engineering literature to train ML models.

In this example, a dataset size of 250 data points results in an ML model that performs better on the test data than the training data for a random_state of 42 but also results in an ML model that overfits for a random_state of 0. An ML model that performs better on the test data than the training data, especially when they have been trained on only a few hundred data points, does not mean that the model performs extremely well; instead, it’s a sign that the model results should be reviewed in further detail as it most likely means that there is not enough data to reliably use ML. While the figures above demonstrate the unreliability of ML models trained on smaller datasets and their dependency on the random_state value in the train/test split, this does not mean that the random_state parameter should be optimized. The random_state parameter in the train/test split is not a hyperparameter and cannot and should not be tuned to achieve the “best” results. In an ideal world, there is enough data such that the random_state value (i.e., how the data is shuffled into its training and test datasets) has no significant impact on the ML model results. If it impacts the ML model results, then that indicates that there is not enough data to reliably use ML. 5

LIMITATIONS

The results of these analyses are specific to the synthetic data generated in SWedge and the RF model outlined in the previous sections. While the learning curves for this example demonstrate a plateau at around 1,750 – 2,000 data points, this may not be the case for other problems, and this range should not be taken as a hard and fast rule as the minimum amount of data needed for ML in rock engineering. The amount of data required for a reliable ML model depends on several factors, including, but not limited to, the problem at hand, the ML model used, the data distribution, the amount of noise in the data, etc. The learning curves in this paper demonstrate the variability of the ML model results and the unreliability of the ML model when smaller datasets are used and should not be taken as a universal guideline. 6

CONCLUSIONS AND RECOMMENDATIONS

Obtaining enough data for ML is a challenge in rock engineering, and while attempts to train ML models on small datasets (i.e., a couple of hundred data points) are interesting, their results should be taken with a grain of salt. ML models trained on smaller datasets are susceptible to how the data is shuffled before data splitting (controlled by the random_state parameter in the train_test_split function in scikit-learn), resulting in unreliable models. Using synthetic data and an RF model, this paper demonstrated the significant variability of ML model results trained on smaller datasets (i.e., dataset sizes commonly encountered in rock engineering) and, subsequently, the importance of data quantity in ML. When reviewing or considering applications of ML to rock engineering, the following recommendations can be made: •

Be critical of ML model results. If they seem too good to be true (ex, the model performs better on

the test data than the training data), then that is most likely the case, and both the ML methodology and data should be further examined. • Before implementing ML, rock engineers should first determine if they have enough data for a reliable ML model by running multiple ML models that vary how the data is randomly shuffled prior to the train/test split (in Python/scikit-learn, this would be varying the random_state parameter in the train_test_split function – similar to the methodology outlined in Section 3.2 of this paper) and comparing their test scores. If there is a noticeable difference between the test scores, then it is safe to assume that there is not enough data to reliably use ML. This methodology is summarized below: o Choose 4 – 5 different ways to randomly shuffle the data before the train/test split (this is equivalent to choosing 4 – 5 different random_state values in the train_test_split function in Python/scikit-learn). o Determine the training and test scores for each random_state. o If the test scores between the different random_state values are significantly different and/or the test scores are better than the training scores (i.e., the model performs better on the test data than the training data), then the subsequent ML model is most likely unreliable, and it’s recommended to acquire more data before implementing ML. • Learning curves should be considered a standard plot to include in ML papers in rock engineering. An increment of 50 data points was used in this paper; however, increments of 10 have also been used (Mitelman et al., 2023). If the dataset isn’t large enough to generate a learning curve (ex, smaller than a hundred data points), then that indicates that the dataset is too small to develop a reliable ML model. While ML is an incredibly powerful tool that can revolutionize rock engineering, rock engineers must understand its fundamentals to avoid misuse and to develop ML models that can be reliably and practically used. 7

REFERENCES

Azure. 2023. Artificial intelligence (AI) vs. machine learning (ML): Understand the difference between AI and machine learning with this overview. https://azure.microsoft.com/en-ca/resources/cloudcomputing-dictionary/artificial-intelligence-vs-machinelearning/ Chen, H. and Zhang, L. 2022. A Machine Learning-Based Method for Predicting End-Bearing Capacity of RockSocketed Shafts, Rock Mechanics and Rock Engineering, 55: 1743-1757.

Fathipar-Azour, H. 2023. Shear Strength Criterion for Rock Discontinuities: A Comparative Study of Regression Approaches, Rock Mechanics and Rock Engineering, https://doi.org/10.1007/s00603-023-03302-6. Furtney, J.K. Thielsen, C. Fu, W., and Le Goc, R. 2022. Surrogate Models in Rock and Soil Mechanics: Integrating Numerical Modeling and Machine Learning, Rock Mechanics and Rock Engineering, 55: 28452859. Gholami, R. Rasouli, V. and Alimoradi, A. 2013. An Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms, Rock Mechanics and Rock Engineering, 46: 1199-1209. Global Mining Guidelines Group. 2019. Foundations of AI: A framework for AI in mining. https://gmggroup.org/wpcontent/uploads/2019/10/GMG_Foundations-of-AI-AFramework-for-AI-in-Mining-2019-10-07_v01_r01.pdf Google. 2023. Machine learning glossary. https://developers.google.com/machinelearning/glossary Hu, J. Zhou, T. Ma, S. Yang, D. Guo, M. and Huang, P. 2022. Rock Mass Classification Prediction Model Using Heuristic Algorithms and Support Vector Machines: A Case Study of Chambishi Copper Mine, Scientific Reports, 12. IBM. 2023a. What is overfitting? https://www.ibm.com/topics/overfitting IBM. 2023b. What is underfitting? https://www.ibm.com/topics/underfitting Koopialipoor, M. Asteris, P.G. Mohammed, A.S. Alexakis, D.E. Mamou, A. and Armaghani, D.J. 2022. Introducing Stacking Machine Learning Approaches for the Prediction of Rock Deformation, Transportation Geotechnics, 34. Mahmoodzadah, A. Mohammadi, M. Salim, S.G. Ali, H.F.H. Ibrahim, H.H. Abdulhamid, S.N. Nejati, H.R. and Rashidi, S. 2022. Machine Learning Techniques to Predict Rock Strength Parameters, Rock Mechanics and Rock Engineering, 55: 1721-1741. Mitelman, A. Yang, B. and Elmo, D. 2023. Implementation of Surrogate Models for the Analysis of Slope Problems, Geosciences, 13. NASA Prediction of Worldwide Energy Resources. 2023. https://registry.opendata.aws/nasa-power/ Rock Mechanics and Rock Engineering. 2023. Editorial on Papers Using Numerical Methods, Artificial Intelligence and Machine Learning, Rock Mechanics and Rock Engineering, 56. Rocscience. 2023. SWedge — Surface Wedge Analysis or Slopes. Shen, J. and Jimenez, R. 2018. Predicting the Shear Strength Parameters of Sandstone Using Genetic Programming, Bulletin of Engineering Geology and the Environment, 77: 1647-1662. Yang, B. and Elmo, D. 2023. Discussion on “A Machine Learning‑Based Method for Predicting End‑Bearing Capacity of Rock‑Socketed Shafts, Rock Mechanics and Rock Engineering. Accepted.

Wednesday, October 4, 2023

ROCK MECHANICS II

Analyzing in situ stress: challenges in quantifying stress domains Muhammad Amir Javaid & John P. Harrison Department of Civil & Mineral Engineering – University of Toronto, Toronto, Ontario, Canada Hossein A. Kasani Nuclear Waste Management Organization (NWMO), Toronto, Ontario, Canada Diego Mas Ivars 1) SKB, Swedish Nuclear Fuel and Waste Management Co, Solna, Sweden 2) Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden ABSTRACT Accurately characterizing the state of in situ stress in rock is important for the design of all underground engineering projects, but is crucial for safety‑critical projects such as deep geological repositories for nuclear waste. However, designers continue to be confronted with the challenging task of characterizing the significant variability and uncertainty found in the in situ stress state across the project volume, and particularly identifying separate stress domains. One routine approach is to partition, or group, stress data on the basis of depth below ground. In this paper we discuss a customary approach to identifying stress domains, and illustrate challenges in its application. We go on to present a novel approach that uses Bayesian linear segmented regression of Cartesian stress components to statistically characterize the variability and uncertainty in the depth of stress domain boundaries. Synthetic data are used to demonstrate the suitability and efficacy of this method, and it is then applied to stress measurements in crystalline rock obtained at the Forsmark site in Sweden. We conclude that use of a Bayesian approach is beneficial as it is able to formally augment stress measurement data with other valuable geological information. RÉSUMÉ Caractériser précisément l'état de contrainte in situ est important pour la conception d’infrastructures souterraines, d’autant plus pour des projets critiques au regard de leur sécurité, tels que le stockage géologique de déchets nucléaires. Cependant, la variabilité et l'incertitude importante associées aux contraintes in situ reste difficile à caractériser à l’échelle d’un site, nécessitant généralement l’identification de domaines de contrainte distincts. Une approche standard consiste à séparer, ou grouper, les données de contrainte selon la profondeur dans le sous-sol. Dans cet article, nous discutons d'une méthode classique permettant d’identifier des domaines de contrainte et illustrons les défis liés à son application. Nous présentons ensuite une nouvelle approche utilisant la régression segmentée linéaire bayésienne des composantes de contrainte cartésiennes pour caractériser statistiquement la variabilité et l'incertitude de la profondeur des limites des domaines de contrainte. Des données synthétiques sont utilisées pour démontrer la pertinence et l'efficacité de cette méthode, qui est ensuite appliquée aux mesures de contraintes obtenues en milieu cristallin sur le site de Forsmark en Suède. Nous concluons que l'utilisation d'une approche bayésienne est bénéfique car elle permet d’associer les données de mesure des contraintes avec d'autres informations géologiques précieuses.

1

INTRODUCTION

Characterization of the in situ stress state in rock is crucial for safety-critical projects such as deep geological repositories for safely accessible containment of nuclear waste. Extensive campaigns are often undertaken to obtain estimates of the in situ stress at various locations in the 3D space that comprises the entire project volume. However, such data lead to the challenging task of characterizing the significant variability and uncertainty in the in situ stress state, made more difficult due to the lack of robust and universally agreed methods for characterization of in situ stress. Partitioning stress data into distinct depth-wise horizons is a commonly adopted approach, although there is no well‑established heuristics for this. Further, the variability and uncertainty associated with estimates of the

interface depths of these horizons (stress domains) is often ignored. In this paper we highlight a few of the challenges in identifying the depth stress domains and present a method that can potentially characterize the variability and uncertainty in depth estimates of these stress domain boundaries. We examine a novel application of Bayesian linear segmented regression firstly using synthetic stress data and then applying the method to over 100 overcoring stress measurements obtained at the Forsmark site in Sweden. The results are presented and discussed, and challenges in objectively identifying depth stress domains and characterizing the variability and uncertainty in their boundaries are highlighted.

2

BACKGROUND

The in situ stress state at the Forsmark site has been partitioned (Martin, 2007) into four depth‑wise domains of 0‒150 m, 150‒300 m, 300‒400 m and 400‒1000 m on the basis of two parameters: one third of the first invariant, I1/3, and the ratio of major and intermediate principal stress, σ1/σ2. Smoothed parameter values were obtained by taking the moving median of six measurements. One shortcoming of this approach is that the results are sensitive to both the number and continuity of the measurements used in the smoothing. To demonstrate this we first replicate Martin (2007) analysis using sample size of 6, and then perform the analysis using sample sizes of 10, 15, and 20 (Figure 1). As expected, the smoothing becomes more pronounced with increasing number of samples. On the basis of these profiles it could be argued that there exist two stress domain boundaries at depths of approximately 80 m and 170 m, but the results are not unequivocal. A more pronounced boundary is seen at about 300 m depth, but we believe that this could be a result of the lack of stress measurements between 300 to 400 m. Another, and more serious, drawback to this analysis is the use of the first invariant of stress. This scalar measure ignores the principal stress orientations (Gao & Harrison, 2018a) and can thus wrongly identify quite different stress states (in terms of principal stress orientations) as being equivalent. These two disadvantages indicate that an improved technique is required for identifying stress domains. Furthermore, it is highly unlikely that such crisp or hard stress domain boundaries will exist in any geological environment: it is almost always the case such domain boundaries exhibit considerable uncertainty due to the various geological processes. Recently, Bayesian linear regression of Cartesian stress components has been proposed as a technique for obtaining mean stress estimates from posterior distributions (Javaid et al., 2022a, 2022b). These authors demonstrated the efficacy of the technique by analysing over 100 overcoring stress measurements obtained at the Forsmark site. Here, we introduce a Bayesian linear segmented regression method that can improve the predicted mean stresses together with quantifying the variability and uncertainty associated with depths (boundaries) of the depth stress domains.

Figure 1. Moving median analysis of Forsmark data using four different sample sizes. Depth boundaries of stress domains from Martin (2007) are shown in dashed lines.

requiring a large number of in situ stress measurements. To overcome this shortcoming, Feng & Harrison (2019) and Feng et al. (2020, 2021) proposed a generalized MV Bayesian stress model for use in the case of limited data. The Bayesian stress model assumes that the six distinct components of a complete 3D stress tensor obtained via stress measurement, Ydata, follow a multivariate normal distribution so that Ydata ~ MVN(µ, Ω),

3

METHODOLOGY

In this section the multivariate (MV) stress model, the generalized MV Bayesian stress model and previously proposed Bayesian linear regression model for stress are first explained, and then the method of analysis used in the current studies is introduced. 3.1

[1]

Bayesian linear regression model

A multivariate model for quantifying the variability of in situ stress has recently been proposed (Gao & Harrison 2016, 2017, 2018a, 2018b). This model is faithful to the tensorial nature of stress, but has limited application in practical rock engineering due to it having a frequentist basis and thus

where Ydata = [σx

τxy

τxz

σy

τyz

σz]

[2]

with the prior distributions µ ~ MVN(µ0, Ω0) and Ω-1 ~ Wishart(S, 𝜈).

[3]

As Equation 3 shows, the prior distributions µ and Ω have their own parameters, µ0, Ω0, S, and 𝜈; these are the mean

stress vector, the covariance matrix, the Wishart distribution scale matrix and the number of degrees of freedom, respectively. Following the development of this generalized MV Bayesian stress model, Javaid et al. (2022a, 2022b) proposed a Bayesian linear regression model for estimating the mean stress vectors as: Yij ~ Normal(µij, ωj),

[4]

where µij = β0j + β,

3.2

Bayesian linear segmented regression

The Bayesian linear segmented regression allows us to obtain distributions of the breakpoints between adjacent segments. Here, the breakpoints represent stress domain boundaries and as already noted there is considerable uncertainty in the depths of these. By statistical convention, the linear segmented or piecewise regression model is written using a dummy or indicator variable function (e.g. Young 2017). However, the general linear segmented regression model can be explained more conveniently as in the following algorithm given as Equation 8.

[5]

with β0j = [β0[1] β0[2] β0[3] β0[4] β0[5] β0[6]  = [β0[σx] β0[τxy] β0[τxz] β0[σy] β0[τyz] β0[σz]]T,

[6]

βj = [β[σx] β[τxy] β[τxz] β[σy] β[τyz] β[σz]]jT.

[7]

]T

and

As Equations 5–7 show, the Cartesian stress components are regarded as independent response variables, with the explanatory variable being depth below ground surface. Therefore, ωj in Equation 4 are the estimates of standard deviations for all individual response variables. The terms in Equation 6 represent the value of the stress components at the ground surface, with those in Equation 7 representing the rate of increase of these with respect to depth. We assume σz = τyz = τzx = 0 at the ground surface, with the result that β0[σz] = β0[τyz] = β0[τzx] = 0. The number of individually regressed variables and regression parameters for a complete 3D stress tensor are thus six and nine respectively.

[8]

Here, Ψ is the depth of the stress domain boundary and ε is the error in predictive estimates obtained from regression. The model presented in Equation 8 can be expanded to include more breakpoints by including the desired number of Ψ terms. In the Bayesian context (Brilleman et al. 2017, Gelman & Hill 2007), regression coefficients (β0, β1) and all the breakpoints (Ψi) follow their own distributions rather taking fixed point estimates of centrality or some other statistical characteristic position. 3.3

Our analyses

To test the suitability and efficacy of the Bayesian linear segmented regression method, we have first applied the method to synthetically generated stress data that contains a distinct stress domain boundary at a depth of 100 m. The synthetically generated stress data are shown in Figure 2. These data were generated using tensorial method (Gao & Harrison 2017) by specifying a covariance matrix with very little dispersion and drawing random samples from the MVN distribution of Equation 1. The gradient discontinuity shown in these data could be explained by several plausible geological processes due to locked-in or residual stresses (Zang & Stephansson 2010) arising from removal of ice loading after glacial retreat. The figure shows only σx, σy and τxy, because the components σz, τyz and τzx are considered to have uniform gradients across the

Figure 2. Posterior estimates of mean stresses and stress domain boundary from Bayesian linear segmented regression on synthetic stress data

depth of interest and thus no gradient discontinuities are anticipated. Bayesian linear segmented regression estimates the posterior distributions of the regression coefficients using the model presented in Equations 4–7. We assume that the regression coefficients and the depth of the stress domain boundary all follow normal distributions. We have used uninformative priors on the regression coefficients, and an informative prior on the stress domain boundary with a mean depth of 100 m and a standard deviation of 10 m. The priors are thus β0j ~ Normal(0, 100), β2j ~ Normal(0, 100),

β1j ~ Normal(0, 100), Ψ ~ Normal(100, 10).

[9]

Discussion on Figure 2 is presented in the following section. A further analysis was performed using 114 overcoring stress measurements obtained at the Forsmark site in Sweden. As noted earlier, the suggestion has been made that there are stress domain boundaries at depths of 150 m and 300 m (Martin 2007). The Bayesian linear segmented regression uses the uninformative priors on the regression coefficients provided in Equation 9 together with informative priors of Ψ1 ~ Normal(150, 20), and Ψ2 ~ Normal(300, 20).

[10]

on the two depth stress domain boundaries. 4

RESULTS AND DISCUSSION

For the analysis of synthetic stress data, the Bayesian posterior estimates of mean stresses for σx, σy and τxy are

Figure 3. Plot of relative frequency for uncertain domain boundary in synthetic stress data

shown in Figure 2 with the relative frequency plot for the depth stress domain boundary being given in Figure 3. Figure 2 shows the posterior mean and 95% Credible Interval (CI) of (a) the mean stresses σx, σy and τxy, and (b) the depth stress domain boundary. As these data were generated using distributions with small variance, the 95% CI for both the mean stresses and the depth stress domain boundary are relatively narrow. The posterior mean of the depth stress boundary is 100.2 m, which is almost equal to the specified value of 100.0 m. The 95% CI for the depth of this boundary is even smaller than the standard deviation used in priors (Equation 9), i.e. 70°) ainsi qu'à des angles de coupe négatifs, montrant que l'énergie spécifique intrinsèque augmente considérablement une fois que l'angle de coupe arrière dépasse 75°. 1

INTRODUCTION

Estimation of mechanical properties of brittle materials such as rock, shale, and iron ore are most often required in rock mechanic projects, in particular, in rock mechanics laboratory testing. The uni-axial compressive strength (q) and the internal friction angle (𝜑) are two key parameters needed to address a broad range of geo-mechanical problems such as in civil, geotechnical, mining, and petroleum engineering, with applications ranging from the design of underground structures in rocks to the selection of tools for mechanical excavation (Lakshminarayana et al., 2019, Rostamsowlat et al., 2022). Several methods have been developed over the years to estimate the uni-axial compression strength (𝑞). Amongst these approaches, the most important ones are UCS (Uni-axial Compressive Strength), PLT (Strength index determined by the loading procedure at the point), and the Brazilian test. Among the above-mentioned methods, the “UCS” test is one of the most trustworthy methods to obtain the compression strength of a specific specimen in which the cylindrical specimens are subjected to axial pressure under dry conditions and atmosphere

pressure. UCS test is a method for estimating the mechanical and strength properties of deformable solids such as rocks, shales, and iron ores, i.e. Young's modulus, Poisson's ratio, and compressive strength. However, the UCS test suffers from several drawbacks. One of the major drawbacks is that the UCS test is relatively expensive. In addition, the TC test requires highquality and intact rock specimens, with adequate shape and size, i.e., cylindrical shape with a length-to-diameter ratio of two, and mutually parallel end faces that must be orthogonal to the cylinder's axis. For instance, in the case of some samples such as shales and sedimentary rock specimens, it is difficult to core and extract the intact rock specimens from highly fractured and weak underground layers (Rostam Sowlat, 2017, Mariano et al., 2011). This paper presents a testing method commonly used to infer the strength of rock material by scratching the surface of a core sample with a cutting tool. One of the parameters inferred from the tests, the intrinsic specific energy (with the unit of MPa) is found very well correlated with a classical measure of specimen strength such as the uniaxial compressive strength.

The objective of this work was to implement and test the methodology on rock, shale, and iron ore samples to assess its ability to provide a measurement of strength for a wide range of samples. To the best of the author’s knowledge, there is no extensive study focused on the determination of the compressive strengths of shales and iron ores. 2

MECHANICS OF SCRATCH TEST

Rock cutting or rock scratching can be described as shaving by machining of a layer of rock from a free surface using a tool moving parallel to the free surface. A better understanding of rock cutting or fragmenting has been one of the main objectives of drilling research since the 1950s and has continued up to now in both theoretical and experimental areas (Rostamsowlat, 2018). Depending on the depth of cut, the cutting process can be carried out in two modes: ductile mode and brittle mode. In the ductile mode, the failure mechanism induced by the cutting tool can be described as a flow of failed rock ahead of the cutting face. The rock is intensively sheared ahead of the cutter and crushed at the tip. The cutting mode characterized by a de-cohesion of the constitutive grains and matrix is referred to as a ductile regime. The cutting depth within this mode is generally less than 1 mm under dry conditions and atmospheric pressure (Richard, 1999, Rostamsowlat et al., 2018, Zhou and Lin, 2013). In the brittle mode, also denoted as “chipping”, cracks are initiated at the tip of the tool and propagate upwards. Once the crack reaches the surface of the sample, the chip is formed and removed by the cutter. The average cutting force does not vary linearly with the depth of cut. The brittle mode is associated with fracture propagation and the force has to be related to the material fracture toughness 𝐾𝐼𝐶 . Numerous experiments performed in the ductile regime with rectangular sharp cutters show strong evidence of the proportionality with the magnitude of the cutting force and the depth of cut, d. The tangential and normal force components can be written as (Detournay and Defourny, 1992, Richard et al., 2012): 𝐹𝑐𝑠 = 𝜀𝜔𝑑

,

𝐹𝑐𝑛 = 𝜁𝜀𝜔𝑑

Figure 1. Forces acting on a sharp PDC cutter An essential asset of the scratch test is its high degree of repeatability quite uncommon in rock testing with dispersion for ‘homogeneous’ rock of about one percent (standard deviation to mean) against ten percent for uniaxial compressive strength. One of the most appealing features of the method is the ability to generate logs of strength along. 3 3.1

EXPERIMENTAL SETUP Scratch Apparatus

The rock scratch machine (Figure 2) was used to scratch the samples under constant depth of cut 𝑑 using a sharp cutter. The depth of cut typically varies between 0.1 to 2 mm. The cutter is moved at a constant velocity 𝒗 while the apparatus records separately the magnitude of the normal 𝐹𝑐𝑛 and tangential 𝐹𝑐𝑠 components of the cutting force acting on the cutting face with a precision of 1 Newton (N). A windows-based software operating on a personal computer is used to control the Wombat, run the test, and save the data.

[1]

where 𝜔 is the width of cutting tool, 𝑑 is the cutting depth, 𝜀 is the intrinsic specific energy (minimum energy to remove a unit volume of sample, with the unit of MPa), and 𝜁 is the inclination of the cutting force. As shown in Figure 1, 𝜃 is the back rake angle, 𝒗 is the velocity vector, and 𝐴𝑐 is the cross-section area (𝐴𝑐 = 𝜔 × 𝑑 for rectangularshaped cutting tool), see Figure 1.

Figure 2. Schematics of the scratch machine

A ball screw via a stepper motor gearbox configuration drives the horizontal traveling block which supports a frame hosting a vertical slide on which a load sensor is mounted. A rotating wheel is used to travel the vertical slide and the sensor, up and down to precisely adjust d. A digital micrometer displays a readout of the position of the traveling mechanism. Locking screws are used to lock the slide in position once depth of the cut is set. The magnitude of the forces associated with depths of cuts can be large (several KNs), and small increments of the depth of cut (about 0.05 mm) can lead to a significant increase of the force magnitude (several tens of a Newton). The most challenging issue in the design of the traveling block is to maximize its vertical stiffness in order to minimize the deformation under loading and therefore the error between the nominal (adjusted by the user before the test) and the effective depth of cut (during the test, i.e. under loading). In the present design, the compliance of the system has been lowered to 0.015 mm/KN. This compliance has been found sufficient to neglect the error of the depth of cut in the case of medium-strength samples (𝑞 < 120MPa) where 𝑞 is the uniaxial strength of the specimen being tested. A visual inspection of the sample helps select the most appropriate location to conduct the tests to scratch the samples across their two distinct regions. Once the sample is securely fixed on the traverse, one or a few preliminary cuts are performed to set an initial flat groove. Scratch was performed subsequently in the same groove by an increment of 0.1 mm starting at 0.1 mm up to 0.9 mm. 3.2

Compression Machine

Figure 4. Plot of differential stress versus axial, radial and volumetric strains for Bentheimer sandstone. 3.3

A compression test machine manufactured by Humboldt was used to measure the uni-axial compressive strength (𝑞) of the samples used in this research. this machine is a displacement-controlled machine comprised of the main mechanical parts: a load frame, a load cell, two strain gauges for radial strain (𝜖𝑟 ), two compression platens, a control system, a gearbox and two LVDTs (Linear variable displacement transducers) for axial strain (𝜖𝑎 ). The samples were cut into cylindrical shapes with length over diameter ratio of approximately 2.2. The samples were set up in the compression test machine with transducers in place to measure sample axial and radial deformations and axial load. Each core plug was tested unsaturated. Each sample was axially loaded under a constant average axial strain rate of 0.5% giving a loading rate of 0.259 mm/min until the samples failed. The volumetric strain (𝜖𝑉 was obtained using: 𝜖𝑉 = 𝜖𝑎 + 2𝜖𝑟

Figure 3. Plot of differential stress versus axial, radial, and volumetric strains for Mountain Gold sandstone.

[2]

Results of uni-axial compressive strength (UCS) tests of two samples are depicted in Figures 3 & 4. The other available UCS plots for the rest of the samples are not shown here for briefness.

Cutter

The research is concerned with PDC (Polycrystalline Diamond Compact) sharp cutters which are made of a thin layer of polycrystalline diamond lay down on a carbide tungsten base. In practical terms “sharp” refers to cutters with a defect at the edge less than 10 mm in length (in the direction of cutting), see Figures 5 and 6. I used a highresolution optical microscope (model AxioScope Imager A1) with the ZEN software to monitor the edge of the cutter.

Figure 5. PDC single cutting tool

4.1

Interpretation of data

All the cutting tests in this study were carried out in the ductile regime. Figure 8 shows an example of the force signals as a function of cutting tool displacement (position). In this example, a region characterized by a “steady mean value” (computed on about 1 cm window) is selected while the section characterized by larger dispersion due to heterogeneities in the rock sample is disregarded.

Figure 6. Drawings of a single cutting tool 3.4

Figure 8. Variation of force signals during one scratch test along the test length.

Core Samples

Five quarry rock samples (two sandstones and three limestones), 5 shales, and 2 iron cores were selected for this study. Based on visual inspection, these specimens are reasonably homogeneous at the scale of the retrieved blocks. For each specimen, several cylindrical specimens (80 mm in diameter and 120 mm in length) were cored from the same block for scratch testing. In order to furtherminimize the influence of heterogeneity, the scratch test was repeated three times on three different sides of each cylindrical specimen, see Figure 7. Additional cylindrical plugs (38 mm in diameter and approximately 80 mm in length) were also extracted from the same blocks for further mechanical characterization under uni-axial compression loading in the laboratory. These UCS tests aim to determine the samples’ strengths.

Once the section of interest has been selected, a single mean value for both force components including normal force (𝐹𝑐𝑛 ) and tangential force (𝐹𝑐𝑠 ) is calculated. These mean values of normal and tangential forces are calculated by taking the mean value of 𝐹𝑐𝑛 and 𝐹𝑐𝑠 for each test. The intrinsic specific energy (𝜀) is calculated from the data recorded during the cutting tests conducted with a sharp cutter. The value of intrinsic specific energy is estimated from the slope of the best linear fit conducted on the pairs 𝐹𝑐𝑠 − 𝑑 scaled by the width of the cutter (𝜔), see Figure 9.

Figure 9. Force versus depth of cut using sharp cutter for Tuffeau limestone 4.2 Figure 7. A photo of one sample prepared for testing

4

RESULTS AND DISCUSSION

Experimental Results

Results of the tests carried out with the PDC sharp cutter are shown in Figure 10 and confirms results obtained by other researchers (Richard et al., 2012, Rostamsowlat et al., 2018), but extend the results over a larger range of back rake angle. Evolution of the intrinsic specific energy scaled 𝜀 by uni-axial compressive strength ( ) shown in Figure 10 𝑞

clearly shows that the intrinsic specific energy (𝜀) obtained

from the scratch test is very close to the uni-axial strength of the samples (𝑞) obtained from UCS test under certain condition; when the back rake angle of the cutting tool in the scratch test ranges from 5° to 20°, see Figure 11.

preparation; the sample is only semi-destructive so the core remains relatively intact and can be used for other tests (such as permeability or porosity); and the test is reproducible. Finally, a very interesting outcome of the scratch test is its ability to create a log of specific energy along the core sample, which can be interpreted as a log of strength. This study showed that drilling engineers can also estimate the compressive strengths of brittle samples using real-time drilling data. References

Figure 10. Scaled intrinsic specific energy versus rake angle

Figure 11. Correlation between the uni-axial compressive strength and the intrinsic specific energy. The quite uncommon level of repeatability observed in the test and the ability to produce logs with a fine spatial resolution make the method an attractive alternative to measure the strength of iron material but also screen homogenous sections and isolate regions characterized by distinct or fast-varying properties. 5

CONCLUSION

A series of experiments were conducted with a sharp cutter (with a width of 10 mm) at various back rake angles (𝜃) varying from -10° to 85°, in the ductile regime of failure. The current results confirm previous results: (𝑖) the intrinsic specific energy (𝜀) increases steadily with the back rake angle, (𝑖𝑖) 𝜀 is found to be very well correlated with the rock uni-axial compressive strength (𝑞) for back rake angles between 5° and 20°. The scratch test offers several advantages over the conventional UCS strength test: it requires minimal sample

DETOURNAY, E. & DEFOURNY, P. A phenomenological model for the drilling action of drag bits. International journal of rock mechanics and mining sciences & geomechanics abstracts, 1992. Elsevier, 13-23. LAKSHMINARAYANA, C., TRIPATHI, A. K. & PAL, S. K. 2019. Estimation of rock strength properties using selected mechanical parameters obtained during the rotary drilling. Journal of The Institution of Engineers (India): Series D, 100, 177-186. MARIANO, L., RICHARD, T. & RAMANAIDOU, E. The scratch test–an attractive method to measure the strength of iron ore material. Iron Ore Conference, 2011. 11-3. RICHARD, T. 1999. Determination of rock strength from cutting tests. University of Minnesota. RICHARD, T., DAGRAIN, F., POYOL, E. & DETOURNAY, E. 2012. Rock strength determination from scratch tests. Engineering Geology, 147, 91-100. ROSTAM SOWLAT, I. 2017. Effect of cutter and rock properties on the frictional contact in rock cutting with blunt tools. Curtin University. ROSTAMSOWLAT, I. 2018. Effect of cutting tool properties and depth of cut in rock cutting: an experimental study. Rock Mechanics and Rock Engineering, 51, 1715-1728. ROSTAMSOWLAT, I., EVANS, B., SAROUT, J., ROSTAMI, J. & KWON, H.-J. 2022. Determination of Internal Friction Angle of Rocks Using Scratch Test with a Blunt PDC Cutter. Rock Mechanics and Rock Engineering, 55, 7859-7880. ROSTAMSOWLAT, I., RICHARD, T. & EVANS, B. 2018. An experimental study of the effect of back rake angle in rock cutting. International Journal of Rock Mechanics and Mining Sciences, 107, 224-232. ZHOU, Y. & LIN, J.-S. 2013. On the critical failure mode transition depth for rock cutting. International Journal of Rock Mechanics and Mining Sciences, 62, 131-137.

Thermal-Hydro-Mechanical coupled numerical analysis of a faulted zone in a Quebec’s potential geothermal engineering site Saeed Vadiee a, Biao Li a, Jasmin Raymond b, Mafalda M Mirandab a. Department of Building, Civil & Environmental Engineering, Concordia University, Montreal, Quebec, Canada, b. Institut national de la recherche scientifique, 490 de la Couronne, Québec, QC, Canada ABSTRACT Previous studies showed that deep geothermal energy can be a promising solution to support Canada’s energy transition, which is particularly valuable for remote northern communities. Due to the production and injection of fluids, geothermal reservoirs are susceptible to experiencing significant variations in both pressure and temperature. These changes in operating conditions have the potential to affect the fault stability in an engineered geothermal reservoir located in a fault zone. In this study, we carry out finite element numerical analysis on thermo-hydromechanical (THM) coupled processes to anticipate fluid production or injection operations in a potential deep geothermal energy project in Kuujjuaq, Canada. The system simulates a doublet operation within a faulted zone with hydraulically stimulated injection and production wells at a targeted depth of 3950 meters. Regional in-situ stresses were obtained based on previous investigations. Laboratory tests were conducted to obtain the mechanical properties of rocks retrieved from the studying site. The slip tendency along the fault under a constant injectionproduction scenario is quantified based on the numerical results. RÉSUMÉ Des études antérieures ont démontré que l'énergie géothermique profonde peut être une solution prometteuse pour soutenir la transition énergétique au Canada, ce qui serait particulièrement utile pour les communautés nordiques éloignées. À cause de la production et de l'injection de fluides, les réservoirs géothermiques sont susceptibles de subir des variations importantes de pression et de température. Ces changement de conditions d’opération ont le potentiel d'affecter la stabilité de la faille dans un réservoir géothermique ouvragé situé dans une zone de faille. Dans cette étude, nous avons effectuons une analyse numérique par éléments finis sur des processus couplés thermo-hydro-mécaniques (THM) pour anticiper les opérations de production ou d'injection de fluides dans un projet potentiel d'énergie géothermique profonde à Kuujjuaq, au Canada. Le système simule l’opération d’un doublet dans une zone faillée avec des puits d'injection et de production stimulés hydrauliquement

à une profondeur ciblée de 3950 mètres. Les contraintes régionales in situ ont été obtenues sur la base d'études antérieures. Des essais en laboratoire ont été menés pour obtenir les propriétés mécaniques des roches extraites du site d'étude. La tendance au glissement le long de la faille dans un scénario d'injection-production constante est quantifiée sur la base des résultats numériques. 1.

Introduction

Canada's harsh environment and resource-intensive industries, including mining, forestry, oil and gas extraction, transportation, and electricity generation for the Northern part of Canada, contribute to its high energy consumption. As a result, the average Canadian consumes 5.1 times more energy than the average world citizen and 23 percent more than the average American citizen. Canada's primary energy source is fossil fuels, resulting in three times the global average for greenhouse gas emissions. (David Hughes, 2018) One promising technology for sustainable, carbon-free sources of energy is Enhanced Geothermal Systems (EGS). EGS utilizes the Earth's natural heat to generate electricity and/or produce heat, emitting no greenhouse gases, and providing a reliable and consistent source of renewable energy unaffected by weather or seasonal fluctuations (Aliyu & Archer, 2021; Houhou & Laloui, 2022). Using EGS technology, a geothermal reservoir is created by injecting water into hot, dry rock (HDR) formations and creating fractures that enable the water to flow and absorb heat. Later, geothermal energy is obtained from a production well that is located in hot dry rock which has very low permeability. To introduce and extract geothermal energy from HDR, fracturing technology is used to create new fractures in the rock. This process transforms the HDR into a fractured porous medium, allowing the geothermal fluids to flow through and be extracted.

However, as beneficial as geothermal systems are, a wide range of fundamental questions and uncertainties about physical processes must be addressed to develop an efficient and functional energy harvesting system. Indeed, understanding the behavior of multicomponent (fractured) porous rock-fluid systems and their Multiphysics dynamics is essential for making accurate predictions about how subsurface reservoirs will behave (Parisio et al., 2019). In other words, modeling the performance of the HDR extraction system can be challenging as it requires a proper conceptual model to account for the characteristics of the fractures, faults, and the matrix in the reservoir (Wang et al., 2023). Additionally, it is worth mentioning that, the injection or pumping of fluids at depth, which is a common practice in various geo-energy operations, can induce alterations in the in-situ stress field, potentially leading to fault rupture and induced seismicity. This is due to the pore pressure increase induced by fluid injection, which can reduce the effective stress on pre-existing faults, making them more prone to failure. The occurrence of induced seismicity depends on various factors, such as the injection rate and volume of fluids, the depth and orientation of the injection well, the geological properties of the surrounding rock, and the stress state of the subsurface. To mitigate the risk of induced seismicity, a comprehensive monitoring and management plan should be implemented, which includes continuous monitoring of seismic activity in the subsurface and the adjustment of injection rates to prevent the triggering of earthquakes. (Buijze et al., 2017; Ellsworth, 2013; Wu et al., 2021) To tackle the complexity and nonlinearity of geotechnical behavior, Cacace & Jacquey (2017) created an opensource FEM package that uses the Galerkin finite-element technique to discretize partial differential equations (PDEs). GOLEM investigates groundwater flow, heat, and solute mass transport in fully saturated fractured rocks with elastoplastic mechanical feedbacks. GOLEM was built on the MOOSE (Multiphysics Object Oriented Simulation Environment) framework, open-source software that enables the solution of coupled, non-linear, partial differential system equations (Slaughter et al., 2014). MOOSE can solve the PDEs implicitly and fully coupled using the Newton-Raphson scheme. In this context, the present study’s focus is to investigate the viability and sustainability of a targeted geothermal energy reservoir in Northern Quebec by examining the thermal, hydraulic, and mechanical coupled processes occurring in the targeted reservoir. A more efficient and effective EGS system can be developed by better understanding these processes, which aligns with Canada’s net-zero emission goal by reducing the dependence on fossil fuels thereby mitigating the negative effects of climate change.

2.

Governing Equations

This section explains a developed constitutive model for a porous medium consisting of a deformable hosting rock and a mobile liquid that is saturating its pore. Firstly, the main governing equations are derived from the mass, energy, and momentum balance equations, then the mathematical model is clarified. The governing equations are based on the work by Cacace & Jacquey (2017). The derived PDEs presented below are denoted by subscripts "f" and "s," referring to the fluid and solid phases, respectively. By describing the equation of continuity in terms of the volumetric average for the fluid phase, the following equation can be derived. 𝜕(𝑛𝜌𝑓 ) 𝜕𝑡

= ∇ ⋅ (𝑛𝜌𝑓 𝑣𝑓 ) = 𝑄𝑓

[1]

where 𝜌𝑓 is the density of the fluid phase, 𝑛 is the porosity, 𝑣𝑓 the fluid velocity, and 𝑄𝑓 is a sink/source term. Additionally, Darcy's law, which can be defined in terms of the fluid's velocity relative to the solid's velocity, may be used to illustrate the conservation of momentum of the fluid phase:

𝑞𝐷 = 𝑛(𝑣𝑓 − 𝑣𝑠 ) = −

𝜅 𝜇𝑓

⋅ (∇𝜌𝑓 − 𝜌𝑓 𝑔)

[2]

where 𝑞𝐷 is the volumetric flow rate per unit of surface area (Darcy velocity), 𝜅 is the permeability tensor of the porous medium, 𝜇𝑓 the fluid viscosity and 𝑔 is the gravity vector. By substituting equations [1] and [2] and applying the Lagrangian derivative with regard to a moving solid, i.e., 𝐷𝑠 (∗) 𝜕(∗) [ ≡ + ∇(∗) ⋅ 𝑣𝑠 ] , the following relation will be 𝐷𝑡 𝜕𝑡 obtained: 𝑛 𝐷𝑓 𝜌𝑓 𝐷𝑡

+

𝐷𝑠 𝑛 𝐷𝑡

+ 𝑛∇ ⋅ 𝑣𝑠 + ∇ ⋅ qD = 0

[3]

In a similar way, from the mass balance equation, the following relation is yielded for the solid phase: 𝜕((1−𝑛)𝜌𝑆 ) 𝜕𝑡

+ ∇ ⋅ ((1 − 𝑛)𝜌𝑠 𝑣𝑠 ) = 𝑄𝑠

[4]

where 𝜌𝑠 is the density of the solid phase, 𝑣𝑠 is the solid velocity, and 𝑄𝑠 is a sink/source term. The following relation is obtained by assuming 𝑄𝑠 to be zero and considering both the solid skeleton and the pore fluid incompressible. 1−𝑛 𝐷𝑠 𝜌𝑠 𝜌𝑠

𝐷𝑡

−

𝐷𝑠 𝑛 𝐷𝑡

+ (1 − 𝑛)∇ ⋅ 𝑣𝑠 = 0

[5]

From equation [5], it can be inferred that when the fluid is injected into or ejected from the pore space, the rock material deforms (contract or dilate).

Reworking equation [5] gives us the evolution of porosity in terms of the Lagrangian derivative with respect to the solid deformation velocity: 𝐷𝑠 𝑛 𝐷𝑡

=

(1−𝑛) 𝐷𝑠 𝜌𝑠 𝜌𝑠

𝐷𝑡

[6]

+ (1 − 𝑛)∇ ⋅ 𝑣𝑠

By substituting equation [3] with equation [6], the following relation is used to determine the problem's variable, i.e., pore pressure, temperature, and the solid skeleton displacements: 𝑛 𝐷𝑓 𝜌𝑓 𝜌𝑓 𝐷𝑡

+

(1−𝑛) 𝐷𝑠 𝜌𝑠 𝜌𝑠

𝐷𝑡

[7]

+ ∇ ⋅ 𝑣𝑠 + ∇ ⋅ 𝑞𝐷 = 0.

With the use of thermodynamic differentiation, the first term in equation (13)'s left-hand side can be expressed as the function of fluid pore pressure and temperature as follows: 𝐷𝑓 𝜌𝑓 𝐷𝑡

= 𝑛(

1 𝐷𝑓 𝜌𝑓

𝐾𝑓 𝐷𝑡

− 𝛽𝑓

𝐷𝑓 𝑇 𝐷𝑡

[8]

),

where 𝐾𝑓 is the fluid bulk modulus, and 𝛽𝑓 is the fluid volumetric thermal expansion coefficient. 1 𝐾𝑓

=

1 𝜌𝑓

𝛽𝑓 = −

(

∂(𝜌𝑓 ) ∂𝑃𝑓

1 𝜌𝑓

(

)

[9]

𝑇

𝜕𝜌𝑓 𝜕𝑇

[10]

)

𝜌𝑓

The stress-strain constitutive relation has been driven by (1−𝑛) 𝐷𝑠 𝜌𝑠 taking the second term in equation [7] and Biot’s 𝜌𝑠

𝐷𝑡

theory, which explains the relationship between stress and 𝑒 elastic strain 𝜎̇ ′ = 𝜎̇𝑖𝑗′ = ℂ𝑖𝑗𝑘𝑙 𝜀̇𝑘𝑙 = ℂ: 𝜀̇ 𝑒 . (1−𝑛) 𝐷𝑠 𝜌𝑠 𝜌𝑠

𝐷𝑡

=

(𝛼−𝑛) 𝐷𝑠 𝑝𝑓 𝐾𝑠

𝐷𝑡

− (1 − 𝑛)𝛽𝑠

𝐷𝑠 𝑇 𝐷𝑡

−

1 𝐷𝑠 𝜎 ̅′ 𝐾𝑠 𝐷𝑡

[11]

where ℂ = ℂ𝑖𝑗𝑘𝑙 = 𝜆𝛿𝑖𝑗 𝛿𝑘𝑙 + 2𝐺𝛿𝑖𝑘 𝛿𝑗𝑙 is a rank-four elastic stiffness tensor with λ and 𝐺 being the first (volumetric) and second (shear) Lamé moduli, respectively, 𝜎̅ ′ represent mean effective stress, 𝛽𝑠 indicating volumetric thermal expansion for the solid grain. In equation [11], 𝛼 is called Biot’s coefficient, which describes the mechanical behavior of a porous elastic solid (Biot 1941) and can be defined below.

𝛼 =1−

𝐾𝑑 𝐾𝑠

[12]

where 𝐾𝑑 is called the drained bulk modulus and 𝐾𝑠 represents the solid grain’s bulk modulus. 𝐾𝑑 is the key parameter in understanding mechanical behavior, which can be measured via laboratory testing. Up to now, constitutive rules for fluid flow and deformation have been derived. The equation for heat transfer within the medium is the final one left. The PDE, explaining heat transfer, is derived from the heat balance conservation equation, which assumes that heat exchange between a

solid and a fluid occurs in a thermally balanced environment.

𝑇

𝜕(𝜌𝑐)𝑏 𝜕𝑡

+ (𝜌𝑐)𝑏

𝜕𝑇 𝜕𝑡

+ ∇ ⋅ (𝜌𝑓 𝑐𝑓 𝑞𝐷 𝑇 − 𝜆𝑏 ∇𝑇) − 𝐻̇ = 0

(𝜌𝑐)𝑏 = 𝑛𝜌𝑓 𝑐𝑓 + (1 − 𝑛)𝜌𝑠 𝑐𝑠 𝜆𝑏 = 𝑛𝜆𝑓 + (1 − 𝑛)𝜆𝑠

[13] [14] [15]

where (𝜌𝑐)𝑏 is the bulk-specific heat of the porous medium, 𝜆𝑏 represents bulk thermal conductivity, and 𝐻 ̇ demonstrates the energy production rate. 3.

Numerical model setup

The geological setting, as illustrated in Figure 1a, is a 4 km by 4 km by 1 km volume located 3,950 meters below the surface. It consists of a hosting rock that is intersected by a natural fault, as well as two horizontally oriented highpermeable hydraulically stimulated fractures of the same size (100-meter cube). The orientation of the fractures was chosen to align with the direction of the least principal stress, as fractures tend to propagate along paths of the least energy configuration. The fractures are spaced 400 meters apart. Given that the orientation of the fault at the desired depth was unknown, it was assumed to have a dip azimuth of 𝑁45°𝐸 based on the literature provided by (Miranda et al. 2021). The fault is believed to cross the middle of the surrounding rock, dividing the domain into two distinct regions. In the context of mechanical boundary conditions, we have assumed a fixed constraint for all faces in this study. This approach is adopted to investigate the coupling effect that occurs subsequent to the injection of geothermal fluid. Our analysis begins with the assumption that the rock is in a state of equilibrium, with an initial displacement of 0, at the onset of the simulation for the entire domain. This enables us to focus on the effects of geothermal fluid injection on the rock's behavior and mechanical response (Wang et al., 2023). The hydraulic boundary conditions applied to the model encompass subjecting all lateral boundaries of the domain to hydrostatic pressure with a zero-velocity boundary condition. The top and bottom face is set to a fixed pressure of 38 MPa and 50 MPa respectively. Recent exploration efforts have shed light on the characteristics of the rock and fault permeability within the region. Specifically, the permeability of the rock has been determined to be 10−15 𝑚2 based on these investigations. Furthermore, an exploration well conducted at the presumed location of the geothermal reservoir has revealed that the fault's permeability is approximately 10−12 𝑚2 . Previous investigations have indicated that the fault represents a substantial ductile deformation zone, exhibiting distinct behavior in comparison to conventional brittle fractures. It is worth mentioning that the presence of small-scale fractures intersecting this deformation zone

may contribute to the overall permeability. However, to accurately capture the hydraulic behavior of this ductile deformation zone, further research in the field of structural geology is warranted. For the thermal boundary conditions, the top and bottom face of the domain is held at a fixed temperature of 72°𝐶 𝑎𝑛𝑑 93°𝐶 , which corresponds to a geothermal gradient of 20.9°𝐶 per kilometer. Furthermore, at the lateral boundary of the model, a prescribed heat flux value of 57.2 𝑚𝑊𝑚−2 is set as the thermal boundary condition.

Additionally, the geological setting under investigation is believed to be controlled by a strike-slip regime, based on available literature and preliminary analysis (Miranda et al., 2023). The stress magnitudes used in this numerical model are as follows: -

Vertical stress (𝜎𝑣 ) = 27 𝑀𝑃𝑎 𝑘𝑚−1 Maximum Horizontal Stress (𝜎𝐻𝑚𝑎𝑥 ) = 45.3 𝑀𝑝𝑎 𝑘𝑚−1 Minimum Horizontal Stress (𝜎ℎ𝑚𝑖𝑛 ) = 26.5 𝑀𝑃𝑎 𝑘𝑚−1

Figure 1. (a) Geological model for the numerical simulation; (b) Applied FEM mesh for the simulation. For the geothermal operation activity, 100 years of geothermal activity has been simulated in which our bestcase scenario consists of injecting fluid with an injection temperature of 60 °𝑐 at a constant rate of 15 𝐿𝑠 −1 . Also, the production of geothermal fluid was set to be the same rate as the injection of fluid. The selection of appropriate element types and the meshing scheme is a crucial step in creating an accurate and reliable Finite Element Method (FEM) simulation. In this regard, as can be seen in Figure 1b, tetrahedral elements with Delaunay's Tet-meshing scheme have been selected for this study. This choice is based on the advantages of this meshing scheme, which reduces the geometry simplifications required for FEM simulations. Other meshing schemes are often not capable of representing discrete fractures and faults, which are commonly observed in dynamic model realizations. As such, the tetrahedral meshing scheme provides a more accurate representation of this complex system. In this geometric model, fractures and faults are regarded as embedded lower-dimensional components within higher-dimensional elements, which is the porous matrix filled with mobile fluid (Cacace & Blöcher, 2015). This makes it possible to describe flow, mass, and energy transport processes in natural reservoirs.

The mesh was generated, and the quality of the mesh was assessed using Coreform Cubit. This mesh quality check was crucial to ensure that the resulting mesh could reliably and accurately represent the real-world geological setting in our numerical simulations. We aimed to minimize any errors or inaccuracies that may arise during the simulation process by subjecting the mesh to quality checks. This approach allowed us to achieve a high degree of fidelity in our model, which is essential for generating reliable and robust results. Table 1. Rock matrix material properties Property name

Symbol

Value

Unit

Porosity

𝑛

4%

−

Permeability

𝐾𝑥=𝑦=𝑧

Fluid modulus

𝑘𝑓

−15

10

1.774

𝑚2 𝐺𝑃𝑎

Fluid viscosity

𝜇𝑓

3 ∗ 10

Fluid density

𝜌𝑓

Fluid heat capacity Fluid thermal conductivity Rock density

𝑐𝑓

1080 4180

𝐽. 𝑘𝑔−1 . 𝐾 −1

𝜆𝑓

0.65

𝑊. 𝑚−1 . 𝐾 −1

𝜌𝑠

2672

𝑘𝑔. 𝑚−3

−4

𝑃𝑎. 𝑆 𝑘𝑔. 𝑚−3

Rock heat capacity Rock thermal conductivity Young modulus

𝑐𝑠

898

𝐽. 𝑘𝑔−1 . 𝐾 −1

𝜆𝑠

2.6

𝑊. 𝑚−1 . 𝐾 −1

𝐸

67

𝐺𝑃𝑎

Poisson’s ratio

𝑣

−

Rock bulk modulus

𝑘𝑠

0.25 44.67

𝐺𝑃𝑎

𝐺

26.8

𝐺𝑃𝑎

Rock shear modulus

Table 2. Fault's material properties Property name

Symbol

Value

Unit

Porosity

𝑛

1

−

Permeability

𝐾𝑥=𝑦=𝑧

−12

10

𝑚2

Fluid modulus

𝑘𝑓

2.5

𝐺𝑃𝑎

Fluid viscosity

𝜇𝑓

3 ∗ 10−4

𝑃𝑎. 𝑆

Thermal conductivity

𝜆𝑓

0.65

𝑊. 𝑚−1 . 𝐾 −1

Table 3. Fracture’s material properties Property name

Value

Unit

Porosity

𝑛

1

−

Permeability

𝐾𝑥=𝑦=𝑧

10−9

𝑚2

Fluid modulus

𝑘𝑓

2.5

𝐺𝑃𝑎

Fluid viscosity

𝜇𝑓

3 ∗ 10

𝜆𝑓

0.65

Thermal conductivity

4.

Symbol

−4

𝑃𝑎. 𝑆 𝑊. 𝑚−1 . 𝐾 −1

Results

The investigation into the thermal, hydraulic, and mechanical processes in a targeted geothermal energy reservoir in Northern Quebec using the GOLEM opensource FEM package resulted in several significant findings, including temperature evolution and heat transfer,

production temperature evolution, pore pressure evolution and flow field, thermal and hydraulic effects on mechanical deformation, and slip tendency analysis. 4.1

Temperature evolution and heat transfer

Figure 2 (a) and 2 (b) illustrates the distribution of temperature over time in the XZ plane. As cold water is injected into the system, it flows through the porous matrix, fault, and fractures to the production well, resulting in a decrease in temperature around the injection well. Over time, the central part of the reservoir experiences a significant drop in temperature (from 82 °𝐶 at the beginning of the simulation to 61 °𝐶 at the end of the simulation – these values have been monitored by controlling points around the injection well), while the lateral boundaries of the rock remain relatively hot. The temperature gradient between the boundaries and the surrounding rock leads to heat energy transfer through conduction, a critical process in geothermal energy extraction. Based on the results shown in Figure 2 (c), the direction of heat convection and 3D temperature slice after production can be observed. The arrow lines in this figure indicate the directions of the convective heat flux, which is the heat flux carried by water. The direction of convective heat flux is the same as the direction of water flow. Along the convection path from the injection well to the production well, the water temperature gradually increases due to the heating of the solids and hot water in the host rock during transport. The 3D view shows that the domain occupied by cold water forms an ellipsoid, with the expansion of this ellipsoid, the temperature change became lower from the injection well to the production well. These findings are significant for understanding the thermal behavior of the geothermal energy reservoir and can be used to optimize energy production.

Figure 2. Temperature and heat transfer evolution at the XZ plane located in the middle of the domain (a) at the beginning of the simulation; (b) at the end of the simulation; (c) Temperature distribution on XZ plane after 100 years of operation 4.2

Pore pressure distribution and flow field in EGS

Figure 3 provides a visual representation of the 2D flow field of pore water pressure during the production period. It shows the pressure gradients and water flow direction in the geothermal reservoir. The colors of the surfaces and lines in the figure indicate different pressure values and flow speeds. The two panels, (a) and (b), respectively, illustrate the temperature gradients at the beginning of the simulation and 100 years of operation activity. During the initial operation phase, the pressure levels experienced rapid fluctuations, with the pressure around

the injection well rising and that around the production well falling. It is important to note that the pressure change was observed to be quicker in the fracture than in the pore space due to the higher fracture permeability. The flow map in Figure 3, panel (c) further illustrates the flow of cold water from the injection well to the production well, where it is heated by the surrounding hot water and rock. This process results in the creation of a thermal plume around the injection well.

Figure 3. Pore pressure variation during operational activity. Panel (a) and (b) shows the distribution of temperature at the beginning and 100 years of the simulation in the XZ plane, respectively and Panel (c) illustrate the 2D pore pressure variation, together with the direction of fluid velocity after 100 years of operation. 4.3

Fault's Slip Tendency Impending Movements

Potential:

Assessing

Reservoir deformation is a complex phenomenon that occurs throughout the operational lifespan of a reservoir, triggered by variations in pressure and temperature. Accurate modeling and analysis of these factors are crucial in understanding the reservoir's behavior and optimizing its performance. The continuous injection of thermal fluid induces changes in pressure and temperature, causing the reservoir to undergo deformation over time. This deformation may result in stress redistribution within the subsurface, affecting fault stability and the potential for fault movement (Heidari et al. 2021). This injection-induced thermal stress leads to a peak displacement around the injection well, aligning with the observed high-temperature variations in its vicinity. Overtime, the low-temperature zone expands toward the production well, as indicated in Figure 4. In light of reservoir deformation and its potential implications, slip tendency analysis has been conducted on the strike-slip fault with the assumed dip angle of 45 °. The analysis aims to assess the interplay between slip tendency, shear stress, normal stress, and the influence of thermal fluid production and introduction. Based on the analysis presented in Figure 4, the slipping tendency of the fault exhibited a discernible decline from an initial value of 0.31 at the onset of operational activity to 0.29 following a protracted century-long period. This observed reduction in

slip tendency suggests a diminishing propensity for fault slippage over time. It is essential to note that a comprehensive understanding of the specific fault and the contextual factors surrounding slip tendency values is imperative for drawing definitive conclusions. Slip tendency serves as a quantitative measure of the fault's susceptibility to movement, and a decrease in this parameter may imply a concomitant decrease in the likelihood or frequency of fault activity.

To elucidate the implications of this diminished slip tendency, further investigation is warranted, taking into consideration additional pertinent factors such as the fault's precise geographic location, prevailing geological conditions, and accompanying seismic or geological data. Only through a holistic analysis of these elements can a more profound comprehension of the fault's behavioral patterns and the broader implications thereof be ascertained.

Figure 4. Temporal evolution of pore pressure, temperature, and displacement in the X direction during the production period

Figure 5. Slip Potential. (a) Before the operation activity, (b) Beginning of the operation (c) 100 years of operation

5.

Conclusions

The present study aims to explore the viability and sustainability of a targeted geothermal energy reservoir in Northern Quebec by investigating the thermo-hydromechanical processes using constitutive and numerical models. A numerical simulation was performed to investigate the potential application of the presented model on the EGS. The simulation provides results of the thermal, hydro, and mechanical behavior during the production of EGS. The analysis reveals that the coupling effects of thermo-hydro-mechanical fields in fractured porous media determine the performance of EGS. Overall, the use of Enhanced Geothermal Energy Systems (EGS) can provide a reliable and consistent source of renewable energy, which is not affected by weather or seasonal fluctuations and emits no greenhouse gases. Additionally, the comprehensive analysis of slip tendency and the influence of thermal fluid production has enhanced our understanding of fault stability and reservoir behavior. The analysis suggests a decline in slip tendency over time; however, comprehensive scrutiny encompassing various geological parameters and contextual factors is necessary to establish a comprehensive understanding of the fault's behavior and its significance within the broader geological framework. 6. Acknowledgement Financial support by Concordia University Seed start-up grant (NO. 300010373) is acknowledged. 7.

References

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Mineralogical Characteristics and Mechanical Properties of Montney Formation Using Instrumented Indentation Test and SEM/EDS Analysis Huan Yu, Wenbo Zheng, Jianhui Zhou School of Engineering – University of Northern British Columbia, Prince George, British Columbia, Canada ABSTRACT Rocks are heterogeneous and anisotropic in nature, and the mineralogical characteristics and microstructures of the rock control the mechanical properties. The conventional methods to obtain the mechanical properties of the rock are the uniaxial test, triaxial test and Brazilian test et al. However, conventional tests have some shortcomings, which are expensive and time-consuming to make numerous specimens for repetitive tests. Meanwhile, specimens for these tests are inch-size, while the mineral grain size ranges from millimetres to micrometre size; the testing scale cannot match the study scale. This study investigates the impact of mineralogical characteristics on rock behaviour and properties in the Doig and Montney Formations. A series of instrumented indentation tests were performed on five rock samples from different depths, and the mineralogical characteristics and their change during the whole test were analyzed by scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) analysis. Pearson correlation was employed to examine the relationships between individual mineralogical characteristics and rock behaviour, such as three typical indentation depths, Young's modulus and hardness. The findings of this study provide valuable insights into the effects of mineralogical characteristics on rock behaviours and properties. RÉSUMÉ Les roches sont hétérogènes et anisotropes par nature, et les caractéristiques minéralogiques et les microstructures de la roche contrôlent les propriétés mécaniques. Les méthodes conventionnelles pour obtenir les propriétés mécaniques de la roche sont le test uniaxial, le test triaxial et le test brésilien, et autres. Cependant, les tests conventionnels présentent certains inconvénients, tels que le coût élevé et le temps nécessaire pour fabriquer de nombreux échantillons pour des tests répétitifs. De plus, les spécimens utilisés pour ces tests ont une taille en pouces, tandis que la taille des grains minéraux varie de millimètres à micromètres ; l'échelle des tests ne correspond pas à l'échelle de l'étude. Cette étude examine l'impact des caractéristiques minéralogiques sur le comportement et les propriétés des roches dans les formations de Doig et de Montney. Une série de tests d'indentation instrumentée a été réalisée sur cinq échantillons de roche provenant de différentes profondeurs, et les caractéristiques minéralogiques et leur évolution pendant tout le test ont été analysées par microscopie électronique à balayage (MEB) et spectroscopie à dispersion d'énergie (EDS). La corrélation de Pearson a été utilisée pour examiner les relations entre les caractéristiques minéralogiques individuelles et le comportement de la roche, telles que les trois profondeurs d'indentation typiques, le module de Young et la dureté. Les résultats de cette étude fournissent des informations précieuses sur les effets des caractéristiques minéralogiques sur le comportement et les propriétés des roches. 1

INTRODUCTION

The Montney unconventional gas play in northeast British Columbia, Canada, has become the province's largest gas producer, and two-thirds of wells drilling targeted the Montney formation (Board, 2013; Rivard et al., 2014; Vishkai et al., 2017). The production of unconventional gas benefits from hydraulic fracturing, where the fracture network created by hydraulic fracturing is highly related to the mechanical rock properties of targeted formations (Cheng et al., 2022; Golding et al., 2014; Katende et al., 2021; Sharma et al., 2019). Natural rocks are anisotropic and heterogeneous, and the effect of minerals on rock arises from differences in the crystal structure of various minerals and variations in the mechanical properties of minerals (Borodich et al., 2015; Rahimi et al., 2021; Zheng et al., 2019).

Instrument indentation tests have been successfully applied to study the mechanical properties of shale rock, such as Young's modulus, hardness, and creep behaviours of rock, especially after the saturation with various hydraulic fracturing fluids (Fan et al., 2019; Mueller & Amro, 2015; Sun et al., 2020; Yang et al., 2016). Compared with traditional compressional tests, indentation tests demonstrate advantages in terms of testing time and financial savings, e.g., the testing time only takes several minutes to reach a high-stress level as the loading area has a small size (Mighani et al., 2019). Rock samples used for indentation tests are relatively easy to prepare and can be reused after non-destructive indentation tests (Liu et al., 2020). Given the measured indentation response is highly related to the localized mineral composition of the indented area, the interpretation of the mechanical behaviours should be correlated to the micro-scale mineral characteristics (Kasyap et al., 2021; Song et al., 2022).

Development of imaging and analytical techniques (e.g. x-ray diffraction) makes it possible to quantify the mineral composition and microstructure of materials (Akkaş et al., 2015; Meyer et al., 2013). These techniques have been used together with indentation tests to understand the relationship between mineralogical characteristics and mechanical properties of rock. Liu et al. (2016) used the nano-indentation technique and XRD to investigate whether the hardness and elastic modulus of shale is closely related to the clay content of the rock and whether the orientation of the clay minerals influences the shale's mechanical properties. Ma et al. (2021) conducted the SEM and EDS analysis to obtain the elemental images of the granite sample and analyze the phase features and minerals percentage. Liu et al. (2022) found that quartz has a larger Young's modulus and hardness than clay minerals and organic matter in the shale. However, these results are based on nano-indentations on each individual mineral grain while the mineral matrix (e.g., include mineral grain boundaries) at the micro-scale is not considered. This research aimed to investigate the influence of micro-scale mineralogical characteristics on rock behaviour and properties. Five rock samples were prepared with different depths of drilling core from the Montney Play in BC. Rock properties, such as Young's modulus and hardness, were obtained through instrumented indentation tests. The mineralogical characteristics and their change during the indentation tests were analyzed by SEM/EDS analysis and image processing. The study established relationships between mineralogical characteristics and rock properties. 2 2.1

METHODOLOGIES Materials and Experiments

Five samples were prepared from the drilling core of well #28232 (HZ TOWN C-031-H/094-B-09), located in the Lower Triassic Montney play in Northeast British Columbia, Canada. The sample information is listed in Table 1. Table 1. Detailed information on every sample No.

Measured depth (m)

Formation

S1

2068.29

Doig Formation

S2

2068.55

Doig Formation

S3

2265.85

Montney Formation

S4

2336.04

Montney Formation

S5

2336.37

Montney Formation

Instrumented micro-indentation tests were performed with a Nanovea M1 Indentation Instrument by 1 mm diameter Brinell indenter. Nine indentation tests (indent locations from L1 to L9) were performed on each sample by using a three-by-three grid shown in Figure. 1. During an indentation test, the indentation force increased gradually to 35 N, and then unloaded to 0 N, both at a constant rate of 17.5 N/minute. Note that, in addition to the loading and unloading stages, L1, L2 and L3 (Fig. 1) have a load-holding stage where the loading force was maintained for 300 seconds once the loading force reaches

35 N to investigate the creep mechanism of Montney; these results are not included in this paper. The curve of the unloading stage can be used to calculate the local conventional mechanical properties, such as Young's modulus and hardness. The method to calculate the hardness and Young's modulus is offered by Oliver and Pharr (Oliver & Pharr, 2004).

Figure 1. Test location (left), and Micro-indentation instrument (right) In order to determine the localized microstructure of mineral grains and their chemical elements on the indentation locations, SEM and EDS analysis were carefully conducted on the same indents (L1 to L9) resulting from the indentation tests. The electron images of tested locations were obtained by the Tescan Mira 3 XMU Scanning Electron Microscope employing a field emission gun, and elemental distributions were determined by the Oxford Instruments X-Max Energy Dispersive Spectrometer detector. 2.2

SEM/EDS Image Processing

SEM and EDS can provide detailed images and elemental maps of the sample surface. The mineral types at a depth of the cores of well #28232 (HZ TOWN C-031-H/094-B-09) can be found from previous laboratory testing results that are retrieved from the BC Oil and Gas Commission website. Detailed information on mineral and their chemical formulas are listed in Table 2. The chemical formulas of minerals and chemical elements from layered images of EDS at the same location can be used to determine the grain boundaries and types of minerals. Table 2. Minerals type and their chemical formulas Mineral type

Chemical formula

Albite

NaAlSi3O8

Calcite

CaCO3

Dolomite

Ca(Mg0.67Fe0.33)(CO3)2

Muscovite

KAl2(Si3AlO10)(OH)2

Orthoclase

KAlSi3O8

Pyrite

FeS2

Quartz

SiO2

Chlorite

(Mg,Al)6(Si,Al)4O10(OH)8

However, analyzing SEM data can be challenging, and the analysis of mineral samples often involves a variety of techniques and processes and requires a series of steps to extract mineralogical information. Figure 2 presents the process in our study to generate a mineral map, which provides valuable insights into the mineral composition and structure of the sample. Finally, the weight percentages (wt%) of all chemical elements were calculated from the mineral map, and the results were used to compare with the wt% of chemical elements from the SEM and EDS analysis, which ensured this image process accurately analyzed mineral samples and obtain detailed information on their chemical composition and structure.

The result (Figure 3) shows that the wt% of most elements calculated from the image processing program is consistent with those detected by SEM/EDS analysis. However, the wt% of Si is smaller, and the wt% of Al is larger than that from SEM/EDS analysis. This is probably due to the mineral grains are assumed to extend downwards in the depth direction from the 2D mineral map as columnar bodies, while the depth of SEM detection is 2 mm. The rock sample is heterogeneous with mineral grains having varying shapes along the depth direction, which may result in some differences between the two results.

Figure. 2 Flow chart of image processing 3 3.1

LABORATORY RESULTS Mechanical Response

Through the instrumented indentation test, the indentation force and indentation depth curves throughout the entire experiment can be obtained. Figure. 4 shows the curve of indentation force and indentation depth of sample S2.

Figure. 3 Comparison of the weight percentage of chemical elements from the image process and the SEM and EDS analysis Based on the indentation depth and indenter geometry, the contact area between rock and the indenter and their changes throughout the entire indentation process can be determined. With the known loading area and the mineral map, the change in the mineral composition of the contact area during indentation tests can be calculated, which is used to correlate to the rock mechanical response under indentation tests.

Figure. 4 Indentation force vs. indentation depth of S2 For the loading stage, the L1 test of S2 has a large indentation depth during the initial stage, which is because the indenter initially lands exactly on a pore. For the unloading stage, the indentation depth increased as the indentation force decreased, like L4 and L9, so the slope

of the initial unloading stage around 35 N is negative. This may be due to the occurrence of microfractures by the peak force, which causes an increase in depth even during the initial unloading stages. Meanwhile, the recovery of indentation depth (i.e. indentation depth decreases) during the holding stage for L1, L2 and L3 has been found. This could be because the elastic recovery occurred in the holding stage. However, this phenomenon is out of scope in this paper. L1, L2 and L3 of all samples with a loading holding stage were not included in the result analysis. The indentation depth did not fully recover to zero after the unloading stage. This final depth is the unrecoverable plastic deformation, whereas the difference between the maximum and the final depth is termed as the elastic depth. The maximum depths, elastic depths, plastic depths and the average value of these three typical depths for all

samples are shown in Figure. 5. Here, to ensure data consistency, the curves of L1, L2 and L3 a load-holding stage were discarded. Meanwhile, due to instability in collecting S1 data, only the L6 and L9 data for S1 were used. The maximum depth (23.07 µm) and elastic depth (16.17 µm) occurred at the location of L5 of S3. The minimum depth (10.41 µm) and elastic depth (2.80 µm) occurred at L7 of S5. Further, the ratio of elastic depths and plastic depths is different for different samples. The average of elastic depths (7.29 µm) and plastic depths (8.20 µm) of S1 and the average of elastic depths (8.17 µm) and plastic depths (6.80 µm) are very similar. The average elastic depths of S3 (11.89 µm) are larger than the plastic depth (7.20 µm), while the elastic depths of S4 (5.61 µm) and S5 (4.02 µm) are smaller than the plastic depth of S4 (9.72 µm) and S5 (8.32 µm).

Figure. 5 Maximum depths, elastic depths and plastic depths for all the sample

Figure. 6 Hardness, Young's modulus and average values Figure. 6 shows the result of hardness, Young's modulus and their average values for all samples, which are calculated by the method mentioned in section 2.2.1. Samples from lower depths (S1, S2 and S3) have softer mechanical properties, lower Young's modulus and hardness than samples from higher depths (S4 and S5). The smallest value of Young's modulus is L4 in S3 (18.15 GPa), while the largest is L5 in S4 (65.15 GPa). The smallest value of the hardness is L4 in S3 (0.86 GPa), while the largest is L7 in S5 (1.19 GPa).

3.2

Mineral Composition and Microstructure

The mineral maps were obtained by SEM/EDS image processing to extract mineralogical information. One typical mineral map can be appreciated in Figure. 7. From the mineral maps as well as SEM/EDS images, the mineral grain size decreased with the increment of samples' depths.

Figure. 7 Typical mineral maps for L2 of S In addition, the area percentage (A%) of pores and minerals for all sample locations can be extracted by the mineral maps shown in Figure. 8. The A% of quartz was the largest, while albite, pyrite and chlorite were among the smallest for all the samples. Further, small amounts of metal, Ti, were also present in the samples. The A% of

calcite and dolomite were relatively larger than that of muscovite for S1 and S2. In contrast, A% of muscovite appeared to be relatively large, while calcite and dolomite were relatively small area ratios in S4 and S5, and S3 appears to have a relatively equal distribution of calcite, dolomite, and muscovite.

Figure. 8 Porosity and A% of minerals for different samples with maximum indentation depths 4

MINERALOGICAL INFLUENCE ON MECHANICAL RESPONSE

The Pearson correlation coefficient is used to investigate the influence of mineralogical characteristics on indentation depths, Young's modulus and hardness of the sample. The P-value of the two groups’ data is calculated and the P less than 0.05 indicates a significant correlation. R is the correlation coefficient ranging from -1 to 1. Minus R shows the negative correlation and plus R shows the positive correlation, while R=0 represents no correlation The correlation coefficient was calculated by the following equation (Vetterling et al., 1992): 𝜌(𝐴, 𝐵) =

1 𝑛−1

𝐴𝑖 −𝜇𝐴

∑𝑛𝑖=1(

𝜎𝐴

𝐵𝑖 −𝜇𝐵

)(

𝜎𝐵

)

[1]

where N is the sample size of variables A and B; 𝜇𝐴 and 𝜎𝐴 are the mean and standard deviation of A, respectively. 𝜇𝐵 and 𝜎𝐵 are the mean and standard deviation of B, respectively. 4.1

Typical Indentation Depths

Figure. 9 shows scatter charts of indentation depths and A% of minerals, as well as values of the P-values and R. For scatter charts, the horizontal ordinate is the maximum depth, elastic depth and plastic depth, respectively, and the vertical ordinate presents A% of all the minerals. According to the P-values and R, there are correlations between porosity, albite, orthoclase, pyrite and the maximum depth, as the P-values are smaller than 0.05. The values of R for porosity and albite are positive, which implies a larger

maximum depth at higher porosity and albite. In contrast, the values of R for orthoclase and pyrite are negative, which means these minerals have a negative relation with maximum depth. The impact of porosity and minerals on elastic depth is the same as maximum depth; porosity and albite have positive relations to elastic depth, while orthoclase and pyrite have a negative relation to elastic depth. Further, the plastic depth is influenced by porosity and most minerals except albite, orthoclase and quartz. According to the R-value, porosity, calcite, and dolomite have a negative correlation with plastic depth. In contrast, muscovite, pyrite, chlorite, and Ti metal have a positive correlation with plastic depth.

Based on the above analysis, currently, there is no significant correlation between all three typical indentation depths and quartz from the results. The impact of mineralogical characteristics on maximum depth is the same as those on elastic depth. In contrast, the impact of porosity and pyrite on plastic depth showed different trends with that of maximum depth and elastic depth. Further, most minerals affect the plastic deformation depth. However, albite and orthoclase, which have an impact on the maximum depth and elastic depth, have no significant correlation with the plastic depth.

Figure. 9 The scatter chart of indentation depths vs. A% of minerals and the values of the correlation coefficient 4.2

Young's Modulus and Hardness

Figure. 10 shows the scatter chart of indentation depths and hardness and Young's modulus and the values of the correlation coefficient. For scatter charts, the horizontal ordinate is the hardness and Young's modulus, reprehensively, and the vertical ordinate presents A% of all the minerals. There is no significant correlation between porosity, minerals and hardness. Compared with the hardness, Young's modulus of samples is influenced by more minerals, except calcite, dolomite, chlorite and quartz. Further, according to the R-value, porosity and

albite have a negative correlation with Young's modulus. In contrast, muscovite, orthoclase, pyrite, and Ti have a positive correlation. Based on the analysis of the influence of mineralogical characteristics on three typical depths, Young's modulus and hardness. Porosity, albite, orthoclase and pyrite have significant correlations with both elastic depth and Young's modulus. However, the impact of these factors on Young's modulus and elastic depth is exactly the opposite. In contrast, the impact of porosity, muscovite orthoclase, pyrite, and Ti on Young's modulus and plastic depth is identical.

Figure. 10 The scatter chart of indentation depths vs. hardness and Young's modulus and the values of the correlation coefficient 5

DISCUSSIONS

The impact of mineralogical characteristics on rock behaviours and properties has been discussed in section 4. However, the results presented in section 4 mainly explore the impact of individual mineralogical characteristics on rock samples from different depths of the Doig Formation and Montney Formation. For some minerals, such as quartz, no significant correlation was

observed between these minerals and rock behaviours and properties, possibly due to insufficient data distribution in the rock samples, which cannot reflect the effect of these minerals. Table 3 lists the average value (Ave), standard deviation (Std) and the coefficient of variation (Cv) of A% of minerals for all sample locations at maximum indentation force. The Cv of quartz is relatively smaller than that of other minerals.

Table 3. Summary of A% of minerals for all sample locations at maximum indentation force

6

Minerals

Albite

Calcite

Dolomite

Muscovite

Orthoclase

Pyrite

Chlorite

Ti

Quartz

Ave

6.61

12.71

14.30

15.43

7.70

1.70

3.06

0.14

34.90

Std

3.78

6.65

11.08

8.61

3.27

0.88

2.72

0.19

8.58

Cv

0.57

0.52

0.78

0.56

0.43

0.52

0.89

1.36

0.25

CONCLUSIONS

This research aimed to investigate the influence of mineralogical characteristics on rock behaviour and properties in the Doig and Montney Formations. Through indentation tests and SEM/EDS analysis, the study established relationships between mineralogical characteristics, rock behaviour, and properties, such as hardness and Young's modulus. The results demonstrated that certain minerals have significant correlations with rock properties: 1) The impact of mineralogical characteristics on maximum depth and elastic depth is the same; porosity and albite are positive relations to maximum depth and elastic depth, while orthoclase and pyrite have negative relations to maximum depth and elastic depth. Most minerals affect the plastic deformation depth. However, the impact of minerals on plastic depth showed

different trends with that of maximum depth and elastic depth. 2) There is no significant correlation between mineralogical characteristics and hardness. The impact of mineralogical characteristics on Young's modulus showed different trends with elastic depth and maximum depth. In contrast, the impact of mineralogical characteristics on Young's modulus showed the same trends with plastic depth. The insights gained from this study contribute to a better understanding of how mineralogical characteristics impact rock behaviour and properties. Progress is being on linking the mineral characteristics to indentation test data using a machine learning approach. 7 REFERENCES Akkaş, E., Akin, L., Evren Çubukçu, H., & Artuner, H. (2015). Application of Decision Tree Algorithm for classification and identification of natural minerals

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