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trends and digital advances in engineering geology
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Table of contents :
Title Page
Copyright Page
Book Series
Editorial Advisory Board
List of Contributors
Table of Contents
Detailed Table of Contents
Preface
Chapter 1: Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass
Chapter 2: Prediction of The Uniaxial Compressive Strength of Rocks Materials
Chapter 3: Fuzzy Rock Mass Rating
Chapter 4: Weathering Indices Used in Evaluation of the Weathering State of Rock Material
Chapter 5: Criteria for Surface Rupture Microzonation of Active Faults for Earthquake Hazards in Urban Areas
Chapter 6: Excavatability Assessment of Rock Masses for Geotechnical Studies
Chapter 7: Geophysical Surveys in Engineering Geology Investigations With Field Examples
Chapter 8: A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks
Chapter 9: Seismic Microzonation and Site Effects Detection Through Microtremors Measures
Chapter 10: General Trends and New Perspectives on Landslide Mapping and Assessment Methods
Chapter 11: Slope Stability of Soils
Chapter 12: Determination of the Cyclic Properties of Silty Sands
Chapter 13: A Review on Enhanced Stability Analyses of Soil Slopes Using Statistical Design
Chapter 14: Geological and Geotechnical Investigations in Tunneling
Chapter 15: Multi Criteria Decision Making Techniques in Urban Planning and Geology
Chapter 16: Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin (Turkey)
Chapter 17: Integration Between Urban Planning and Natural Hazards For Resilient City
Chapter 18: Soil Liquefaction Assessment by Anisotropic Cyclic Triaxial Test
Compilation of References
About the Contributors
Index

Citation preview

Handbook of Research on Trends and Digital Advances in Engineering Geology Nurcihan Ceryan Balikesir University, Turkey

A volume in the Advances in Civil and Industrial Engineering (ACIE) Book Series

Published in the United States of America by IGI Global Engineering Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2018 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Ceryan, Nurcihan, editor. Title: Handbook of research on trends and digital advances in engineering geology / Nurcihan Ceryan, editor. Description: Hershey, PA : Engineering Science Reference, [2018] | Includes bibliographical references. Identifiers: LCCN 2017009487| ISBN 9781522527091 (hardcover) | ISBN 9781522527107 (ebook) Subjects: LCSH: Engineering geology--Data processing--Handbooks, manuals, etc. Classification: LCC TA705 .H353 2018 | DDC 624.1/510285--dc23 LC record available at https://lccn.loc.gov/2017009487 This book is published in the IGI Global book series Advances in Civil and Industrial Engineering (ACIE) (ISSN: 23266139; eISSN: 2326-6155) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

Advances in Civil and Industrial Engineering (ACIE) Book Series Ioan Constantin Dima University Valahia of Târgovişte, Romania

ISSN:2326-6139 EISSN:2326-6155 Mission

Private and public sector infrastructures begin to age, or require change in the face of developing technologies, the fields of civil and industrial engineering have become increasingly important as a method to mitigate and manage these changes. As governments and the public at large begin to grapple with climate change and growing populations, civil engineering has become more interdisciplinary and the need for publications that discuss the rapid changes and advancements in the field have become more in-demand. Additionally, private corporations and companies are facing similar changes and challenges, with the pressure for new and innovative methods being placed on those involved in industrial engineering. The Advances in Civil and Industrial Engineering (ACIE) Book Series aims to present research and methodology that will provide solutions and discussions to meet such needs. The latest methodologies, applications, tools, and analysis will be published through the books included in ACIE in order to keep the available research in civil and industrial engineering as current and timely as possible.

Coverage • Transportation Engineering • Urban Engineering • Construction Engineering • Optimization Techniques • Hydraulic Engineering • Structural Engineering • Ergonomics • Productivity • Materials Management • Operations Research

IGI Global is currently accepting manuscripts for publication within this series. To submit a proposal for a volume in this series, please contact our Acquisition Editors at [email protected] or visit: http://www.igi-global.com/publish/.

The Advances in Civil and Industrial Engineering (ACIE) Book Series (ISSN 2326-6139) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http://www. igi-global.com/book-series/advances-civil-industrial-engineering/73673. Postmaster: Send all address changes to above address. Copyright © 2018 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or information and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global.

Titles in this Series

For a list of additional titles in this series, please visit: www.igi-global.com/book-series

Recent Advances in Applied Thermal Imaging for Industrial Applications V. Santhi (VIT University, India) Engineering Science Reference • copyright 2017 • 306pp • H/C (ISBN: 9781522524236) • US $205.00 (our price) Performance-Based Seismic Design of Concrete Structures and Infrastructures Vagelis Plevris (Oslo and Akershus University College of Applied Sciences, Norway) Georgia Kremmyda (University of Warwick, UK) and Yasin Fahjan (Gebze Technical University, Turkey) Engineering Science Reference • copyright 2017 • 320pp • H/C (ISBN: 9781522520894) • US $205.00 (our price) Engineering Tools and Solutions for Sustainable Transportation Planning Hermann Knoflacher (Vienna University of Technology, Austria) and Ebru V. Ocalir-Akunal (Gazi University, Turkey) Engineering Science Reference • copyright 2017 • 374pp • H/C (ISBN: 9781522521167) • US $210.00 (our price) Design Solutions and Innovations in Temporary Structures Robert Beale (Independent Researcher, UK) and João André (Portuguese National Laboratory for Civil Engineering, Portugal) Engineering Science Reference • copyright 2017 • 503pp • H/C (ISBN: 9781522521990) • US $200.00 (our price) Modeling and Simulation Techniques in Structural Engineering Pijush Samui (National Institute of Technology Patna, India) Subrata Chakraborty (Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India) and Dookie Kim (Kunsan National University, South Korea) Engineering Science Reference • copyright 2017 • 524pp • H/C (ISBN: 9781522505884) • US $220.00 (our price) Computational Modeling of Masonry Structures Using the Discrete Element Method Vasilis Sarhosis (Newcastle University, UK) Katalin Bagi (Budapest University of Technology and Economics, Hungary) José V. Lemos (National Laboratory for Civil Engineering, Portugal) and Gabriele Milani (Technical University in Milan, Italy) Engineering Science Reference • copyright 2016 • 505pp • H/C (ISBN: 9781522502319) • US $210.00 (our price) Handbook of Research on Emerging Innovations in Rail Transportation Engineering B. Umesh Rai (Chennai Metro Rail Limited, India) Engineering Science Reference • copyright 2016 • 664pp • H/C (ISBN: 9781522500841) • US $235.00 (our price)

701 East Chocolate Avenue, Hershey, PA 17033, USA Tel: 717-533-8845 x100 • Fax: 717-533-8661 E-Mail: [email protected] • www.igi-global.com

Editorial Advisory Board Nuray Korkmaz Can, Istanbul University, Turkey Murat Ercanoglu, Hacettepe University, Turkey Murat Karakus, University of Adelaide, Australia Ayhan Kesimal, Karadeniz Technical University, Turkey Srđan Kostić, Institute for Development of Water Resources, Serbia Harun Sonmez, Hacettepe University, Turkey Hasan Sözbilir, Dokuz Eylül University, Turkey Şule Tüdes, Gazi University, Faculty of Architecture, Turkey

List of Reviewers Mustafa Akgun, Dokuz Eylul University, Turkey Yesim Aliefendioglu, Ankara University, Turkey Erdin Bozkurt, Middle East Technical University, Turkey Gokhan Cevikbilen, Istanbul Technical University, Turkey Sener Ceryan, Balıkesir University, Turkey Süleyman Dalgıç, Istanbul University, Turkey Tamer Duman, General Directorate of Mineral Research and Exploration of Turkey, Turkey Senol Gürsoy, Karabük University, Turkey Mete Hancer, Pamukkale University, Turkey Fahrettin Kadirov, Azerbaijan National Academy of Sciences, Azerbaijan Sair Kahraman, Hacettepe University, Turkey Eyyüb Karakan, Kilis 7 December University, Turkey Kadir Karaman, Karadeniz Technical University, Turkey Celal Karpuz, Middle East Technical University, Turkey Cumhur Kocabas, Balıkesir University, Turkey Cağlar Özkaymak, Afyon Kocatepe University, Turkey Burcu H. Özüduru, Gazi University, Turkey





Pijush Samui, National Institute of Technology Patna, India Levent Selcuk, Yüzüncü Yıl University, Turkey Koray Ulamis, Ankara University, Turkey Banu Yagci, Balıkesir University, Turkey Ahmet Gunes Yardimci, Middle East Technical University, Turkey Erol Yilmaz, Cayeli Copper Mine, Turkey

List of Contributors

Akyol, Erdal / Pamukkale University, Turkey.................................................................................... 257 Altun, Selim / Ege University, Turkey................................................................................................ 416 Avcı, Pınar / Hacettepe University, Turkey........................................................................................ 569 Aydin, Ali / Pamukkale University, Turkey........................................................................................ 257 Bayarı, Celal Serdar / Hacettepe University, Turkey......................................................................... 569 Calderon, Francisco Alberto / National Technology University, Argentina..................................... 326 Can, Nuray Korkmaz / Istanbul University, Turkey............................................................................ 31 Ceryan, Nurcihan / Balikesir University, Turkey................................................................................ 31 Ceryan, Sener / Balikesir University, Turkey............................................................................. 132,591 Cevikbilen, Gokhan / Istanbul Technical University, Turkey............................................................ 380 Cihangir, Ferdi / Karadeniz Technical University, Turkey................................................................ 231 Dalgıç, Süleyman / Istanbul University, Turkey................................................................................ 482 Ercanoglu, Murat / Hacettepe University, Turkey............................................................................ 350 Ercikdi, Bayram / Karadeniz Technical University, Turkey.............................................................. 231 Frau, Carlos Daniel / National Technological University, Argentina................................................ 326 Gallucci, Ruben / National Technological University, Argentina..................................................... 326 Genockey, Michael / University of Adelaide, Australia..................................................................... 281 Giolo, Emilce Gisela / National Technological University, Argentina............................................... 326 Gungor, Mahmud / Denizli Water and Sewerage, Turkey................................................................ 257 Ingerson, Ashton / University of Adelaide, Australia........................................................................ 281 J, Jagan / Galgotias University, India.................................................................................................... 1 Jones, Jesse / University of Adelaide, Australia................................................................................. 281 Karakan, Eyyüb / Kilis 7 Aralik University, Turkey......................................................................... 416 Karakus, Murat / University of Adelaide, Australia......................................................................... 281 Karaman, Kadir / Karadeniz Technical University, Turkey.............................................................. 231 Karpuz, Celal / Middle East Technical University, Turkey................................................................. 97 Kaya, Ali / Pamukkale University, Turkey......................................................................................... 257 Kesimal, Ayhan / Karadeniz Technical University, Turkey............................................................... 231 Kostić, Srđan / Institute for Development of Water Resources “Jaroslav Černi”, Serbia................ 446 Kumlu, Kadriye Burcu Yavuz / Gazi University, Turkey........................................................... 530,591 Kurup, Pradeep / University of Massachusetts – Lowell, USA............................................................. 1 Kuşku, İbrahim / Istanbul University, Turkey................................................................................... 482 Lujan, Fabian / National Technological University, Argentina........................................................ 326 Özkaymak, Çağlar / Afyon Kocatepe University, Turkey................................................................. 187 Özyurt, Naciye Nur / Hacettepe University, Turkey........................................................................... 569 



Rengel, Marcelo Gerardo Jesús Guevara / National Technological University, Argentina............ 326 Rodriguez, Hernan / National Technological University, Argentina................................................ 326 Roy, Sanjiban Sekhar / VIT University, India....................................................................................... 1 Samui, Pijush / National Institute of Technology Patna, India.............................................................. 1 Sonmez, Harun / Hacettepe University, Turkey................................................................................ 350 Sözbilir, Hasan / Dokuz Eylül University, Turkey.............................................................................. 187 Sümer, Ökmen / Dokuz Eylül University, Turkey.............................................................................. 187 Tasdelen, Suat / Pamukkale University, Turkey................................................................................. 257 Thurlow, William / University of Adelaide, Australia....................................................................... 281 Tornello, Miguel / National Technological University, Argentina.................................................... 326 Tüdeş, Şule / Gazi University, Turkey......................................................................................... 530,591 Ulamis, Koray / Ankara University, Turkey....................................................................................... 631 Uzel, Bora / Dokuz Eylül University, Turkey...................................................................................... 187 Yardimci, Ahmet Gunes / Middle East Technical University, Turkey................................................. 97

Table of Contents

Preface................................................................................................................................................... xx Chapter 1 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass.................................................... 1 Jagan J, Galgotias University, India Sanjiban Sekhar Roy, VIT University, India Pijush Samui, National Institute of Technology Patna, India Pradeep Kurup, University of Massachusetts – Lowell, USA Chapter 2 Prediction of The Uniaxial Compressive Strength of Rocks Materials................................................. 31 Nurcihan Ceryan, Balikesir University, Turkey Nuray Korkmaz Can, Istanbul University, Turkey Chapter 3 Fuzzy Rock Mass Rating: Soft-Computing-Aided Preliminary Stability Analysis of Weak Rock Slopes..................................................................................................................................................... 97 Ahmet Gunes Yardimci, Middle East Technical University, Turkey Celal Karpuz, Middle East Technical University, Turkey Chapter 4 Weathering Indices Used in Evaluation of the Weathering State of Rock Material............................ 132 Sener Ceryan, Balikesir University, Turkey Chapter 5 Criteria for Surface Rupture Microzonation of Active Faults for Earthquake Hazards in Urban Areas.................................................................................................................................................... 187 Hasan Sözbilir, Dokuz Eylül University, Turkey Çağlar Özkaymak, Afyon Kocatepe University, Turkey Bora Uzel, Dokuz Eylül University, Turkey Ökmen Sümer, Dokuz Eylül University, Turkey





Chapter 6 Excavatability Assessment of Rock Masses for Geotechnical Studies................................................ 231 Ayhan Kesimal, Karadeniz Technical University, Turkey Kadir Karaman, Karadeniz Technical University, Turkey Ferdi Cihangir, Karadeniz Technical University, Turkey Bayram Ercikdi, Karadeniz Technical University, Turkey Chapter 7 Geophysical Surveys in Engineering Geology Investigations With Field Examples.......................... 257 Ali Aydin, Pamukkale University, Turkey Erdal Akyol, Pamukkale University, Turkey Mahmud Gungor, Denizli Water and Sewerage, Turkey Ali Kaya, Pamukkale University, Turkey Suat Tasdelen, Pamukkale University, Turkey Chapter 8 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks............................... 281 Murat Karakus, University of Adelaide, Australia Ashton Ingerson, University of Adelaide, Australia William Thurlow, University of Adelaide, Australia Michael Genockey, University of Adelaide, Australia Jesse Jones, University of Adelaide, Australia Chapter 9 Seismic Microzonation and Site Effects Detection Through Microtremors Measures: A Review...... 326 Francisco Alberto Calderon, National Technology University, Argentina Emilce Gisela Giolo, National Technological University, Argentina Carlos Daniel Frau, National Technological University, Argentina Marcelo Gerardo Jesús Guevara Rengel, National Technological University, Argentina Hernan Rodriguez, National Technological University, Argentina Miguel Tornello, National Technological University, Argentina Fabian Lujan, National Technological University, Argentina Ruben Gallucci, National Technological University, Argentina Chapter 10 General Trends and New Perspectives on Landslide Mapping and Assessment Methods.................. 350 Murat Ercanoglu, Hacettepe University, Turkey Harun Sonmez, Hacettepe University, Turkey Chapter 11 Slope Stability of Soils........................................................................................................................ 380 Gokhan Cevikbilen, Istanbul Technical University, Turkey



Chapter 12 Determination of the Cyclic Properties of Silty Sands........................................................................ 416 Eyyüb Karakan, Kilis 7 Aralik University, Turkey Selim Altun, Ege University, Turkey Chapter 13 A Review on Enhanced Stability Analyses of Soil Slopes Using Statistical Design........................... 446 Srđan Kostić, Institute for Development of Water Resources “Jaroslav Černi”, Serbia Chapter 14 Geological and Geotechnical Investigations in Tunneling................................................................... 482 Süleyman Dalgıç, Istanbul University, Turkey İbrahim Kuşku, Istanbul University, Turkey Chapter 15 Multi Criteria Decision Making Techniques in Urban Planning and Geology.................................... 530 Kadriye Burcu Yavuz Kumlu, Gazi University, Turkey Şule Tüdeş, Gazi University, Turkey Chapter 16 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin (Turkey)..................................................................................................................................... 569 Naciye Nur Özyurt, Hacettepe University, Turkey Pınar Avcı, Hacettepe University, Turkey Celal Serdar Bayarı, Hacettepe University, Turkey Chapter 17 Integration Between Urban Planning and Natural Hazards For Resilient City................................... 591 Şule Tüdeş, Gazi University, Turkey Kadriye Burcu Yavuz Kumlu, Gazi University, Turkey Sener Ceryan, Balikesir University, Turkey Chapter 18 Soil Liquefaction Assessment by Anisotropic Cyclic Triaxial Test.................................................... 631 Koray Ulamis, Ankara University, Turkey Compilation of References................................................................................................................ 665 About the Contributors..................................................................................................................... 755 Index.................................................................................................................................................... 762

Detailed Table of Contents

Preface................................................................................................................................................... xx Chapter 1 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass.................................................... 1 Jagan J, Galgotias University, India Sanjiban Sekhar Roy, VIT University, India Pijush Samui, National Institute of Technology Patna, India Pradeep Kurup, University of Massachusetts – Lowell, USA Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models. Chapter 2 Prediction of The Uniaxial Compressive Strength of Rocks Materials................................................. 31 Nurcihan Ceryan, Balikesir University, Turkey Nuray Korkmaz Can, Istanbul University, Turkey This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.  



Chapter 3 Fuzzy Rock Mass Rating: Soft-Computing-Aided Preliminary Stability Analysis of Weak Rock Slopes..................................................................................................................................................... 97 Ahmet Gunes Yardimci, Middle East Technical University, Turkey Celal Karpuz, Middle East Technical University, Turkey Rock mass classification systems are the most commonly used empirical tools in preliminary design of rock slopes. In spite of numerous advantages, these systems lack the common drawbacks of classification systems originated from uncertainties. These drawbacks may lead to similar or so close quality scores for different rock mass properties. Fuzzy Sets is a rising trend in describing Geomechanical problems by including the expert opinion. Especially in the case of weak rocks it allows prediction of more realistic rock mass quality scores. Although the empirical systems form a basis for the preliminary slope stability investigation, slope height and overall slope angle are still two missing important characteristic slope parameters. However, there have been some attempts to describe the graphical presentation of rock quality score, slope height and overall slope angle relation. These charts are called as slope performance charts. This chapter presents a short review on integration of Fuzzy RMR with these charts to provide a useful modification for the case of weak rock slopes. Chapter 4 Weathering Indices Used in Evaluation of the Weathering State of Rock Material............................ 132 Sener Ceryan, Balikesir University, Turkey There are various definition of weathering and differences between authors seem to steam in part from the different viewpoints of pedolog, geomorpholog, geolog, geochemists and geology engineer. In this study, weathering is handled from various aspects such as time, form and phases of progress, studies it is majored and research scale. The engineering behavior of rock materials depends not only on stress state and stress history but also on the physical, mineralogical and chemical change of the rock materials due to weathering. Weathering indices are used to define these changes due to weathering. Several weathering indices have been devised for quantifying the changes in the intrinsic properties of rocks from different points of view, some of which can be related to the engineering properties of weathered rocks. The most commonly used methods can be broadly categorized as chemical, mineralogical-petrographical, petrochemical and engineering indices. In this study, the brief literature review for weathering indices used to evaluate of the effects for weathering of rock materials. Chapter 5 Criteria for Surface Rupture Microzonation of Active Faults for Earthquake Hazards in Urban Areas.................................................................................................................................................... 187 Hasan Sözbilir, Dokuz Eylül University, Turkey Çağlar Özkaymak, Afyon Kocatepe University, Turkey Bora Uzel, Dokuz Eylül University, Turkey Ökmen Sümer, Dokuz Eylül University, Turkey Formation of surface rupture zone along active faults buried directly beneath major cities create devastating earthquakes that seriously threaten the safety of human lives. Surface rupture microzonation (SRM) is the generic name for subdividing a region into individual areas having different potentials hazardous



earthquake effects, defining their specific seismic behavior for engineering design and land-use planning in case a large devastating earthquake strikes the region. The basis of SRM is to model the rupture zone at the epicenter of an earthquake, and thus develop a hazard-avoid map indicating the vulnerability of the area to potential seismic hazard. Earthquake hazard assessment of active faults in urban areas are thus an important systematic engineering for disaster mitigation in major cities. Chapter 6 Excavatability Assessment of Rock Masses for Geotechnical Studies................................................ 231 Ayhan Kesimal, Karadeniz Technical University, Turkey Kadir Karaman, Karadeniz Technical University, Turkey Ferdi Cihangir, Karadeniz Technical University, Turkey Bayram Ercikdi, Karadeniz Technical University, Turkey The excavatability of rocks is of importance for the selection of suitable and cost–effective excavation methods not only in mining and quarrying but also in the construction of tunnels, subways, highways and dams. Moreover, selection of the right excavation method and equipment in mining and geotechnical projects depends on the excavatability properties of rocks. A number of different methods have been proposed to evaluate the excavatability of rocks based on their geotechnical properties, such as rock mass rating (RMR), uniaxial compressive strength (UCS), discontinuity spacing of rock masses, point load index (PLI) and seismic velocity of intact rock. The type of equipment used and the method of working also affect the excavatability of rocks. In this work, the term excavatability is considered as the ease of excavation of rock and rock masses and comprises the methods of digging, ripping, breaking and blasting for easy/very easy, moderate to difficult, soft or moderately to highly fractured rock and very difficult excavation conditions, respectively. Chapter 7 Geophysical Surveys in Engineering Geology Investigations With Field Examples.......................... 257 Ali Aydin, Pamukkale University, Turkey Erdal Akyol, Pamukkale University, Turkey Mahmud Gungor, Denizli Water and Sewerage, Turkey Ali Kaya, Pamukkale University, Turkey Suat Tasdelen, Pamukkale University, Turkey This chapter focusses on geophysical survey techniques, employed in engineering geological investigations and it includes case studies. Goal of a geophysical study in an engineering geological research is to display discontinuities in the rock masses, physico-mechanical properties of soils and rocks, groundwater exploration, faults, landslides, etc. It is also helpful to learn type and thickness of soil, layer inclination. These techniques include engineering geological surface mapping, geotechnical drilling and in situ testing. Then the obtained geophysical field data are analyzed and interpreted in conjunction with the results of geological information.The most common geophysical methods namely seismic, magnetometric, vertical electrical sounding (VES), Very Low Frequency (VLF) Electromagnetics methods, ground penetration radar (GPR) provide sufficient information about the subsurface although they have their limitations, setting up the minimum tests requirements in relation to the type of the geological formations.



Chapter 8 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks............................... 281 Murat Karakus, University of Adelaide, Australia Ashton Ingerson, University of Adelaide, Australia William Thurlow, University of Adelaide, Australia Michael Genockey, University of Adelaide, Australia Jesse Jones, University of Adelaide, Australia The Acoustic Emission (AE) due to the sudden release of energy from the micro-fracturing within the rock under loading has been used to estimate pre-load. Once the pre-load is exceeded an irreversible damage occurs at which AE signals significantly increase. This phenomenon known as Kaiser Effect (KE) can be recognised as an inflexion point in the cumulative AE hits versus stress curve. In order to determine the value of pre-load (σm) accurately, the curve may be approximated by two straight lines. The intersection point projected onto the stress axis indicates the pre-load. However, in some cases locating the point of inflexion is not easy. To overcome this problem we have developed a new method, The University of Adelaide Method (UoA), which use cumulative acoustic energy. Unlike existing methods, the UoA method emphasises the energy of each AE, the square term of the amplitude of each AE. As the axial pre-load is exceeded, the micro cracks become larger than the existing fractures and therefore energy released with new and larger cracks retain higher acoustic energy. Chapter 9 Seismic Microzonation and Site Effects Detection Through Microtremors Measures: A Review...... 326 Francisco Alberto Calderon, National Technology University, Argentina Emilce Gisela Giolo, National Technological University, Argentina Carlos Daniel Frau, National Technological University, Argentina Marcelo Gerardo Jesús Guevara Rengel, National Technological University, Argentina Hernan Rodriguez, National Technological University, Argentina Miguel Tornello, National Technological University, Argentina Fabian Lujan, National Technological University, Argentina Ruben Gallucci, National Technological University, Argentina Seismic microzonation of a city can be a difficult and expensive undertaking depending on the method used. In the last years, the HVSR method has been one of the most popular ways to define the natural frequency of the soil and seismic amplification factor in order to make quick microzonations due to that it is an expeditious and cheap method. This is very important in developing countries and poor countries. The fundamental reason to use this method is the fact that the amplification factor has well correlation with damage distribution. Additionally with the help of another methods it is possible obtain the structure of the superficial soil strata. In this chapter, an introduction with seismic microzonation, site effects concepts, microtremors, description of the HVSR method, advantages and disadvantages of this method, limitations and comparison with other methods, are presented. Finally, highlight of the importance of the method in order to identify site effects are displayed as examples and the incorporation of these data to Geographic Information Systems is also shown.



Chapter 10 General Trends and New Perspectives on Landslide Mapping and Assessment Methods.................. 350 Murat Ercanoglu, Hacettepe University, Turkey Harun Sonmez, Hacettepe University, Turkey Landslides and their consequences are of great importance throughout the world and they constitute an important responsibility on the damages and fatalities among the natural or man-made hazards. Landslide mapping and assessment studies have become a very important issue for the geoscientists and the decision makers to prevent from the consequences of the landslides, particularly in the last decades. In addition to the increase in population and poor economic conditions, unconsciously built settlements, located in the landslide-prone areas, were the most influencing factors on these losses and damages sourced from the landslides. This section particularly focuses on the landslide mapping and assessment methods considering the chronological development of these methods. In addition, this section also summarizes the landslide inventory, susceptibility, hazard and risk concepts, considering the scientific landslide literature. Furthermore, past-actual trends and new perspectives on these issues were also compiled to show the readers how this subject emerged and evolved progressively. Chapter 11 Slope Stability of Soils........................................................................................................................ 380 Gokhan Cevikbilen, Istanbul Technical University, Turkey Slope stability problems in soils underlie most of the landslides that cause losses of human and property in the world. The stability of natural or man-made slopes in soils is an important topic that requires great attention while site exploring, testing, modelling and analyzing. Engineering geology and geotechnical engineering interdisciplinary team work is essential to achieve a sufficient understanding of site geology and the behavior of soil. The developments of urban areas require new sites for settlement. Soil structure interaction in slopes requires more sophisticated numerical analysis methods to develop. This section particularly summarizes the factors cause a slope to fail. In addition, site exploration steps, in-situ and laboratory test methods were mentioned. Slope stability analysis methods such as LEM, FEM, DEM, BEM were discussed in details. The developments of empirical or statistical regional approaches were stated. Remediation techniques were discussed regarding the construction costs. Finally, the necessity of further studies in numerical modelling was emphasized. Chapter 12 Determination of the Cyclic Properties of Silty Sands........................................................................ 416 Eyyüb Karakan, Kilis 7 Aralik University, Turkey Selim Altun, Ege University, Turkey Liquefaction may be triggered by cyclic loading on saturated silty sands, which is responsible of severe geotechnical problems. Development of excess pore water pressure in soil results in a liquid-like behavior and may be the reason of unavoidable superstructural damage. In this study, in order to investigate the behavior of saturated silty sands exposed to cyclic loading under undrained conditions, a systematic testing program of stress-controlled cyclic triaxial tests was performed on specimens of different silt contents, under different loading conditions and environment. The effect of parameters such as silt content on the liquefaction behavior of specimens was studied. Pore water pressure and shear strain curves were obtained for the silty sands. Furthermore, the boundaries existing in the literature on sands are compared with the results current research, on silty sands. Conclusively, the outcomes of this study were useful to develop insight into the behavior of clean and silty sands under seismic loading conditions.



Chapter 13 A Review on Enhanced Stability Analyses of Soil Slopes Using Statistical Design........................... 446 Srđan Kostić, Institute for Development of Water Resources “Jaroslav Černi”, Serbia This chapter deals with the application of experimental design in slope stability analysis. In particular, focus of the present chapter is on the application of Box-Behnken statistical design for assessment of stability of slopes in homogeneous soil (general case), for estimation of slope stability in clay-marl deposits at the edge of Neogene basins (case study) and for the extension of grid search method for locating the critical rupture surface. Extensive statistical analysis, internal and external validation imply high estimation accuracy and reliability of developed mathematical expressions for slope safety factor and for parameters of location of critical rupture surface. Main advantages and limitations of the proposed approach are thoroughly discussed with suggestions for main directions of further research. Chapter 14 Geological and Geotechnical Investigations in Tunneling................................................................... 482 Süleyman Dalgıç, Istanbul University, Turkey İbrahim Kuşku, Istanbul University, Turkey The one of important matter of the design of tunnel is that choosing of the building materials and to make away rock material from tunnel walls or surface. Another important point is that these factors are effective for the building of tunnel process. It is necessary to investigation detailed the matters such as choosing the route of tunnel, the method of excavation, design of tunnel, choosing of the building material, to preparing to application Project, revision of the Project, recycling of the rest materials etc. The one of important duty of engineer is that to realize the importance of security and economic conditions together. It isn’t random the economic and timing work about this type of project, on the contrary it is definitely depending on good geotechnical and geological pre-research. The research which is along the route of tunnel supply economic and feasible work for the Project. The unit of in this book we will describe the methodology of tunnel building by using suit geotechnical and geological processes. Chapter 15 Multi Criteria Decision Making Techniques in Urban Planning and Geology.................................... 530 Kadriye Burcu Yavuz Kumlu, Gazi University, Turkey Şule Tüdeş, Gazi University, Turkey In this paper, Multi Criteria Decision Making (MCDM) processes will be clarified in the context of the disciplines related with the spatial information, as urban planning and its geographical perspective. For this purpose, first Spatial MCDM will be introduced, then the relation between the geographical data and GIS is established. Therefore, following sections include the detailed explanation of three widely used Spatial MCDM techniques, as Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). These techniques will be clarified by giving examples related with urban planning and geological science.



Chapter 16 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin (Turkey)..................................................................................................................................... 569 Naciye Nur Özyurt, Hacettepe University, Turkey Pınar Avcı, Hacettepe University, Turkey Celal Serdar Bayarı, Hacettepe University, Turkey Land subsidence which is defined as gradual settling or sudden collapse of Earth’s surface, is a geohazard phenomenon that occurs worldwide. Land subsidence occurs in time mainly due to excessive groundwater abstraction. This problem occurs usually in semi-arid regions where the groundwater is the sole source of water. Eliminating the adverse effects of land subsidence requires careful observations on the temporal change of elevation coupled with groundwater flow modeling. In this study, numerical groundwater flow modeling technique is applied to a confined aquifer system in the Konya Subbasin of Konya Closed Basin (KCB), central Anatolia, Turkey. Groundwater head in the KCB has been declining with a rate of about 1m/year since early 1980s. Recent GPS observations reveal subsidence rates of 22 mm/year over the southern part of KCB. MODFLOW numerical groundwater flow model coupled with subsidence (SUB) package is used to simulate the effect of long term groundwater abstraction on the spatial variation of subsidence rates. Chapter 17 Integration Between Urban Planning and Natural Hazards For Resilient City................................... 591 Şule Tüdeş, Gazi University, Turkey Kadriye Burcu Yavuz Kumlu, Gazi University, Turkey Sener Ceryan, Balikesir University, Turkey Analyses and syntheses conducted before the urban planning process are significant. Accurate analysis and synthesis enable to determine proper site selection and the proper site selection is the basis of a sustainable urban plan. In this sense, fundamental analysis inputs of the proper site selection could be indicated as the related parameters of the earth sciences. The interpretation of these inputs require the essential analyses and syntheses of initially the geological and geotechnical research with geophysics, tectonic, topography, mineral and natural resources, hydrogeology, geomorphology and engineering geology. Synthesis maps composed of these inputs especially provide guides for natural thresholds consisting of landslide, flood, inundation, earthquake etc. for land use planning and site selection parts in the urban planning processes. In this regard, this chapter of the book contains the relation between the earth sciences parameters with the urban planning and the way these parameters lead the way of urban planning processes.



Chapter 18 Soil Liquefaction Assessment by Anisotropic Cyclic Triaxial Test.................................................... 631 Koray Ulamis, Ankara University, Turkey Liquefaction of saturated sandy soils is one of the most significant aspects of earthquake triggered natural hazards. The main mechanism deals with the loss of effective stress due to rapid pore water pressure generation during earthquake shaking. This chapter involves with the fundamental mechanism and impacts of liquefaction. Liquefaction susceptibility of geological environments are briefly represented for preliminary assessment. Standard procedures of liquefaction are summarized. The dynamic response of sands are also reviewed. A case of anisotropic loading is considered, using three different particle sized sands below a shallow footing. Such sandy soils are subjected to anisotropic consolidation before performing undrained cyclic triaxial testing along limited cycles. Variation of axial strain, pore water pressure and related parameters are investigated. Main outcome of this study is to review the initial liquefaction state of sands by anisotropic loading case. Compilation of References................................................................................................................ 665 About the Contributors..................................................................................................................... 755 Index.................................................................................................................................................... 762

xx

Preface

Engineering geology organize the bridge between the geological sciences to engineering study. It is mainly related to the application of geology to civil and mining engineering practice. The purpose is to assure that geological factors affecting the planning, design, construction and maintenance of engineering works and the developing of groundwater resources are recognized, sufficiently interpreted and presented for usage in engineering practice. But engineering geologists face the task of quantifying variability and uncertainties in observations and interpretations in less time with fewer resources. Traditional methods are able to meet these new needs. Such problems are far from being solved and will need much attention. With the right tools and an interdisciplinary profile, engineers can spend a lot less time searching for answers and much more time making critical engineering decisions. So, the aim of the book is to describe some aspects of modern engineering geology in a way that might be of interest to policy makers and forecasters of science and technology trends. The focus in this book is on the new trends and technology, and especially on different computational methods that can model engineering phenomena automatically. It will open up possibilities of more rigorous analysis with new methods and technology. So engineers can find the effective and efficient execution of their work to optimise very complicated and non-linear relations. The book will be very useful for academic scientists, industry and applied researchers, and policy and decision makers. There are 18 chapters in the book. The brief description of the book is given below. Chapter 1 adopts various intelligent model techniques like Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN) for determination of the elastic modulus of jointed rock mass and compares these models with the results Artificial Neural Network (ANN) models. The inputs of the ELM, GRNN and MPMR models are Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) (MPa), and elastic modulus (Em) (GPa) of intact rock and the output of these models is elastic modulus (Em) of jointed rock mass. Chapter 2 will briefly review direct and indirect methods to determine uniaxial compressive strength (UCS) based on regression method and soft computing techniques such as fuzzy interface system (FIS), artificial neural network (ANN) and least squares support vector machine (LS-SVM). Moreover, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models is investigated for predicting the UCS of the magmatic rocks from east Pondite, NE Turkey. Chapter 3 presents weak rocks, rock mass characterization and modification of a popular geomechanical classification system like the rock mass rating (RMR) for better presenting weak rock conditions and concerns in classifying. Therefore, the integration of Fuzzy RMR with these charts to provide a useful modification for the case of weak rock slopes is reviewed and a new fuzzy system is suggested and proven to be useful in characterizing the weak rock slopes. Finally, the Bieniawski’s original chart was  

Preface

modified and both the modified chart and proposed fuzzy RMR validated by two large slope failures in Turkish lignite mines. Chapter 4 will briefly give an overview on the principle of weathering indices. It is known that stress state, stress history, the physical, mineralogical and chemical change of the rock materials due to weathering influence the engineering behavior of rock materials. Therefore, it is important for geotechnical engineers to estimate weatherability of rocks, quantitatively the changes during weathering and classification of the weathered rocks. The most commonly used methods in the literature are chemical, mineralogical-petrographical, petro-chemical and engineering indices and these methods explain in detail. Chapter 5 evaluates all active fault classification criteria used in active tectonic studies and prepared geological maps enriched with seismologic, geologic, geodetic and solid paleoseismological data in order to achieve the best results. Firstly, the steps are explained in detail to identify active faults in a given region, then the earthquake potential of faults is determined and evaluated according to geological, geomorphologic, geophysic, paleoseismologic, geodetic and geoarchaeologic data. Chapter 6 presents the excavatability of rocks and selection of the right excavation method and equipment in mining and geotechnical projects in detail. A number of different methods have been discusses to evaluate the excavatability of rocks based on their geotechnical properties, such as rock mass rating (RMR), uniaxial compressive strength (UCS), discontinuity spacing of rock masses as well as point load index (PLI) and seismic velocity of intact rock. Chapter 7 discusses the methods of geophysics and their application to the field’s problems. The popular geophysical methods, which are seismic, magnetometric, vertical electrical sounding (VES), Very Low Frequency (VLF) Electromagnetics methods and ground penetration radar (GPR), are explained in detail and some case studies are investigated in this chapter. The given case studies in this chapter provides an insight into the effectiveness of the explained methods in engineering geology/geotechnical engineering applications. Chapter 8 introduces a new acoustic energy based method developed by The University of Adelaide Method (UoA) to estimate preloads on cored rocks. The proposed method successfully estimates the maximum pre-load stress in all of tests, to within 3% accuracy. Furthermore, this method was successful in both lateral and axial subcores, and therefore, the orientation of the sub-core is believed to not influence the stress being estimated, as it is the principal pre-load stress which is always prominent. Chapter 9 presents an introduction with seismic microzonation, site effects concepts, microtremors, description of the HVSR method, advantages and disadvantages of this method, limitations and comparison with other methods. Moreover, highlighting the importance of the method in order to identify site effects are displayed as examples and the incorporation of these data to Geographic Information Systems is also shown. Chapter 10 focuses on the landslide mapping and assessment methods considering the chronological development of these methods and also summarizes the landslide inventory, susceptibility, hazard and risk concepts, considering the scientific landslide literature. Moreover, past-actual trends and new perspectives on these issues is also compiled to show the readers how this subject emerges and evolves progressively. Chapter 11 presents slope stability of soils and reviews the types and causes of soil slope failures. Moreover, site exploration steps, in-situ and laboratory test methods is mentioned, and also limits equilibrium methods (LEM), the finite element method (FEM), the boundary element method (BEM) and the discrete element method (DEM) widely used the stability analysis of slope are discussed in details. Well known LEM available in most of the slope stability problems such as Bishop’s, Bishop’s Simplified, Janbu’s, Janbu’s Simplified, Spencer’s, Corps of Engineers Modified Swedish Method, Lowe and Karafiath’s, Morgerstern and Price Methods are examined. Furthermore, the developments of empirical or statistical regional approaches were stated and remediation techniques were discussed regarding the construction costs. The effect of intergranular – interfine void ratios and liquefaction behavior of silt sand mixtures is investixxi

Preface

gated in Chapter 12. A systematic testing program of stress-controlled cyclic triaxial tests is performed on specimens of different silt contents, under different loading conditions and environment to explore the behavior of saturated silty sands exposed to cyclic loading under undrained conditions. Based on the dynamic triaxial test results on the silt sand mixtures, relationships among factor of safety/liquefaction/ maximum shear strain, and also volumetric strain/ maximum shear strain is obtained. In Chapter 13, author provides a brief review on the existing application of statistical design in slope stability analysis. It focuses on the appliance of Box-Behnken statistical design for assessment of stability of slopes in homogeneous soil (general case), for estimation of slope stability in clay-marl deposits at the edge of Neogene basins (case study) and for the extension of grid search method for locating the critical rupture surface and main advantages and limitations of the proposed approach are thoroughly discussed with suggestions for main directions of further research. Chapter 14 briefly describe the methodology of tunnel building süit geotechnical and geological processes. For these purposes the parameters affecting the effective use of tunnel during and after the construction of tunnel are discussed in detail. Then In order to establish a stable structure in tunnels necessary geologic, geotechnical properties and hydrogeologic investigations are determined. It is emphasized that geophysical and drilling data also important for verifiying the geologic mapping, geologic model and tunnel cross section. To determine problematic sites along the tunnel route, necessary tests conducted within the borehole and on disturbed and undisturbed samples are emphasized. It is stated that some geotechnical measurements are needed to monitor and record changes in deformation and load on revetment elements and adjacent rocks in tunnels. To provide information on absolute displacement amount around the tunnel and depth and pattern of deformations on tunnel rock extensometer readings are adopted. They are also used for specifying and examination of length of rock bolts. In Chapter 15, multi criteria decision making (MCDM) processes is presented to clarify in the context of the disciplines related with the spatial information in urbanism and its geographical perspective. Spatial MCDM will be introduced and the relation between the geographical data and geological information system GIS is established. Three widely used Spatial MCDM techniques, Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), are explained with given examples related to urban planning and geological science. In Chapter 16, the phenomenon of land subsidence is investigated using groundwater flow model for Konya Closed Basin, which is the biggest endorheic basin in Turkey. It was revealed the subsidence rate of 22mm/year over the southern part of Konya Closed Basin by recent GPS observations. One of the most common causes of land subsidence is excessive usage of groundwater. Therefore, MODFLOW numerical groundwater flow model of Konya Closed Basin coupled with subsidence (SUB) package is used to simulate the effect of long term groundwater abstraction on the spatial variation of subsidence rates. 17 chapter discuses the relation between the earth sciences parameters with the urban planning and the way these parameters lead the way of urban planning prosesses. This chapter focus that accurate analysis and synthesis enable to determine proper site selection and the proper site selection is the basis of a sustainable urban plan. The interpretation of these inputs require the essential analyses and syntheses of initially the geological and geotechnical research with geophysics, tectonic, topography, mineral and natural resources, hydrogeology, geomorphology and engineering geology. For this purpose synthesis maps are produced based on the related parameters of the earth sciences. Synthesis maps shows guides for natural thresholds consisting of landslide, flood, inundation, earthquake etc. for land use planning and site selection parts in the urban planning processes. The aim of Chapter 18 is to present the soil xxii

Preface

liquefaction phenomena with brief explanations of the mechanism and the previous work is used to enlighten readers to understand soil susceptibility and the field investigation of liquefaction. Liquefaction is investigated by anisotropic cyclic testing of three different sands under undrained conditions. The case of a shallow footing is considered to depict the anisotropic loading conditions with some series of cyclic testing based on a fixed earthquake magnitude. Thus, it is reviewed the initial liquefaction state of sands by anisotropic loading case. It is not possible to cover the current trends and technology of all the engineering geology topics into one book. However, important aspects of engineering geology have been discussed in a new perspective. These approaches influences the way in which engineering geologists investigate the real world, how they communicate and how they solve . This can lead to better science, cost saving, increased speed and flexibility in problem solution. I hope the engineers and researcher will be subjected to a lot of pressure by the way this book and we will have to rethink our approaches to meet the demands.

xxiii

1

Chapter 1

Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass Jagan J Galgotias University, India

Pijush Samui National Institute of Technology Patna, India

Sanjiban Sekhar Roy VIT University, India

Pradeep Kurup University of Massachusetts – Lowell, USA

ABSTRACT Elastic Modulus (Ej) of jointed rock mass is a key parameter for deformation analysis of rock mass. This chapter adopts three intelligent models {Extreme Learning Machine (ELM), Minimax Probability Machine Regression (MPMR) and Generalized Regression Neural Network (GRNN)} for determination of Ej of jointed rock mass. MPMR is derived in a probability framework. ELM is the modified version of Single Hidden Layer Feed forward network. GRNN approximates any arbitrary function between the input and output variables. Joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) (MPa), and elastic modulus (Ei) (GPa) of intact rock have been taken as inputs of the ELM, GRNN and MPMR models. The output of ELM, GRNN and MPMR is Ej of jointed rock mass. In this study, ELM, GRNN and MPMR have been used as regression techniques. The developed GRNN, ELM and MPMR have been compared with the Artificial Neural Network (ANN) models.

INTRODUCTION The elastic modulus is a number that defines the object’s resistance to being deformed elastically but not permanently, when a force is applied to it. It is also defined as the slope of its stress-strain curve in the elastic deformation region. It is also known as modulus of elasticity, tensile modulus or Young’s modulus. Modulus of elasticity of rocks depend upon several factors, such as, • •

Surface texture Type of rock

DOI: 10.4018/978-1-5225-2709-1.ch001

Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

• •

Confining pressure Porosity

Usually, joints generate the decisive effects to the failure properties of rocks. Joint frequency can be defined as the number of joints per meter length. Tang (2015) considered the specific property, joint inclination of rock with many number of tests and found that the location and direction of main crack on rock masses without joint were uncertain and random, the peak strength was highest. The main cracks of jointed rock masses were affected obviously with joints, expected the joint inclined angle was 90ο. The peak strength was raised with the inclined angles bigger and bigger, even the peak strength was close to the rock masses without joint which the joints inclined angles were 90ο. Clearly, the joint inclined angle and its frequency affects obviously the strength and failure properties of rock masses. Joint surface roughness is a measure of the inherent surface unevenness and waviness of the discontinuity relative to its mean plane. The roughness is characterized by large scale waviness and small scale unevenness of a discontinuity. It is the principal governing factor the direction of shear displacement and shear strength, and in turn, the stability of potentially sliding blocks. Ebadi et al., (2011) utilized the resultant displacement of rock mass and joints for forecasting the deformation modulus of rock mass. They also concluded that confining pressure affects the rock mass deformation modulus linearly. In order to determine the value of elastic modulus for the rocks, the static and dynamic methods are available. The static methods comprised of tension or compression test, bending test and natural frequency vibration test. The quality of rock can be accessed by the elastic modulus value. Greater value of modulus of elasticity represents the high quality of rocks with better configuration. The elastic moduli and Poisson’s ratio adopts various applications that include: • • • • • •

Predictions of formation strength Well stimulation (fracture pressure and fracture height) Borehole and perforation stability Sand production and drawdown limits in unconsolidated formations Coal evaluation Determining the roof-rock-strength index for underground mining operations

The above mentioned in-situ tests may be applied, however those methods are very expensive and time-consuming.

BACKGROUND Elastic Modulus is the paramount parameter for the mining and civil engineering projects. It is also the eminent criterion for pre-failure mechanical behavior of rock mass. Goodman Jack Test, Cable Jacking test, Plate load test, etc., was used to determine the elastic modulus of rock mass. The available field tests for determination of elastic modulus are very expensive and time consuming (Bieniawski, 1978; Hoek & Diederichs, 2006). In order to overcome this difficulty, the researchers projected some empirical relationships to figure out the elastic modulus of rock mass (Bieniawski, 1973, 1978; Barton et.al., 1974; Hoek and Brown, 1997).

2

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Many machine learning techniques has its own reputations in wide area of field and infinite applications. Some of the few intelligent models adopted are Minimax Probablity Machine Regression (MPMR), Extreme Learning Machine (ELM) and Generalized Regression Neural Network (GRNN). Extreme Learning Machine (ELM) is an emerging learning technique which delivers the unified solution to generalized feed forward networks. The hidden neurons is very much important as it randomly generates and independent of applications. It has its great contribution towards the classification and regression applications. ELM has successfully landed its efficient hands towards strenuous sales prediction problems in the fashion retailing. These have been assessed by the real data from the fashion designer (Zhan et al. 2008). The photovoltaic power prediction is a paramount and challenging task in energy management systems. ELM predicted in an effective way for grid planning, scheduling, maintenance and improving stability on energy management (Teo et al. 2015). An algorithm for perception of new human face was developed based on ELM (Mohammed et al. 2011). Minimax Probablity Machine Regression (MPMR) maximizes the minimum probability of all outputs of the genuine regression functions. Similar to that of Support Vector Machine (SVM) (Schölkopf et al. 2001) MPMR of single class seeks to cleave the normal pattern from origin to the maximum margin (Kwok et al. 2007). MPMR helps in the novel detection of the worst case of pattern to fall in the normal region (Kwok et al. 2007). MPMR provides its better support in medical diagnosis by improving the sensitivity (Huang et al. 2006). Pollution prevention from the stack of industries is more mandatory in the environment. Anghel and Ozunu (2006) predicted the emission of pollutants and measured the concentrations of CO, NOx, NO, and SO2 by MPMR. Generalized Regression Neural Network (GRNN) was proposed by Specht (1991). It does not require any repetitive workouts as that of the back propagation method. The flow of Tigris River in Northern Iraq region was projected, since it’s very much strenuous and influential for flood mitigation and drought periods (Awchi, 2014). Liang et al. (2014) had utilized GRNN to its extent for weighing the urban distribution system’s resource integration process. They have justified that GRNN is more competent and conducive. The determination of elastic modulus by direct field test is very expensive and time consuming and result satisfaction is questionable (Hoek and Diederichs, 2006). This chapter discusses the determination of strength and deformations of rock mass which are very much pivotal for any analysis utilized for the design of slopes, underground excavations and foundations. However, the available in-situ methods are very much expensive and extracts more time, the adoption of numerical methods are very much useful for the determination of deformation modulus. These analytical methods not only determine the Elastic Modulus (Em), but also evaluate the parameters influencing them.

EXTREME LEARNING MACHINE (ELM) ELM was proposed by Huang et al. (2004) which give a promising learning algorithm for single hidden layer feedforward neural network (SLFN). ELM randomly chooses the weights of the inputs and figure out the output weights in an analytical way. The ELM issues the best generalization performance at the superlative learning speed and less human intervention. Random hidden layer feature mapping based ELM improves the stability in the calculation of the output weights according to the ridge regression theory (Huang et al. 2012, Man et al. 2011, and Man et al. 2011). The Kernel based ELM makes use of the corresponding kernel instead of the hidden layer feature mapping itself, and the dimensionality 3

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

of the hidden layer feature space needs not be specified either (Frénay and Verleysen, 2010, 2011). The incremental ELM (IELM) shows an efficient and practical way to construct the incremental feedforward network with a wide type of activation functions, where the hidden nodes can be added one by one (Huang & Chen, 2007; 2008). The online sequential ELM (OS-ELM) can learn the training data sequentially not only one-by-one but also chunk by chunk and discard the observations as soon as the learning procedure has already been done (Huang et al. 2011). The optimally-pruned ELM (OP-ELM) starts with a large network and then eliminates the hidden nodes that have low relevance to the learning (Miche et al. 2010, 2011). ELM ensembles are widely used to improve single network’s performance with a plurality consensus scheme (Heeswijk et al. 2009, 2011 and Nian et al. 2012).

Details of ELM Moore-Penrose generalized inverse and the minimum norm least-squares solution plays a vital role in the ELM algorithm. Let us consider the general linear system as Ax = y , where A may be singular and may not be square. It can be made very simple by the use of the Moore-Penrose generalized inverse (Serre, 2002). A matrix G of order n × m is the Moore-Penrose generalized inverse of matrix A of order m ×n , AGA = A, GAG = G,

(AG )

T

= AG, (GA) = GA T

(1)

The Moore-Penrose generalized inverse of matrix A will be denoted by A†



But for the minimum norm least squares solution of general linear system Ax = y , we say that x is the least squares solution if ∧

A x − y = min Ax − y

(2)

x

where

. is a norm in Euclidean space.

For the Single Layer Feed-forward Networks (SLFN), an extremely fast learning algorithm has been ~

~

proposed with N hidden neurons and the number of training samples N with the condition N ≤ N . Let us assume samples for N as (x i , ti ) where x i = x i 1, x i 2 , x i 3 .....x in  ∈ Rn and ti = ti 1, ti 2 , ti 3 .....x in  ∈ Rm with the activation function g (x ) which are modeled mathematically as follows: T

T

~

N

∑ β g (w .x i =1

i

i

j

+ bi ) = o j , j = 1, 2, ...., N T

(3) T

where wi = wi 1, wi 2 , wi 3 .....x in  and βi = βi 1, βi 2 , βi 3 .....βin  are the weight vectors;

4

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

bi is the threshold and wi .x j represents the inner product of wi and xj The weight vector wi bridges the ith hidden neuron and the input neurons, whereas the other weight vector βi connects with the output neurons and the hidden neuron. The typical SLFN with the hidden neuron and the activation function can approximate the samples with zero error as ~

N

∑o j =1

j

− tj = 0

(4)

The above equation (4) depicts the existence of βi , wi and bi such that ~

N

∑ β g (w .x i =1

i

i

j

+ bi ) = t j , j = 1, 2, ...., N

(5)

The above equation (5) can be written precisely in the form Hβ =T

(6)

where  g (w1 ..x 1 + b1 )   .   .     H w1,...w ~ , b1,...b ~ , x 1,...x ~  =  .   N N N  g (w1 ..x N + b1 )     

  ...... g w ~ ..x 1 + b ~   N N ...... . ..... . ...... .   ...... g w ~ ..x N + b ~   N N

                ~  N ×N

(7)

where H is the hidden layer of the output matrix. tT   βT  1  1 .  .      and T =   β=  . .    T  T t ~  β ~  ~  N  N ×m  N  N ×m

(8)

5

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

The former works determined that when the count of concealed neurons is identical in number of ~  training samples N = N  , then the matrix H is square and invertible. Hence the SLFN can approximate   these training samples with zero error (Huang and Babri, 1998; Tamura and Tateishi, 1997; Huang, 2003). But in maximum cases count of hidden neurons is less than the number of training samples, ~

N < N , therefore H is non square and the existence of wi , bi , βi are difficult which leads to H β = T . ∧ ∧ ∧ ~   Thus it may necessary to determine the specific w i , b i , β i where i = 1,...., N  such that   ∧ ∧ ∧ ∧ ∧   ~ ~ H w 1,... w N , b 1,...b N ,  β− T = min H w1,....w ~ , b1,....b ~  β − T wi ,bi ,βi    N N

(9)

The above equation (9) is equivalent to the minimizing the cost function 2

 N~    E = ∑ ∑ βi g (wi .x j + bi ) − t j   j =1    u =1 N

(10)

For fixed input weights wi and the hidden layer biases bi , seen from equation (9), to train an SLFN ∧

is simply equivalent to finding a least-squares solution β of the linear system Hβ=T:  ∧   ~ ~ H w 1,... w N , b 1,...b N ,  β− T = min H w1,....w ~ , b1,....b ~  β − T β    N N

(11)

The smallest norm least squares solution of the above linear system is ∧

β =H†T

(12)

Precisely, for a given training set N =

{(x , t ) | x i

i

i

∈ Rn , ti ∈ Rm , i = 1,...,N

} with the activation



function g (x ) and hidden neuron number N , • • •

6

~   Commit random input weight wi and bias bi i = 1,...., N    Reckon the secluded layer output matrix H. Enumerate the output weight β which are defined in the equations (7) and (8).

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

In this study, joint frequency (Jn), joint inclination parameter (n), joint roughness parameter (r), confining pressure (σ3) (MPa), and elastic modulus (Em) (GPa) of intact rock, are used as input variables. The dataset consists of 896 data and they are normalized between 0 and 1, in the event of escaping from the computation difficulties. This chapter utilizes the database collected by Maji & Sitharam (2008). The details of the dataset have also been given by different researchers (Arora, 1987; Yaji, 1984; Roy, 1993; Brown & Trollope, 1970; Einstein & Hirschfield, 1973). The dataset was combined after experimenting uniaxial and triaxial tests on jointed rocks. In order to predict the Elastic modulus of rock mass through ELM model, 896 dataset have been cleaved into two: 1. Training Dataset: This is used to craft the model. In this study, 726 data are considered for training 2. Testing Dataset: This is used to evaluate the developed model. 170 data were utilized for testing. MATLAB package is used for developing the ELM model.

MINIMAX PROBABILITY MACHINE REGRESSION (MPMR) The ultimate aim of the pattern recognition and classification procedure is to accomplish the decision boundaries that cleave the different classes. A possible way to choose a classifier is to minimize the maximum probability of misclassification of future data points for the worst-case setting. MPMR from the linear MPMC and showing that, due to the symmetry, it is computationally easier to find an MPM regression model than it is to find an MPM classifier (Strohmann and Grudic. 2003). MPMR has greatly disseminated its success to the vast classification and regression problem (Lanckriet et al., 2002). Classically, the Gaussian process, make some assumptions with data distribution which leads to lack of generalization, but MPMR avoids this drawback. The specific and detailed provision of low bound on the probability that the predicting model is within the designated defect of the real regression function when the mean and covariance of the matrix are disclosed (Strohmann and Grudic. 2003). Anghel and Ozunu (2006) suggested the basic flowchart for the MPMR which was depicted in the upcoming figure 2. Figure 1. MPMR Algorithm

7

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Figure 2. Flowchart of MPMR

Details of MPMR Let us consider some unknown regression function f ∗ : Rd → R . Some bounded distribution creates −

some random vector x ∈ Rd , which has the mean and covariance x and ∑x . They will be represented −  in the form X , ∑x  . Let us consider the training dataset as T = (X1, y1 ) .... (X n , yn ) be generated   according to:

{

}

y = f * (X ) + ρ

(13) −

where ρ is the noise term with an expected value E (ρ ) = ρ = 0 and some finite varianceVar (ρ ) < ∞ . ∧

For the given hypothesis space Η functions from Rd to R , we have to determine the model f ∈ Η which enlarges the least probability of being ±ε and it is being symbolized as Φ f

8

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Φf =

inf_ _ 







(X ,y )~X ,y ,∑

{

}

Pr f (x ) − y < ε

(14)

where ∑ = cov (y, x 1, x 2 ,....x d ) We have to make an assumption that the first and second moments of the underlying distributions _

_

X , y and ∑ are finite. Φ ∧ ≥ Φ f for all f ∈ Η

(15)

f



The above equation (15) is the condition for satisfaction for the model f . For the linear model, Η contains all the functions that are linear combinations of the input vector: d

f (x ) = ∑ βj x j + β0 = βT X + β0

(16)

j =1

where β ∈ Rd , β0 ∈ R For the nonlinear models we consider the general basis function formulation as N

f (x ) = ∑ βi Ωi (X ) + β0

(17)

i =1

Smola and Scholkopf (1998) mentioned that the hypothesis space consists of all linear combinations of kernel functions with training inputs as their first arguments Ωi (X ) = K (X i , X ) . Therefore the

(

)

above equation (17) is as follows N

f (x ) = ∑ βi K (X i , X ) + β0

(18)

i =1

where X i ∈ Rd denotes the ith input vector of training set T =

{(X , y ).... (X , y )} . 1

1

n

n

MPMR consists of two dataset, training and testing dataset for predicting the elastic modulus of joint rock mass. Training dataset is used to frame the model, whereas testing dataset is used to verify the developed model. MPMR adopts the same dataset which was utilized for the ELM model.

9

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

GENERALIZED REGRESSION NEURAL NETWORK (GRNN) Specht (1991) proposed the GRNN, a branch from neural network and also belong to Radial Basis Function (RBF) network. However, it does not oblige any repetitive training procedure as that of Back Propagation (BP). Since GRNN is similar to RBF network, eventually the structure of GRNN is also similar to RBF which consists of input layer, number of hidden layers and a linear output layer. GRNN has different and valuable essence such as: • • • • •

Nimble learning Good convergence with more count of training samples Dispersed data will be handled well Possible memory hog Consumes less computing time

The upcoming figure 3 represents the structure of GRNN. The architecture of GRNN depicted that each layers of nodes plays distinctive aspects. The first tier consists of linear input layer as it is only liable for the disposal of normalized values to the hidden layer, however it does not engage in any computations. The next layer is the hidden layer; either it can be single hidden layer or several hidden layers. These hidden layers are in charge for the calculation of variables. In this layer, the count of neurons must be identical in the number of indexes. Euclidean distance function is the weight function which intents to compute the space between the input and the hidden tier. The threshold of the hidden layer will be represented by the symbol “.”. Gaussian function equation has the good local approximation capability which leads to the improvement in the efficiency of GRNN. Gaussian function is considered as the transfer function, which makes the hidden layer to yield large output, when the input value is close to intermediate of Gaussian function. Figure 3. Structure of GRNN

10

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

The last layer of the network is the linear output layer, and the weight function is the normalized dot product function. The distinctness between the GRNN and the RBF is only the linear output layer. The upcoming flowchart will depict the evaluation process of GRNN (Figure 4). • • • • • •

In the training and testing dataset generation, the scale of the dataset sample has to consider along with fidelity and reliability of the samples. In our case, GRNN model will be crafted by the MATLAB software When the construction of GRNN is completed, the normalized data can be brought into the GRNN model, and it yields the output. In order to evaluate the workability of the developed model, the output data and the actual data has to be computed. When the performance is not satisfactory, then the test on smooth factor has to be carried out. The smooth-factor value selection is based on the trial and error method. The smooth-factor value can be acquired, when the errors between the actual and the predicted is very low. After acquiring the smooth-factor, the final evaluation model has been established.

Details of GRNN The GRNN approximates any arbitrary function between input and output vectors, drawing the function estimate directly from the training data. Also it is consistent, (i.e.) when the training set size becomes large, the forecasting error becomes to the least, with the less restriction on the function. The GRNN Figure 4. Evaluation process of GRNN

11

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

have been utilized for the prediction of continuous variables. It is also related to the standard statistical technique called kernel regression. The regression techniques will yield the predicted value which trims the mean squared error. Let us consider f (x , y ) as the joint continuous probability density function, where x is the vector and y is the scalar variables. Then the conditional mean or the regression of y on X is given by ∞

E y | X  =

∫ yf (X , y )dy

−∞ ∞

∫ f (X , y )dy



(19)

−∞

The actual observations of x and y will be utilized when the density function f(x, y) is unknown (Specht, 1991). ∧

1

1 X f (X ,Y ) = (p +1)/2 (p +1) n σ (2π )

  X − Xi exp ∑ − 2α2 i=1   n

(

)

T

   Y −Y i   exp   2α2     

(



)  2

  

(20)



where f (X ,Y ) is the probability estimator which is the sum of sample probabilities; X i and Y i are the sample values of x and y; n is the number of samples p is the dimension of the vector variable x. α is the width for each sample

(

Di2 = X − X i

) (X − X ) T

i

(21)

where Di2 - is the scalar function. On defining the scalar function and performing the indicated integrations yield the following equation (21)  D 2  − i  i Y exp ∑  2  ∧ i =1  2α  Y (X ) = n  D 2   i  ∑ exp − 2α2  i =1   n

12

(22)

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

The above equation (22) helps is direct execution of numerical problems. The workability of GRNN rely upon the appropriate selection of α values. When the smoothing parameter α is made large, the estimated density is forced to be smooth and in the limit becomes a multivariate Gaussian with covariances α2I. On the other hand, a smaller value of α allows the estimated density to assume non-Gaussian shapes, but with the hazard that wild points may have too great an effect on the estimate (Specht, 1991). There few advantages of GRNN are listed below: •

This is the one way training network which means there is no possible for iteration. It dwindles the complications of the algorithm and also enhances its capability. The quantity of neurons is dictated by the training samples, and this quality has as of now been turned out to be the best esteem, which dispose of the dreary neurons testing. The weights between each layer are uniquely determined by the training samples, and they avoid modifying the weights, which can improve the algorithm’s learning efficiency. The activation function used in the RBF hidden layers is Gaussian function, which has the local activation characteristics of the input values, and it has a strong attraction of the input values which can help improving the learning efficiency.

• • •

In order to develop the GRNN model, the dataset has to be cleaved into: • •

Training Dataset: Used to craft the model. Testing Dataset: Used to evaluate the developed model.

GRNN adopts the same dataset which was utilized for the ELM and MPMR model. MATLAB software had serviced to frame the GRNN model.

RESULTS AND DISCUSSION This section refers to the output of the adopted intelligent model techniques. Coefficient of correlation (R) is used to assess the performance of the regression techniques. _ _    E − E jam  E − E jpm    jp  ∑  ja    i =1 _ _ n     E − E jam  E − E jpm    jp  ∑  ja    i =1 n

R=

(23)

_

_

where E ja and E jp are the actual and predicted values of Ej respectively, whereas E jam and E jpm are the mean values of actual and predicted Ej values and n is the number of data. For a better model, the value of R should be near to unity. ELM adopts the radial basis function as the activation function. Various numbers of concealed nodes have to be tried as trial and error approach, in order to determine the best performance. The developed

13

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

ELM provides the best workability for 14 hidden nodes. The performance of the training and the testing dataset are depicted in figure 5 and figure 6. Figures 5 and 6 depict the better workability of ELM in predicting the elastic modulus of jointed rock mass and it is assessed by the value of R. For MPMR, the two design values of ε and σ are required and it was obtained by trial and error approach. The framed MPMR shows its optimum capability at ε=0.001 and σ=0.53. Figures 7 and 8 provides the details about the performance of MPMR in predicting the Em. Figure 5. Performance of Training dataset of ELM

Figure 6. Performance of Testing dataset of ELM

14

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Figure 7. Performance of Training dataset of MPMR

Figure 8. Performance of Testing dataset of MPMR

The R values represented in the figure 7 and 8 clearly detailed the capability of the developed MPMR in forecasting the value of Em. The design value of α on Root Mean Square Error (RMSE) which is necessary for determining the capability of GRNN and it was figured out by the trial and error approach. At α=0.10, the value of RMSE is very low, which was exposed in figure 9. The optimum value of α is more responsible for the workability of GRNN. Figures 10 and 11 depict the performance of training and testing dataset of GRNN model.

15

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Figure 9. Effect of α on the RMSE

Figure 10. Performance of Training dataset of MPMR

Figures 10 and 11 exposed their capability through its R value, which is near to unity in both the training and testing dataset. MPMR had proved its efficiency in predicting the elastic modulus of jointed rock mass. A comparative study has been framed between the developed models of ELM, GRNN and MPMR based on value of Coefficient of Corelation (R) value. Figure 12 shows the comparative chart between the developed models. 16

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Figure 11. Performance of Testing dataset of MPMR

Figure 12. Comparative performances of ELM, GRNN and MPMR

17

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Figure 12 elaborates the efficiency of the adopted models, in which the GRNN and MPMR has the better capability in determining the elastic modulus of jointed rock mass than ELM. In order to assess the performance of the predicting techniques various scientists and researchers have utilized number of statistical factors (Gokceoglu, 2002; Gokceoglu & Zorlu, 2004; Nayak et al., 2005; Wang et al., 2009; Ceryan et al., 2013; Yagiz et al., 2012; Moriasi et al., (2007) Chen et al., 2012; Srinivasulu & Jain, 2006; Vinay et al., 2015; Ceryan, 2014) The statistical parameters include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Coefficient of Correlation (R), Coefficient of Efficiency (E), root mean square error to observation’s standard deviation ratio (RSR), Normalized Mean Bias Error (NMBE), Variance Account Factor (VAF), Maximum Determination Coefficient value (R2), Adjusted determination coefficient (Adj.R2), Performance Index (PI), Weighted Mean Absolute Percentage Error (WMAPE). RMSE evaluates the residual between desired and output data, a lower value of RMSE indicates good prediction performance of the model. But RMSE gives more weightage to large errors (Kisi et al., 2013). MAPE is a dimensionless statistics that provides an effective way of comparing the residual error for each data point with respect to the observed or target value. Smaller values of MAPE indicate better performance of the model and vice versa. WMAPE measures the weighted mean absolute percentage error of the prediction. RSR incorporates the benefits of error index statistics and includes a scaling/ normalization factor, so that the resulting statistic and reported values can apply to various constituents. The optimal value of RSR is zero. Hence a lower value of RSR indicates good prediction. NMBE measures the ability of the model to predict a value which is situated away from the mean value. A positive NMBE indicates over-prediction and a negative. NMBE indicates under-prediction of the modelVAF represents the ratio of the error variance to the measured data variance. R2 and adj. R2 evaluate the linear regression relationship between the desired and output data, while E evaluates the capability of the model at simulating output data from the mean statistics. For a statistical model, in theory, the VAF, RMSE and R2 are 100% for VAF, 0 for RMSE and 1 for R2 (Yagiz et al., 2012). In reality, performance indices, such as VAF, RMSE and R2, can be separately used to examine the model accuracy. However, none of these indices is superior (Yagiz et al., 2012). As a result, the performance index (PI) suggested by Yagizet al. (2012) was used examine the accuracy of the statistical models in this study. Unlike the study performed by Yagiz et al. (2012), Adj. R2 was used instead of R2 for the PI. The following are the equations for determining the above mentioned statistical parameters: RMSE =

RSR =

18

2 1 N ∑ Ti − Pi N i=1

(

)

RMSE 2  _   1 N  ∑ T − T i   N i = 1  i  



(24)

(25)

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

MAPE (%) =

1 N Ti − Pi × 100 ∑ N i=1 Ti

(26)

N 2 ∑ Ti − Pi E = 1 − i=1 _ 2 N   ∑ Ti − Ti    i=1 

(

)

(27)

1 N ∑ P − Ti N i=1 i × 100 NMBE (%) = 1 N ∑T N i=1 i

(28)

N Ti − Pi ∑ i=1 Ti WMAPE = N ∑T i=1 i

(29)

(

( ( (

)

)

VAF = 1 − var Ti − Pi / var Ti

Adj .R2 = 1 −

)) * 100

(30)

(n − 1)

1 − R2 ) ( (n − p − 1)

(31)

PI = Adj .R2 + 0.01VAF − RMSE

(32) _

_

Where Ti and Pi are the observed and the predicted values, Ti and Pi is the mean of observed and predicted values, var is the variance, n denotes the number of training and testing samples and p denotes the model input quantity. The above mentioned statistical parameters were calculated and compared in the table 1.

19

 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Table 1. Comparison of the developed models based on various Statistical Criteria’s Statistical Criteria’s

GRNN Training

ELM Testing

Training

MPMR Testing

Training

Testing

RMSE

0.0289

0.0258

0.0659

0.0448

0.4491

0.0264

MAPE (%)

60.6177

114.2007

360.9865

253.9366

39.3481

74.9301

E

0.9760

0.9351

0.8751

2.4097

-4.7957

0.9323

RSR

0.0058

0.2547

0.0131

0.4411

0.0893

0.0151

NMBE (%)

-0.5409

9.8648

3.7419

16.0591

0.0288

11.1739

WMAPE

0.1131

0.2847

0.3270

0.4885

0.1208

0.2956

VAF

97.5965

93.8313

87.5817

81.5153

97.3005

93.6505

0.9742

0.9390

0.8649

0.8100

0.8100

0.9370

Adjusted R

0.9740

0.9375

0.8642

0.8054

0.8089

0.9355

PI

1.9211

1.8499

1.6740

1.5758

1.3329

1.8456

R

2 2

SOLUTIONS AND RECOMMENDATION It depicts the path for easy and optimal value on elastic modulus of rock mass. With any number of inputs, the predicted equation will provide the outcomes directly without any conditions.

FUTURE RESEARCH DIRECTION The differences in the dataset ranges tends to the inappropriate outputs. This inadequacy can be resolved by the researchers in the future. Various datasets can be gathered and various technical models can be adopted for foretelling the elastic modulus of rock mass. It is also suggested for the scientists and researchers to adopt various techniques for determining the various crucial and sensitive geomechanical properties of rocks.

CONCLUSION This chapter adopts various intelligent model techniques like ELM, GRNN and MPMR for forecasting the elastic modulus of jointed rock mass, Em. ELM adopts no tuning parameter, whereas GRNN utilized only one tuning parameter and MPMR adopts two tuning parameters. These developed models provide their performance and justified their efficiency in predicting the Em, specifically GRNN and MPMR.

REFERENCES Anghel, C. I., & Ozunu, A. (2006). Prediction of gaseous emissions from industrial stacks using an artificial intelligence method. Chemical Papers, 60(6), 410–415. doi:10.2478/s11696-006-0075-z

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 Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass

Arora, V. K. (1987). Strength and deformation behavior of jointed rocks (Ph.D. Thesis). Indian Institute of Technology, Delhi, India. Barton, N. R., Lien, R., & Lunde, J. (1974). Engineering classification of rock masses for the design of tunnel supports. Rock Mechanics, 6(4), 189–236. doi:10.1007/BF01239496 Bieniawski, Z. T. (1973). Engineering classification of jointed rock masses. Transaction of South African Institution of Civil Engineers, 15(12), 335-344. Bieniawski, Z. T. (1978). Determining rock mass deformability, experience from case histories. International Journal of Rock Mechanics and Mining Sciences, 15(5), 237–247. doi:10.1016/0148-9062(78)90956-7 Brown, E. T., & Trollope, D. H. (1970). Strength of model of jointed rock. Journal of the Soil Mechanics and Foundations Division, 96(SM2), 685–704. Ceryan, N., Okkan, U., & Kesimal, A. (2013). Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ. Earth Sci., 68(3), 807–819. doi:10.1007/s12665-012-1783-z Chen, H., Xu, C., & Guo, S. (2012). Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology (Amsterdam), 434–435, 36–45. doi:10.1016/j.jhydrol.2012.02.040 Chen, L., Dong, M., & Zheng, Q. J. (2014). Evaluation Research of Urban Distribution Systems Resource Integration based on Generalized Regression Neural Network. International Journal of Multimedia and Ubiquitous Engineering, 9(4), 21–30. doi:10.14257/ijmue.2014.9.4.03 Ebadi, M., Karimi Nasab, S., & Jalalifar, H. (2011). Estimating the deformation modulus of jointed rock mass under multilateral loading condition using analytical methods. Journal of Mining & Environment, 2(2), 146–156. Einstein, H. H., & Hirscfeld, R. C. (1973). Models studies in mechanics of jointed rocks. Journal of the Soil Mechanics and Foundations Division, 99, 229–248. Frénay, B., & Verleysen, M. (2010). Using SVMs with randomized feature spaces: an extreme learning approach. Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) (pp. 315–320). Frénay, B., & Verleysen, M. (2011). Parameter-insensitive kernel in extreme learning for nonlinear support vector regression. Neurocomputing, 74(16), 2526–2531. doi:10.1016/j.neucom.2010.11.037 Gokceoglu, C. (2002). A fuzzy triangular chart to predict the uniaxial compressive strength of the agglomerates from their petrographic composition. Engineering Geology, 66(1–2), 39–51. doi:10.1016/ S0013-7952(02)00023-6 Gokceoglu, C., & Zorlu, K. (2004). A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Engineering Applications of Artificial Intelligence, 17(1), 61–72. doi:10.1016/j.engappai.2003.11.006

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Man, Z. H., Lee, K., Wang, D. H., Cao, Z. W., & Miao, C. Y. (2011). A new robust training algorithm for a class of single-hidden layer feedforward neural networks. Neurocomputing, 74(16), 2491–2501. doi:10.1016/j.neucom.2010.11.033 Man, Z. H., Lee, K., Wang, D. H., Cao, Z. W., & Miao, C. Y. (2011). A modified ELM algorithm for single-hidden layer feedforward neural networks with linear nodes. Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications (pp. 2524–2529). doi:10.1109/ICIEA.2011.5976017 Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., & Lendasse, A. (2010). OP-ELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks, 21(1), 158–162. doi:10.1109/ TNN.2009.2036259 PMID:20007026 Miche, Y., Sorjamaa, A., Bas, P., Simula, O., & Lendasse, A. (2011). TROP-ELM: A double regularized ELM using LARS and Tikhonov regularization. Neurocomputing, 74(16), 2413–2421. doi:10.1016/j. neucom.2010.12.042 Mohammed, A. A., Minhas, R., Jonathan, Q. M. W., & Sid-Ahmed, M. A. (2011). Human face recognition based on multidimensional PCA and extreme learning machine. Pattern Recognition, 44(10–11), 2588–2597. doi:10.1016/j.patcog.2011.03.013 Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. doi:10.13031/2013.23153 Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models Part I – A discussion of principles. Journal of Hydrology (Amsterdam), 10(3), 282–290. doi:10.1016/0022-1694(70)90255-6 Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2005). Short-term flood forecasting with a model. Water Resources Research, 41(4), W04004. doi:10.1029/2004WR003562 Nian, R., He, B., & Lendasse, A. (2012). 3D object recognition based on a geometrical topology model and extreme learning machine. Extreme learning machine’s: Theory and application. Neural Computing & Applications, 22(3–4), 427–433. Nurcihan, C. (2014). Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks. Journal of African Earth Sciences, 100, 634–644. doi:10.1016/j.jafrearsci.2014.08.006 Roy, N. (1993). Engineering behavior of rock masses through study of jointed models (Ph.D. Thesis). Indian Institute of Technology, Delhi, India. Schölkopf, B., Platt, J., Shawe, J. T., Smola, A., & Williamson, R. (2001, July). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. doi:10.1162/089976601750264965 PMID:11440593 Serre, D. (2002). Matrices: Theory and applications. New York: Springer-Verlag. Smola, A., & Scholkopf, B. (1998). A tutorial on support vector regression. Technical Report NC2TR-1998-030, Royal Holloway College. South African Institution of Civil Engineers, 15(12), 335-344.

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ADDITIONAL READING Ahmed, M. A. H., Mustafa, M. W., Ibrahim, F. A. F., & Ibrahim, A. A. (2011). Transient stability evaluation of electrical power system using generalized regression neural networks. Applied Soft Computing, 11(4), 3558–3570. doi:10.1016/j.asoc.2011.01.028 Alexandros, I., Anastasios, T., & Ioannis, P. (2014). Regularized extreme learning machine for multiview semi-supervised action recognition. Neurocomputing, 145(5), 250–262.

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Alisson, S. C. A., Ajalmar, R. R. N., & João, P. P. G. (2016). A new pruning method for extreme learning machines via genetic algorithms. Applied Soft Computing, 44, 101–107. doi:10.1016/j.asoc.2016.03.019 Averbakh, I. (2000, September). Minmax regret solutions for minimax optimization problems with uncertainty. Operations Research Letters, 27(2), 57–65. doi:10.1016/S0167-6377(00)00025-0 Aybar, A. R., Fernández, S. J., Cornejo, L. B., Mateo, C. C., Sanz, J. J., Salvador, G., & Salcedo, S. S. (2016). A novel Grouping Genetic Algorithm–Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs. Solar Energy, 132, 129–142. doi:10.1016/j. solener.2016.03.015 Behnaz, N., Jafar, H., Kasra, M., Shahaboddin, S., & Othman, S. A. R. (2016). Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature. Computers and Electronics in Agriculture, 124, 150–160. doi:10.1016/j.compag.2016.03.025 Bilal, M., & Zhiping, L. (2016). Meta-cognitive online sequential extreme learning machine for imbalanced and concept-drifting data classification. Neural Networks, 80, 79–94. doi:10.1016/j.neunet.2016.04.008 PMID:27187873 Byungwhan, K., Duk, W. L., Kyung, Y. P., Serk, R. C., & Seongjin, C. (2004). Prediction of plasma etching using a randomized generalized regression neural network. Vacuum, 76(1), 37–43. doi:10.1016/j. vacuum.2004.05.018 Byungwhan, K., Minji, K., & Sang, H. K. (2009). Modeling of plasma process data using a multiparameterized generalized regression neural network. Microelectronic Engineering, 86(1), 63–67. doi:10.1016/j.mee.2008.09.015 Byungwhan, K., Sanghee, K., & Dong, H. K. (2010). Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm. Expert Systems with Applications, 37(1), 182–186. doi:10.1016/j.eswa.2009.05.007 Cao, J., Lin, Z., & Huang, G. B. (2010). Composite function wavelet neural networks with extreme learning machine. Neurocomputing, 73(7–9), 1405–1416. doi:10.1016/j.neucom.2009.12.007 Cao, J., Lin, Z., & Huang, G. B. (2012). Self-adaptive evolutionary extreme learning machine. Neural Processing Letters, 36(3), 285–305. doi:10.1007/s11063-012-9236-y Cao, J., Lin, Z., Huang, G. B., & Liu, N. (2012). Voting based extreme learning machine. Information Sciences, 185(1), 66–77. doi:10.1016/j.ins.2011.09.015 Ertugrul, O. F. (2016). Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems, 78, 429–435. doi:10.1016/j. ijepes.2015.12.006 Gaurav, K., & Hasmat, M. (2016). Generalized Regression Neural Network Based Wind Speed Prediction Model for Western Region of India. Procedia Computer Science, 93, 26–32. doi:10.1016/j. procs.2016.07.177 Guang, B. H., & Lei, C. (2007). Convex incremental extreme learning machine. Neurocomputing, 70(16–18), 3056–3062.

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Guoren, W., Zhao, Y., & Wang, D. (2008). A protein secondary structure prediction framework based on the Extreme Learning Machine. Neurocomputing, 72(1–3), 262–268. Hai, J. R., Yew, S. O., Hwee, A. T., & Zexuan, Z. (2008). A fast pruned-extreme learning machine for classification problem. Neurocomputing, 72(1–3), 359–366. Haichen, Y., Cheng, S., & Desheng, Z. (2013). The design and simulation of networked control systems with online extreme learning machine PID. International Journal of Modelling, Identification and Control, 20(4). Hilmi, B. C. (2006). Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modeling. Mathematical and Computer Modelling, 44(7–8), 640–658. Hong, L., Sen, G., Chun, L., & Jing, S. (2013). A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm. Knowledge-Based Systems, 37, 378–387. doi:10.1016/j.knosys.2012.08.015 Huang, G., Song, S., & You, K. (2015). Trends in extreme learning machines: A review. Neural Networks, 61, 32–48. doi:10.1016/j.neunet.2014.10.001 PMID:25462632 Huang, G. B., Li, M. B., Chen, L., & Siew, C. K. (2008). Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing, 71(4–6), 576–583. doi:10.1016/j.neucom.2007.07.025 Huang, G. B., Zhu, Q. Y., & Siew, C. K. (2006). Extreme learning machine: Theory and applications. Neurocomputing, 70(1–3), 489–501. doi:10.1016/j.neucom.2005.12.126 Irrine, B. S., Ardyono, P., Asrarul, O. Q., Soeprijanto, A., & Yorino, N. (2016). Critical Clearing Time prediction within various loads for transient stability assessment by means of the Extreme Learning Machine method. International Journal of Electrical Power & Energy Systems, 77, 345–352. doi:10.1016/j. ijepes.2015.11.034 Jelena, P., Svetlana, I., Zorica, D., Milica, J., & Owen, I. C. (2007). An investigation into the usefulness of generalized regression neural network analysis in the development of level A in vitro–in vivo correlation. European Journal of Pharmaceutical Sciences, 30(3–4), 264–272. PMID:17188851 Jiuwen, C., & Lianglin, X. (2014). Protein Sequence Classification with Improved Extreme Learning Machine Algorithms. BioMed Research International, 12. Johnny, K. C. N., Yuzhuo, Z., & Shiqiang, Y. (2007). A comparative study of Minimax Probability Machine-based approaches for face recognition. Pattern Recognition Letters, 28(15), 1995–2002. doi:10.1016/j.patrec.2007.05.021 Kaizhu, H., Haiqin, Y., Irwin, K., Michael, R. L., & Laiwan, C. (2004). The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research, 5, 1253–1286. Kaizhu, H., Haiqin, Y., King, I., & Lyu, M. R. (2004). Learning classifiers from imbalanced data based on biased minimax probability machine Computer Vision and Pattern Recognition. Proceedings of the 2004 IEEE Computer Society Conference.

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Lahouari, G., Tarek, R. S., & Khaled, S. A. (2013). Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines. Procedia Computer Science, 19, 305–312. doi:10.1016/j.procs.2013.06.043 Liming, Y., & Siyun, Z. (2016). A sparse extreme learning machine framework by continuous optimization algorithms and its application in pattern recognition. Engineering Applications of Artificial Intelligence, 53, 176–189. doi:10.1016/j.engappai.2016.04.003 Mahesh, P. (2009). Extreme‐learning‐machine‐based land cover classification. International Journal of Remote Sensing, 30(14). Mahesh, P., Aaron, E. M., & Timothy, A. W. (2013). Kernel-based extreme learning machine for remotesensing image classification. Remote Sensing Letters, 4(9), 853–862. doi:10.1080/2150704X.2013.805279 Mahmood, A., Hamed, D., Alireza, S., & Sylvain, C. (2010). Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks. Neural Networks, 23(7), 892–904. doi:10.1016/j.neunet.2010.05.005 PMID:20627454 Manuel, A. F. G., Antonio, M. G. C., & Federico, G. V. (2016). Corporate reputation and market value: Evidence with generalized regression neural networks. Expert Systems with Applications, 46(15), 69–76. Marina, T. (2011). Disaggregation & aggregation of time series components: A hybrid forecasting approach using generalized regression neural networks and the theta method. Neurocomputing, 74(6), 896–905. doi:10.1016/j.neucom.2010.10.013 Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., & Lendasse, A. (2010). OP-ELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks, 21(1), 158–162. doi:10.1109/ TNN.2009.2036259 PMID:20007026 Min, H., Xue, Y., & Enda, J. (2016). An Extreme Learning Machine based on Cellular Automata of edge detection for remote sensing images. Neurocomputing, 198(19), 27–34. Ming, B. L., Guang, B. H., Saratchandran, P., & Sundararajan, N. (2005). Fully complex extreme learning machine. Neurocomputing, 68, 306–314. doi:10.1016/j.neucom.2005.03.002 Mozaffari, A., Bandpy, M. G., Samadian, P., Rastgar, R., & Kolaei, A. R. (2013). Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network. Swarm and Evolutionary Computation, 9, 90–103. doi:10.1016/j. swevo.2012.11.004 Nouredine, D., Jalal, F., Foudel, B., Kamel, B., Said, A., & Mohammed, F. (2014). Seismic noise filtering based on Generalized Regression Neural Networks. Computers & Geosciences, 69, 1–9. doi:10.1016/j. cageo.2014.04.007 Ozturk, A. U., & Mustafa, E. T. (2012). Prediction of effects of microstructural phases using generalized regression neural network. Construction & Building Materials, 29, 279–283. doi:10.1016/j.conbuildmat.2011.10.015 Ozyildirim, B. M., & Mutlu, A. (2016). One pass learning for generalized classifier neural network. Neural Networks, 73, 70–76. doi:10.1016/j.neunet.2015.10.008 PMID:26555854

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Ping, J., & Jiejie, C. (2016). Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing, 198(19), 40–47. Pingzhou, T., Chen, D., & Yushuo, H. (2016). Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting. Chaos, Solitons, and Fractals, 89, 243–248. doi:10.1016/j.chaos.2015.11.008 Punyaphol, H., Sirapat, C., & Khamron, S. (2013). Robust extreme learning machine. Neurocomputing, 102(15), 31–44. Qin, Y. Z., Qin, A. K., Suganthan, P. N., & Guang, B. H. (2005). Evolutionary extreme learning machine. Pattern Recognition, 38(10), 1759–1763. doi:10.1016/j.patcog.2005.03.028 Sachnev, V., & Kim, H. J. (2014). Parkinson disease classification based on binary coded genetic algorithm and extreme learning machine. Proceedings of the IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (pp.1–6), Singapore. doi:10.1109/ ISSNIP.2014.6827649 Shahab, K., Shahaboddin, S., Shervin, M., Roslan, H., & Chandrabhusha, R. (2016). A systematic extreme learning machine approach to analyze visitors’ thermal comfort at a public urban space. Renewable & Sustainable Energy Reviews, 58, 751–760. doi:10.1016/j.rser.2015.12.321 Shahin, S., Shahaboddin, S., Meysam, A., Yee, P. L., Zulkefli, M., Manaf, A. A., & Mostafaeipour, A. et al. (2016). Extreme learning machine for prediction of heat load in district heating systems. Energy and Building, 122(15), 222–227. Song, L., Lalit, G., & Peng, W. (2016). An ensemble approach for short-term load forecasting by extreme learning machine. Applied Energy, 170(15), 22–29. Sun, Z. L., Choi, T. M., Au, K. F., & Yu, Y. (2008). Sales forecasting using extreme learning machine with applications in fashion retailing. Decision Support Systems, 46(1), 411–419. doi:10.1016/j.dss.2008.07.009 Trieu, P. L., Low, K. H., Xingda, Q., Lim, H. B., & Hoon, K. H. (2014). An individual-specific gait pattern prediction model based on generalized regression neural networks. Gait & Posture, 39(1), 443–448. doi:10.1016/j.gaitpost.2013.08.028 PMID:24071020 Wong, W. K., & Guo, Z. X. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm. International. Xian, S., Jian, W., Gang, L., Liu, Y., Xinmin, G., & Shu, J. (2016). Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs. Journal of Natural Gas Science and Engineering, 33, 687–702. doi:10.1016/j.jngse.2016.05.060 Xiang, P., & Irwin, K. (2009). A biased minimax probability machine-based scheme for relevance feedback in image retrieval. Neurocomputing, 72(7–9), 2046–2051. Xiaojun, W., Mingshuang, Y., Zhizhong, M., & Ping, Y. (2016). Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace. Advanced Engineering Informatics, 30(3), 368–375. doi:10.1016/j.aei.2016.05.001

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Xin, M., Haibo, W., Bingxia, X., Mingang, Z., Bing, J., & Yibin, L. (2014). Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine. IEEE Journal of Biomedical and Health Informatics, 18(6). Yan, L. H., Yuan, X., & Qun, X. Z. (2016). Soft-sensing model development using PLSR-based dynamic extreme learning machine with an enhanced hidden layer. Chemometrics and Intelligent Laboratory Systems, 154(15), 101–111. Yılmaz, K., Lokman, K., Ramazan, T., & Ertuğrul, Ö. F. (2014). Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. Journal of Experimental & Theoretical Artificial Intelligence, 26(2), 267–281. doi:10.1080/0952813X.2013.861875 Yoshiyama, K., & Sakurai, A. (2014). Laplacian minimax probability machine. Pattern Recognition Letters, 37(1), 192–200. doi:10.1016/j.patrec.2013.01.004 Yuan, L., Yeng, C. S., & Huang, G. B. (2009). Ensemble of online sequential extreme learning machine. Neurocomputing, 72(13–15), 3391–3395. Yuan, T. Y., Yan, P. Z., Jie, C., & Zhang, Y. W. (2016). Incomplete data classification with voting based extreme learning machine. Neurocomputing, 193(12), 167–175. doi:10.1016/j.neucom.2016.01.068 Zhang, R., Lan, Y., Huang, G. B., Xu, Z. B., & Soh, Y. C. (2013). Dynamic extreme learning machine and its approximation capability. IEEE Transactions on Cybernetics, 43(6), 2054–2065. doi:10.1109/ TCYB.2013.2239987 PMID:23757515 Zhao, L. J., Diao, X. K., Yuan, D. C., & Tang, W. (2011). Enhanced classification based on probabilistic extreme learning machine in wastewater treatment process. Procedia Engineering, 15, 5563–5567. doi:10.1016/j.proeng.2011.08.1032 Zhao, Y., & Qingshan, L. (2012). Generalized recurrent neural network for ϵ-insensitive support vector regression. Mathematics and Computers in Simulation, 86, 2–9. doi:10.1016/j.matcom.2012.03.013 Zhao, Y. P., & Huerta, R. (2016). Improvements on parsimonious extreme learning machine using recursive orthogonal least squares. Neurocomputing, 191(26), 82–94. doi:10.1016/j.neucom.2016.01.005 Zhifei, S., & Meng, J. E. (2016). Efficient Leave-One-Out Cross-Validation-based Regularized Extreme Learning Machine. Neurocomputing, 194(19), 260–270.

KEY TERMS AND DEFINITIONS Confining Pressure: Confining Pressure is defined as the stress or pressure forced on a layer of soil or rock by the heaviness of the overlying substance. Elastic Modulus: Elastic Modulus (Ej) or Modulus of elasticity is the material property which describes its stiffness. It is defined as the ratio of the stress applied to a body or a material to the resulting strain within the elastic limit.

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Extreme Learning Machine: Extreme Learning Machine (ELM) is a feed forward neural network utilized for the characterization and regression with a solitary layer of shrouded nodes, where the weights associating inputs to the concealed nodes that are arbitrarily allotted and never renovated. Generalized Regression Neural Network: Generalized Regression Neural Network (GRNN) falls under probabilistic neural network category, which is utilized for function approximation. Inclination: Inclination can be defined as the angle between two lines or two planes. It is the deviation from the normal position especially horizontally or vertically. Minimax Probability Machine Regression: Minimax Probability Machine Regression (MPMR) is defined as the process of maximizing the minimum probability of regression model for all possible distribution with known mean and covariance matrix. Prediction: Prediction is defined as the process of forecasting something. Roughness: Surface roughness is a component of surface texture. It is the deviation in the direction of the normal vector of a real surface from its ideal form. Rock surface roughness has proved difficult to quantify.

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Chapter 2

Prediction of The Uniaxial Compressive Strength of Rocks Materials Nurcihan Ceryan Balikesir University, Turkey Nuray Korkmaz Can Istanbul University, Turkey

ABSTRACT This study briefly will review determining UCS including direct and indirect methods including regression model soft computing techniques such as fuzzy interface system (FIS), artifical neural network (ANN) and least sqeares support vector machine (LS-SVM). These has advantages and disadvantages of these methods were discussed in term predicting UCS of rock material. In addition, the applicability and capability of non-linear regression, FIS, ANN and LS-SVM SVM models for predicting the UCS of the magnatic rocks from east Pondite, NE Turkey were examined. In these soft computing methods, porosity and P-durability secon index defined based on P-wave velocity and slake durability were used as input parameters. According to results of the study, the performanc of LS-SVM models is the best among these soft computing methods suggested in this study.

1. INTRODUCTION The uniaxial compressive strength (UCS) of intact rocks is important and pertinent properties for characterizing rock mass and it is also one of the most widely used parameter in geological, geotechnical, geophysical and petroleum engineering project. UCS can be measured directly in laboratory experiments or estimated indirectly. UCS test requires high quality core samples of regular geometry. According to the ISRM (2007), the uniaxial compressive strength (UCS) test is conducted on cylindrical specimens under dry and saturated conditions. The laboratory tests are expensive, complicated and time consuming. These tests need expensive sophisticated laboratory equipment. Furthermore, UCS test and deformation test require high-quality core samples with regular geometry. Standard cores cannot always be extracted DOI: 10.4018/978-1-5225-2709-1.ch002

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 Prediction of The Uniaxial Compressive Strength of Rocks Materials

from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. In addition, a careful execution of this test is difficult, time-consuming, and expensive, as well as involving destructive tests (Gokceoglu and Zorlu 2004, Ceryan 2014). To overcome these difficulties, various predictive models based on index tests, including mineralogical-petrographic analyses, physical properties, an elastic wave velocity test and basic mechanical tests have been developed by many researchers (Yilmaz 2009;Heidari et al 2010, Zhang et al 2012; Ceryan et al. 2012; Mishra and Basu 2012; Yesiloglu-Gultekin et al. 2013a; Mishra and Basu 2013; Nefeslioglu 2013, Kumar et al 2013). Another methods to estimate UCS of rock materials is to use existing tables and diagrams This study will briefly review direct and indirect methods to determine UCS of rock materials, and the development of prediction methods which are regression analysis and soft computing techniques in estimating of UCS of rock materials. Moreover, the applicability and capability of none-linear regression and soft computing methods including fuzzy interface system (FIS), artificial neural network (ANN) and least squares support vector machine (LS-SVM) for predicting the uniaxial compressive strength of the magmatic rock from NE Turkey was examined. Considering that the rock materials consist of a solid and porous portion, intrinsic properties that affect the UCS of rock materials can be divided into two groups; one is pore characteristics, and the second is microstructural variables, consisting of the mineralogical composition and rock texture. These cases were considered using soft-computing models to estimate the UCS of the volcanic rock materials herein. To represent the mineralogical composition and rock texture, the P-durability second index, a new engineering index, derived from the P-wave velocity in the solid portion of the rock materials, and the slake durability index were developed. Furthermore, the porosity was used as a characteristic of the porous rock material. The performance index (PI), Nash-Sutcliffe coefficient (NS) and weighted mean absolute percentage error (WMAPE) were used to determine the accuracy of the SVM, RVM, and ANN models developed.

2. BACKGROUND Uniaxial compressive strength (UCS) of rock mass is very important parameter for the design of rock structures and can be measured directly and indirectly in laboratory. The petro-chemical, mineralogical and textural characteristics of rocks significantly affect their mechanical behavior, including UC. These properties were widely used to estimate the UCS (Shakoor and Bonelli 1991; Ulusay et al. 1994; Tugrul and Zarif 1999; Singh et al. 2001; Gokceoglu 2002; Hale and Shakoor 2003; Jeng et al. 2004; Ceryan 2008, Zorlu et al. 2008; Ceryan et al. 2008a; Sabatakakis et al 2008; Török and Vásárhelyi 2010; Yesiloglu-Gultekin et al. 2012a; Ceryan 2012, Yesiloglu-Gultekin et al. 2013b; Tandon and Gupta 2013, Ceryan 2015a). Eliminating the real effects of rock fabric parameters on the mechanical properties of rocks by applying complex fabric coefficients inhibits their practical use in petrography applied to geomechanics. (Přikryl 2006); these conditions are valid for the aforementioned indices developed for specific rocks. Simple indices, such as slake-durability, elastic wave velocity and physical properties, for rock materials are indicative of petrographic features, including the mineralogical composition, fabric and weathering state. Therefore, these indices are widely used to estimate UCS of rock material (Kahraman 2001; Karakus et al. 2005; Yılmaz and Yuksek 2009; Ceryan et al. 2008 a-b, Ceryan et al. 2012; Ceryan et al. 2013a). Weathering indices are commonly used to estimate UCS of weathered rocks (Ceryan et al 2008 a-b, Ceryan 2008, Ceryan 2015a).

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 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Index and basic mechanical tests are easy to perform. Sophisticated instruments are not required for index tests; they can be easily performed in the field (Kumar et al.2013). These tests and analyses require smaller samples than the samples needed to determine the UCS directly, and they are faster and more economical (Ulusay et al 1994, Singh et al. 2001, Gokceoglu 2002, Tiryaki 2008, Ceryan 2014). However, the index tests always include a certain level of uncertainty. One study observed no consistency between the equations suggested by these methods (Fener et al. 2005). Physical properties, especially porosity, effective porosity, water absorption and density, are widely used to characterize various physico-mechanical parameters such as UCS and weathering grades of rock materials (Aydin and Duzgoren-Aydin, 2002, Ceryan 2008, Ceryan et al. 2008a, Ceryan 2014). Pore characteristics is an important physical property that aids in governing physical attributes of rocks, such as strength, deformability and hydraulic conductivity (Tugrul 2004). Porosity was used to define the pore characteristics of rock materials by Ceryan (2014). Porosity is often used in the models developed to estimate UCS. (e.g. Dunn et al. 1973; Vernik et al., 1993; Schultz and Li, 1995; Tugrul and Gurpinar, 1997; Gupta and Rao, 1998; Al-Harthi et al., 1999; Palchik, 1999; Tugrul and Zarif, 1999; Leite and Ferland 2001, Chatterjee and Mukhopadhyay, 2002; Palchik and Hatzor, 2004, Kahraman et al., 2005; Basu, 2006, Dehghan et al. 2010; Rajabzadehet al. 2012). These studies demonstrated that a negative linear or curvilinear relationship exists between UCS and porosity of rock materials. Water content is another physical parameter used to estimate UCS. In the literature, the dependences of mechanical properties on the moisture content have been thoroughly characterized for various types or rocks (Hawkins and McConnell 1992, West 1994; Sun and Hu 1997; Vasarhelyi 2005; Grgic et al. 2005;Vasarhelyi and Van 2006; Talesnick and Shehadeh 2007,Gorgulu et al. 2008; Yilmaz 2010; Torok and Vasarhelyi (2010) Tonnizam Mohamad et al. 2011; Li et al. 2012; Shukla et al. 2013, Celik et. al. 2014, Cherblanc et al. 2016, Shi et al. 2016). When plotting the variation of tensile or compressive mechanical strength as a function of the water content, it exhibits a sharp decrease for low water content and remains almost constant for larger water content (from Cherblanc et al. 2016). This is especially valid for clayey rocks and weathered rocks. A nondestructive method such as the ultrasonic test offers an alternative to determine strength and deformation properties of rocks including UCS and E with relative ease and at low operational cost. During elastic wave velocity tests, the samples are not disturbed because it employs low-amplitude waves producing stresses well below the yield stress of most materials (Green, 1991, Winkler 1997) and hence, the test can produce many results using one sample. The modulation of elastic wave waves by microstructural variables (including mineralogy, shape and size of grains, density and orientation of pores/cracks) is reflected in the wave velocity, and therefore, it is possible to characterize rock materials by the velocity measurements (Basu and Aydin, 2006b, Ceryan 2014). Shear waves (S-wave) reflect the instinct and physico-mechanical properties of the rock materials better than the P wave. But it is difficult to obtain S waves. For this reason, the P waves are commonly used to evaluate the physical and mechanical properties of the rocks materials. There are good relationships between the elastic wave velocity and chemical and mineralogical composition of the rocks (Buntebarth 1982; Rybach and Buntebarth 1982, 1984; Hodder 1984; Kern and Siegesmund 1989; Ceryan and Sen 2003, Ceryan et al 2008b). For this reason, P-wave velocity is widely used for predicting UCS (e.g., Kahraman 2001, (Basu, 2006; Chary et al. 2006; Vasconcelos et al. 2007; Sharma and Singh 2008; Vasconcelos et al. 2008; Ceryan et al. 2008a; Moradian and Behnia, 2009; Khandelwal and Singh 2009, Moradian and Behnia 2009; Yagiz 2011; Jabbar 2011, Kurtulus et al. 2012; Yurdakul and Akdas 2013, Khandelwal 2013, Minaeian and Ahangari 2013, Tonnizam Mohamad et al 2015, Jahed Armaghani et al 2014, 2016a, Kainthola et al. 2015, Selcuk 33

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

and Nar 2016) and used for defining the weathering grades, predicting the engineering properties of the weathered rocks and weatherability of rocks (Ceryan and Sen 2003, Ceryan et al. 2008a-b). However, most of these studies present certain limitations in using the elastic wave velocity to predict mechanical properties of rock materials (Martínez-Martínez et al. 2011, Tandon and Gupta 2013). Martínez-Martínez et al. (2011) reported that the influence of rock fabric properties, including crystal size, fracture properties, and porosity, on ultrasonic propagation wave velocity is usually unsatisfactory (Martínez-Martínez et al. 2011). However, many studies demonstrate a good relationship between the P-wave and mineralogical composition of rocks (Buntebarth 1982; Rybach and Buntebarth 1982, 1984; Hodder 1984; Kern and Siegesmund 1989; Ceryan and Sen 2003, Ceryan et al. 2008a). Considering the result of the previous studies in recent years, P-wave velocity in the rock materials which would have lacked pores and fissures are used instead of P-wave velocity in the whole rocks (Ceryan et al 2008b, Ceryan 2014). Ceryan et al (2008b) used P-wave velocity in the rock materials which would have lacked pores and fissures to define the mineralogical and physical change parameters. The parameters were used in estimating the mechanical properties of weathered rocks by these authors. Ceryan (2014) suggested a new engineering index, P-durability index used to characterize the influence of the microstructural variables on the UCS of rock materials. The index was defined as P-wave velocity in rock samples without pores and fissures (i.e., the P-wave in the solid portion) multiply the Id is the slake durability index (Eq. 1) Vid = Vm.Id

(1)

where Vid is P-wave durability, Vm is the P-wave velocity in rock samples without pores and fissures (i.e., the P-wave in the solid portion) and the Id is the slake durability index. The slake durability of a rock is an important property and is closely related to its mineralogical composition. Hence, its resistance to degradation (weakening and disintegration) is measured using a standard drying and wetting cycle (Sharma and Singh 2008). The slake durability test is a cheap and easy test to carry out and requires very little sample preparation. It may be an indirect measure of the UCS of rock if significant correlations are derived (Kahraman et al 2016). But there are some limitations and weaknesses associated with this method (Erguler and Ulusay 2009). Some researchers have investigated the relation between UCS and slake durability index (SDI) to develop an estimation equation for UCS (e.g. Cargill and Shakoor 1990, Koncagul and Santi 1999, Gokceoglu et al. 2000, Dincer et al. 2008, Yagiz 2011, Kahraman et al. 2016). Bonelli (1989) investigated the correlation between the UCS and SDI for sandstones but did not find any correlation. SDI would be useful when a wide range of values could be obtained and this is expected to be the case for weak or highly weathered rock (Engin et al. 1999, Kahraman et al. 2016) Similarly, Cargill and Shakoor (1990) who focused on hard rocks suggested including weak rocks for future studies, since the range of slake durability values would increase (from Engin et al. 1999). A few researchers have attempted to correlate resistivity with rock properties (Kate and Sthapak 1995; Bilim et al 2002); Kahraman and Alber 2006a-b; Kahraman et al (2006), Alber and Kahraman (2009), Kahraman and Yeken, 2010; Slatalla et al 2010, Kahraman and Alber 2014). Kate and Sthapak (1995) and found a logarithmic relation between resistivity and uniaxial compressive rock strength. According to these authors, resistivity increases with increasing compressive strength. Kahraman (2009) correlated the uniaxial compressive strength of fault breccia (Germany) having blocks weaker than the matrix with volumetric block proportion (VBP) and texture coefficient (TC) and found strong correlations between

34

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

UCS and both VBP and TC. In addition, Slatalla et al (2010) performed acoustic emission measurements during the uniaxial compression tests on Misis fault breccia. In the study performed by Kahraman and Alber 2014, a strong correlation between UCS and resistivity for the samples having VBP between 25 and 75% was found. The using existing tables and diagrams based on “simple means tests” is first approach to estimate UCS of rock materials. In simplest cases, the UCS estimation can be made according to an index test based on the method recommended by the British Standard (BS 5930, 1981) and the International Society for Rock Mechanics (ISRM) (Table 1). Relatively easy to execute field tests with an impact method or with a ‘simple means’ field test (hammer, scratching, molding, breaking by hand, etc.) lead to intact rock strength values assessment (Hack and Huisman 2002). In table 1, the estimation of UCS of rock materials is made using portable equipment (nail, knife, geological hammer) and an appropriate description is given (Briševac, et al 2016). In the literature, the most known diagram to estimate UCS was published in Millers dissertation (1965) and based on the Schmidt hardness (SRH) and unit weight of rock, regardless of rock type that is being tested (from Briševac, et al 2016). These diagrams and tables are used to assess the strength of rock materials in easily and quickly in the field work. As is known, UCS is one of the basic parameters to use in rock mass classification. The using existing tables and diagrams are useful in classification of rock mass, but the use of the UCS value estimated from the tables and diagram in engineering calculations is not appropriate. Basic mechanical tests such as Schmidt hammer, impact strength test, the point load index block punch test, cone indentor, core strangle index, nail penetration test and Equotip hardness tester are often used with empirical equations to estimate the UCS (Ceryan 2014). There are serious shortcomings, limitations and problems related to these testing methods (Yilmaz 2009, Kayabali and Selcuk 2010, Nefeslioglu 2013). Table 1. Determination of UCS by hand held accessories (from Briševac, et al 2016) Grd

UCS

UCS

Is(50)

Field Identification

Rock types

R6

Extremely strong rocks

>250

>10

Specimen can only be poll apart by a geological hammer

fresh basalt chert, diabase, granite and quartzite

RS

Very strong rocks

100-200

4-10

Specimen requires many blows of geological hammer to fracture It

amphibolite, sandstone, basalt gabbro, gneiss, granodiorite, limestone marble, rhyolite, and tuff

R4

Strong rocks

50-100

2-4

Specimen requires more than one blow by geological hammer to fracture It

limestone, marble, sandstone, and schist

R3

Medium strong rock

25-50

1-2

Cannot be scraped or peeled with a pocket knife; specimen can be fractured with a single firm blow of a geological hammer.

phyllite. schist siltstone

R2

Weak rock

S-25

-

Can be peeled by a pocket knife with difficulty; shallow indentations made by firm blow with a point of geological hammer.

chalk, rock salt claystone, marl, siltstone, schist

R1

Very weak rock

1-5

-

Crumbles under firm blows with point of geological hammer; can be peeled by pocket knife

highly weathered or altered rock, schist

RO

Extremely weak rock

0,25-1

-

Indented by thumbnail.

stiff fault gouge

(Grd: Grade, UCS - uniaxial compressive strength [MPa Is(50): Point Load strength index (MPa))

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 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Non-destructive dynamic rebound hardness tests such as the Shore Scleroscope hardness, Schmidt hammer hardness, Equotip Hardness Tests (EHT) and Hybrid Dynamic Hardness (HDH) are widely used to estimate UCS of rock materials. The Shore Scleroscope hardness was designed for using on metals. The ISRM (1981b) details a method for Shore hardness testing of rocks using model C-2. It measures the relative rebound of a diamondtipped hammer that drops freely from a fixed height onto the surface of a specimen (from Yasar and Erdogan 2004). In the literature, some relationships between UCS and Shore hardness were given (e.eg. Deere and Miller, 1966; Rabia and Brook, 1979, Atkinson 1993, Koncagül and Santi 1999; Yasar and Erdogan 200). Shore scleroscope is essentially a bench-top laboratory tool which is not convenient for field applications (Yilmaz 2013). The Schmidt hammer, developed in the late 1940s as an index apparatus for non-destructive testing of concrete (Schmidt, 1951) in situ, has been used in rock mechanics practice since the early 1960s, mainly for estimating the uniaxial compressive strength (UCS) of rock materials according to Miller (1965). The principle of the test is based on the absorption of part of the spring-released energy through plastic deformation of the rock surface, while the remaining elastic energy causes the actual rebound of the hammer (Yilmaz and Sendir 2002). A number of authors reported that it has been used for a variety of other specific applications to obtain the UCS of the rock materials (Singh et al. 1983;Shorey et al. 1984; Haramy and De Marco, 1985; O’Rourke 1989; Sachpazis,1990; Tugrul and Zarif, 1999; Katz et al., 2000; Yilmaz and Sendir (2002; Yasar and Erdogan, 2004; Fener et al., 2005; Goktan and Gunes 2005; Aydin and Basu ; 2005; Shalabi et al. 2007; Kilic and Teymen 2008; Yagiz 2009; Bruno, et al. 2013; Karaman and Kesimal, 2015; Karaman et al. 2015). But there exist certain limitations to the test concerning its application. These limitations are given following (Yilmaz and Sendir 2002); (a) very fractured, closely jointed rocks or rocks with schistosity. The test surface should be free from cracks to a depth of at least 6 cm. Additionally, the loose surface material must be removed; (b) the method is not applicable to very or extremely weak rocks; (c) nonhomogeneous rocks like conglomerates and brecciate and (d) when in the laboratory, the specimens or blocks should be well clamped in order to avoid any movement. This index is also affected by the roughness and water content of the surface, surface area where the test was conducted, rock strength, testing direction (lateral, vertical or tangential), cleavage, bedding planes and pores (Haramy and DeMarco 1985). Equotip hardness tester (EHT) is a relatively new instrument which has the potential for removing the afore-mentioned shortcomings of the Shore Scleroscope and Schmidt hammer (Yılmaz 2013). The Equotip Hardness Tester, EHT is a portable, electronic battery-operated, and non-destructive instrument which was initially developed to determine the dynamic hardness of metallic materials (Yilmaz 2013). Various versions of the ‘single impacts’ and ‘repeated impacts’ test procedures have been adopted by different authors for different applications to estimate uniaxial compressive strength (UCS) prediction using the Equotip hardness tester (EHT) are (Verwaal and Mulder (1993), Alvarez Grima and Babuska (1999), Meulenkamp and Alvarez Grima (1999), Kawasaki et al. (2001), Aoki and Matsukura (2008). Because different testing procedures of EHT yielded different UCS prediction models, some of them with very poor correlation coefficients, Yilmaz (2013) proposed the new testing methodology. The methodology involves the parameter hybrid dynamic hardness (HDH), which is a combination of the surface rebound hardness and deformation ratio (compaction ratio) of a rock material. Here, the term ‘hybrid’ is used in the sense that it incorporates some versions of both the ‘single’ and ‘repeated’ impacts recording techniques, which is given by the general expression (Eqs. 2. Yilmaz 2013):

36

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

HDH = ESHS .DR,

DR = ESHS .ESHR

(2)

Where DR is the deformation characteristics of the tested rock by compaction under the action of repeated impacts, ESHS is the average rebound hardness of the rock surface and ESHR represents the quantity of impact energy absorbed by the rock during a series of repeated impacts at a test spot. Point load index test, first suggested by Broch and Franklin (1972) is a well-known method and especially used when the core samples having a sufficient height cannot be obtained. In this test, rock specimens are broken by application of concentrated loads in points through a pair of spherically truncated, conical platens (ISRM 2007). Point load index, PLI is being still used to predict UCS and to classify rocks according to strength (e.g. D’Andrea et al. 1964; Deere and Miller 1966; Broch and Franklin 1972; Bieniawski 1975; Hassani et al. 1980; Read et al. 1980; Brook 1985; ISRM 1985; Turk and Dearman 1986; O’Rourke 1988;Turk 1989; Cargill and Shakoor 1990; Singh and Singh 1993; Chau and Wong 1996; Smith 1997; Hawkins 1998; Romana 1999; Rusnak and Mark 1999;Thuro and Plinninger 2001; Kahraman 2001; Palchik and Hatzor 2004; Fener et al. 2005; Kahraman et al. 2005; Cobanglu and Celik 2008;Kilic¸ and Teymen 2008; Karakus and Tutmez 2008; Diamantis et al. 2009; Singh et al. 2012; Heidari et al. 2012Mishra and Basu 2012, 2013; Karaman et al. 2014, Kaya and Karaman 2016). Some researchers have used the regression method to evaluate linear relationships between UCS and PLI while some researchers uncovered power relationships between related parameters (Kahraman and Gunaydin 2009, Azimian et al. 2014, Kaya and Karaman 2016). According to Yilmaz (2009), limitations and problems related to point load index test are as follows: (a) tested rocks are generally anisotropic and heterogenic, but tests are applied in very small area; (b) irregular failures (invalid test result) frequently occur and cause a requirement of too many rock specimens; (c) the specimen may move during loading; and (d) micro-fissures may cross the conical platens. It is known that no single UCS/PLI ratio is applicable to the full range of rock material strengths known, and there appear to be a broad range of strength conversion factors (Kaya and Karaman 2016). Broch and Franklin (1972) indicated that the UCS/PLI ratio was approximately 24 for a standard-size (50-mm) core. However, many researchers have obtained that UCS/PLI ratio of 24 would give erroneous results. (e.g. Pells 1975; Greminger 1982; Forster 1983,Smith 1997; Rusnak and Mark 2000; Ceryan et al 2008a; Kahraman 2014; Karaman et al.2014). Kahraman (2014) pointed out that in tests of many different rock types, the said ratio varied between 15 and 50, especially for anisotropic rocks. Similarly, Yilmaz and Sendir (2002) obtained this range is even wider, from 6 to 105. Ceryan et al (2008a) said that the UCS/PLI ratio depends on the degree of weathering. According to Singh et al. (2012), the UCS/PLI ratio of 21–24 should be used for harder rocks and 14–16 for softer rocks. Kaya and Karaman (2016) indicated that the geological origin of the rock has the greatest influence on the relationship between its UCS and PLI. The core length may be too short to allow the preparation of specimens long enough even for point load testing. In the case Block punch index (BPI) test is suggested to solve this problems (Ulusay and Gokceoglu 1997, Kahraman et al 2016). The block punch index (BPI) test is intended as an index test for the strength classification of rock materials and can be correlated with the UCS (Schrier 1988, Ulusay and Gokceoglu 1997, Gokceoglu and Aksoy 2000., Sulukcu and Ulusay 2001, Mishra and Basu 2012, Yesiloglu-Gultekin et al. 2013). The BPI tests were standardized in ISRM 2007. Sulukcu and Ulusay (2001) carried out a very detailed study was on a wide range of rock types and suggested that the UCS/ BPI ratio is 5.1. However, the recent studies (Mishra and Basu 2012; Yesiloglu-Gultekin et al. 2013) show that the relation between UCS and BPI may differ from the suggested relation for individual rock

37

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

types. Similarly, pyroclastic rocks tests were performed in the study by Kahraman et al (2016) showed a different trend with the suggested relation. According to the said study, the nonlinear regression analysis also indicates that the correlation coefficients of power law functions are higher than that of the linear function representing. According to Yilmaz (2009), there are some shortcomings associated with this conventional test, it allows to obtain UCS of rock cores divided into small disks, due to the presence of thin bedding or schistosity planes. This test can be conducted on very thin specimens only. And, Irregular failure (invalid test result) occurrence causes a requirement of too many rock specimens (Yilmaz 2009). If the rock is loaded through as line instead of a point in case point load, the effect of the heterogeneity or anisotropy is lessened (Yilmaz 2009). In order to overcome the shortcomings associated with point load index testing method, the core strangle test (CST) method and apparatus has been developed by Yilmaz (2009). The Core Strangle Test (CST) is an index test and mainly unconfined compressive strength indirectly determined when core samples having standard dimensions cannot be obtained. In the test, the core samples having a length of 25 mm or more are broken by the load applied through a circle perpendicular to the core axis as a “strangle”. Core Strangle Index (CSI) is calculated as a ratio of the load applied in circle to the circumference (Yilmaz 2009). The author said that CST can be performed with portable equipment in the laboratory. if the portable facility for rock coring is available, CST can also be conducted in the field. According to the results of the said study, nearly the same empirical equations can be used to relate CST to UCS, for different rock type and CST will be more preferable than point load index test. Brazilian Test (BT) is used for indirect determination of tensile strength of rock samples and related to UCS (from Nazir et al. 2013). According to Sheorey (1997), the compressive strength of the rock is approximately 10 times its tensile strength. Nevertheless, Sheorey`s strength ratio variation is high and consequently cannot be generalized due to the fact that rock behavior varies from place to place and is site specific (Nazir et al. 2013). According to the study performed by Farah (2011), indirect tensile strength, BTS has a better correlation with UCS than that of point load strength. Tugrul and Zarif (1999) said that there is a linear correlation between UCS and Brazilian Tensile Strength (BTS). Kahraman et al. (2012) conducted a research on compressive and tensile strength of different type of rocks. Based on their results, they proposed a linear correlation between UCS and BTS. However, the coefficient of determination, R2, of their study was almost 0.5 which is not reliable enough. Nazir et al. (2013) found a strong correlation with high reliability between UCS and BTS. The author proposed correlation for prediction of UCS for specific type of rock which is limestone is given in the following equation. The coefficient of determination of developed correlation is 0.9 which is relatively higher than previous suggested correlations (Eq. 3) UCS = 9.25 BTS 0.947

(3)

A new test called “Edge Load Strength” (ELS) for indirect estimation of unconfined compressive strength (UCS) of rock block specimens was introduced by Palassi and Pirpanahi (2013).The said test has some similarities with the indentation test; however, unlike indentation test and similar to the UCS test, the application of load is continued until failure takes place (Palassi and Pirpanahi 2013). The test apparatus is almost the same as point load test apparatus except that the lower conical platen of the point load apparatus is replaced by a flat seating. A cubic rock sample is placed on this seating and the load is applied by the upper conical platen at a certain point close to the edge of the specimen (The failure

38

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

surface passes through the upper and front face of the block (Palassi and Pirpanahi 2013). These authors indicated that, the following equation (Eq. 4) can be used to estimate UCS from ELSD. According to the result of the said study, good correlations exist between ELS and UCS tests for the estimation of rock strength and elastic modulus UCS = 187.312 * ELSD * D −1.329

(4)

where UCS is the unconfined compressive strength (MPa), ELSD is the edge load applied at the distance of D (kN), and D is the distance of the application of load from the block edge (mm). The indentation hardness test given in ISRM (1998) is a simple and easy test and can be conducted using a point load test apparatus. The test is of particular value when only a limited amount of rock material, e.g. a thin disc of core or a small lump sample, is available (Szwedzicki 1998). A standard indentation test was recommended by ISRM (2001). The study performed by Szwedzicki (1998), the relation between IHI and both UCS and BTS was investigated for igneous, metamorphic, and sedimentary rocks. Zausa and Civolani(2001) proposed this test to estimate UCS and other researchers have refined the approach. Mateus et al. (2007) studied methods of estimating the UCS of sandstone. Garciaetal (2008) evaluated the results of in dentation testing to determine the UCS of shale. In the study performed by Leite and Ferland (2001),indentation tests using a sphericonical indenter have been performed on artificial porous materials From these tests, three parameters have been calculated Young’s modulus (E), uniaxial compressive strength (C0) and Permanent penetration modulus (PPM). And C0 obtained from the indentation tests have been compared to those obtained from conventional uniaxial compression tests (Leite and Ferland 2001). According to Leite and Ferland (2001), PPM is a very good index to assess the variability of strength of porous materials since, although the value of the PPM is approximately 10 times greater than the value of C0 for a given porosity ratio, the variation of both with porosity is by all practical means the same. The needle penetrometer, a nondestructive portable testing devices test has been developed to overcome shortcomings of index strength tests such as the point load, indentation hardness, Schmidt hammer, Block Punch and Equotip hardness tests (Ngan-Tillard et al. 2011). It operates successfully, according to the manufacturer on extremely weak and/or very weak rocks where the point load, indenter, Schmidt hammer and the equotip do not (Ngan-Tillard et al. 2011). Preparation of small specimens from such rocks for some of these simple index tests like BPI and point load could sometimes be difficult (Erguler and Ulusay 2009) Contrary to the block punch strength test, the needle penetration test does not require the fabrication of a thin disk. In clayey rocks sensitive to water and rocks prone to fracturation during sample fabrication, the needle penetration test is probably the most adequate strength estimation test (Erguler and Ulusay, 2008, Ngan-Tillard et al. 2011). The needle penetrometer was introduced and used in the 1970s for indirect estimation of compressive strength of weak and uniform soil in some projects (Humboldt Mfg Corporation 2003). The most common model is the SH-70 penetrometer manufactured by Maruto Corporation, Ltd, Tokyo, Japan. The test equipment and procedure have been presented by Erguler and Ulusay (2008). In this instrument a constant was defined called needle penetration resistance (NPR). The other needle penetrometer used for estimating UCS is the equipment manufactured by Eijkelkamp in the Netherlands. The needle penetrometer test was successfully used by Ngan-Tillard (2011) to distinguish qualitatively carbonate sands from very weak and weak calcarenites. Same researchers suggested the relationships between NPR and UCS (e.g. Chaudhary 2004; Yamaguchi et al.2005;

39

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Maruto Corporation 2006; Ulusay and Erguler 2007; Erguler and Ulusay 2008 and 2009; Aydan et al. 2008; Ngan-Tillard et al. 2009; Ngan-Tillard et al. 2011, 2012). The said device has some disadvantages as well (Azada and Ahangari 2014). The most important disadvantages of the NP are its static force exertion system, its reliance on hand pressing, 10 N and 1 mm graduation of the device indicators (), and its high predictive uncertainty in field studies (Maruto Co. 2006, Ngan-Tillard et al. 2011, Azada and Ahangari 2014). The most important disadvantages of the NP are its static force exertion system, its reliance on hand pressing, 10 N and 1 mm graduation of the device indicators (Maruto Co. 2006), and its high predictive uncertainty in field studies (Ngan-Tillard et al. 2011, Azadanand Ahangari 2014). For this, the dynamic needle penetrometer (DNP) has been designed with different mechanical structures and modified for application on the surface of weak rocks both in field (Azadan and Ahangari 2014). The DNP test does not require special shape of specimen, and it could be introduced as an advantage of this in comparison with some tests like BPI and point load. And this device is its low production cost as economic viability has been accounted for in its designing (Azada and Ahangari 2014). Azada and Ahangari (2014) indicated that the UCS values predicted from the DNP were obviously more accurate than three different estimated values for UCS, resulting from Schmidt hammer, point load, and dynamic needle penetrometer. Recently, a new method introduced to estimate the uniaxial compressive strength of rocks was purposed by Mazidi et al. (2012). The said method includes reconstruction of artificial cylindrical specimens from rock cuttings with a density approaching about 80%of the density of intact rocks. For this purpose. drill cuttings were compacted in the standard compaction apparatus. The reconstructed cylinders are then subjected to unconfined compressive test. And a strong correlation between the estimated and measured UCS of the limestone investigated were obtained. Even the new developed indirect USC estimation methods such as block punch index test (Sulukcu and Ulusay, 2001), core strangle test (Yilmaz, 2 009), nail penetration test (Kayabali and Selcuk, 2010), and edge load strength test (Palassi and Pirpanahi, 2013) need large amount and big sizes of rock samples which are not available in the conditions in which there are not big scale samples for evaluating the UCS (or any other mechanical properties of rocks) by direct and indirect conventional tests (Ahmadi Sheshde and Cheshomi. 2015). Cheshomi et al. (2012) designed a single-particle loading apparatus and introduced the single-particle compressive strength (SCS) loading test to determine the UCS uniform particles of rock. They tested spherical rock particles of 2, 3 and 4 mm in diameter under direct loading in quasi-static conditions (1 mm/h). The load being applied at the instant of failure was determined to be the single-particle compressive strength index (SCSI). Cheshomi and Ahmadi-Sheshde (2013) proposed the following equation (Eq.5) to estimate the UCS of microcrystalline limestone; UCS =

(− 25.8 Ln (D )

)

+ l 53.5 Ln (SCSI ) − 83.51 Ln

(D ))

+ 310.2

(5)

where D is the single particle diameter (mm) and SCSI is single-particle strength (N). Their research showed that the size of the individual particles has a significant effect on the results of single-particle loading. Cheshomi et al., (2015) used the single-particle loading apparatus introduced by Cheshomi et al. (2012) for SCS testing on sandstone samples. The authors give the general empirical to estimate UCS (Eq. 6)

40

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

UCS =

(− 0.3 D

+ 1, 92) SCSI + /1.24 D + 6.72)

(6)

A comparison of the results of this study with the results from a previous study on microcrystalline limestone showed that, although the experimental equations of each rock type followed a similar pattern, the lithology of the rock affected the correlations between UCS and SCSI (Cheshomi et al., 2015). Ahmadi-Sheshde and Cheshomi (2015) introduced modified point load test (MPI) as an indirect measurement method to estimate UCS using the strength of small rock samples such as drilled rock cuttings. MPLT and SCS tests are different in term of their apparatus, strain rate and shape of small samples tested in them. Although MPLT and its loading system is analogous to PLT, the quasi-static condition of loading and the infinitesimal size of tested small rock samples in this method are the advantages of the MPLT in comparison to the PLT test (Ahmadi-Sheshde and Cheshomi 2015). The authors indicated that the accuracy of empirical relations is acceptable enough to estimate the UCS by using small rock samples in restricted sampling condition. In rock mechanic engineering design, UCS of rock materials should be obtained using direct measurement in the laboratory test standardized, if possible. In the condition that reliable estimation of rock strength using empirical equations is useful. Most investigations involve determining the individual correlation between an index and the UCS (i.e., a simple regression analysis) (e.g., Rzhevsky and Novick 1971, Koncagul and Santi 1999;Tugrul and Zarif 1999; Horsrud 2001;Fener et al. 2005, Chang et al. 2006; Kahraman et al. 2008; Sabatakakis et al. 2008; Mishra and Basu 2013). Certain studies have used models that relate the indices simultaneously with the UCS (i.e., multiple regression analysis) (e.g., Ulusay et al 1994; Tiryaki 2008; Gokceoglu and Zorlu 2004, Karakus and Tutmez (2006), Zorlu et al. 2008; Kahraman et al. 2008, Yilmaz and Yuksek (2009), Dehghan et al. 2010 Yagiz et al. 2012; Monjezi et al. 2012, Mishra and Basu 2013, Beiki et al.2013, Ceryan et al. 2016). Yan et al., (2009) indicated that there are two critical issues to be addressed in the formulation of an empirical model: 1) identification of the most influential rock properties or index parameters; and 2) selection the best model among relationships with different complexity. Fener and co-workers (2005) carried out the uniaxial compression test on eleven Nigde rock including six igneous rocks, three metamorphic rocks and two sedimentary rock, and analyzed the test results using regression analysis and predictive equations for compressive strength. In these predictive equations, Schmidt hammer test, point load test and impact strength test were used by the authors. In the said study, the derived equations were compared with the equations previously obtained by different researchers. According to the results of the t comparison there was no agreement between the equations suggested by different researchers. While some equations exhibit the same trend, the others differ. The author indicated that it is is probably because of the differentiation of rock types rock micro-texture, and test conditions. The past use of specific rock types is the main limitation of the existing empirical equations (Sonmez et al 2006). For a given database, there is a limitation over the regression of the data, which a trend line can accommodate. For a particular database the relation having the maximum possible regression coefficient is ranked highest (Sridevi 2000) and the accuracy is considered best. According to Maji and Sitharam (2008), in such a case, the possibility of greater accuracy in prediction and conformity to a wider range of data is completely neglected. Moreover, in evolving trend fitting curves by statistical regression, the data is constrained along a particular two-dimensional geometry of the statistical model used). For a dataset containing sparse data, statistical methods encourage the filtering of data outlying a particular geometrical band, to obtain best fitting. (from Maji and Sitharam 2008). In doing so, the es-

41

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

sence of the remaining data, and the information contained therein, is completely eliminated (from Maji and Sitharam 2008). Maji and Sitharam (2008) indicated that there is a need for a method of prediction, which can capture the maximum possible information from the whole dataset without confining the correlation along a particular geometry. Ng et al. (2015) indicated that statistical analyses and empirical formulae suffer from some major limitations and the reliability and applicability of the previous relationships is questionable. It is noted that an oversimplified empirical model will result in large estimation uncertainty. In contrast, a more complicated model contains more adjustable/free parameters and it is not surprising to result in smaller fitting error (Ng et al. 2015). However, the over-complicated models may over-fit the data and they are sensitive to measurement noise and modeling error (from Ng et al. 2015). These problems are difficult to be addressed using traditional methods. One of the drawbacks of using multi-variate linear regression analyses for the established empirical relationships is that it provides only the mean estimation of the targeted parameter (e.g., UCS) but not the associated uncertainty (from Ng et al. 2015). In this case, it may lead to overestimation of the low targeted parameter as well as underestimation of the high targeted parameter values (from Ng et al. 2015) An empirical model with more free parameters will normally result in smaller fitting error is the drawback of the least-squares regression analysis However, the models that are unnecessarily too complicated may over-fit the data and they are sensitive to measurement noise and modeling error (Yuen, 2010, Ng et al. 2015). Feng (2015) said that most empirical correlations are obtained using regression methods that do not quantify the uncertainties of predictions and it is not always possible to modify them to incorporate project-specific data. This limits their practical utility and makes them inappropriate for reliability-based design. According to Feng (2015), to overcome these problems, an alternative approach is needed to assess these commonly used empirical correlations. The Bayesian framework appears as an interesting solution to this need, as in addition to best estimates of model parameters it also provides uncertainty estimates of parameters and predictions, further allowing us to update and improve the empirical correlations as new project-specific data becomes available (from Feng 2015). Wang and Aladejare (2015) developed an approach to select the most appropriate site-specific regression model for UCS among numerous models available in order to characterize the UCS using only a limited number of PLI data. In the said study, the most appropriate regression model selected was further used in the development of the Bayesian equivalent sample approach for characterization of UCS, by combining it with the prior knowledge and the 19 site-specific PLI data available. Bayesian equivalent sample approach transformed the updated information from the prior knowledge and 19 site-specific PLI data into a large number of UCS samples. To overcome these difficulties of these conventional methods, many researchers have employed soft computing methods in estimating UCS of rock material (Table 2). The term soft computing was proposed by the inventor of fuzzy logic, Lotfi A. Zadeh. He describes it as follows (Zadeh, 1994): “Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neuro computing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind”. The potential benefits of soft computing models extend beyond their high computation rates. The higher performances of soft computing models arise from the greater degree of robustness and fault tolerance than traditional statistical models because there are many more processing neurons, each with primarily local connections (Yılmaz et al. 2012) The principal SC technologies can be categorized as 42

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

fuzzy algorithms, neural networks, supporting vector machines, evolutionary communication, machine learning, and probabilistic reasoning (Ceryan 2016). Fuzzy Set Theory (FST) introduced by Zadeh (1965) provides the means for representing epistemic uncertainty using set theory and describes the concept of gradualness and bipolarity (Dubois and Prade 1997). The techniques employed can be classified into four categories: basic fuzzy inference (fuzzy set/ logic); advanced fuzzy inference (combining other soft computing techniques), fuzzy probability theory and Klisiński fuzzy plasticity methods (Adoko and Wu 2011). Fuzzy set is an extension of the concept of a crisp set. A crisp set only allows full membership or no membership to every element of a universe of discourse, whereas a fuzzy set allows for partial membership several rule-based fuzzy modeling methods have been proposed in the last two decades. According to the structure of the consequent parts and the inference method to compute the output of the model, rule-based models can be classified into four main groups: fuzzy relational model, linguistic models, neural network based models and Takagi– Sugeno–Kang model (TSK) (Finol et al 2001). FIS is used to assess the correlation between physical and mechanical properties of rock including UCS. (e.g. Gokceoglu 2002, Gokceoglu and Zorlu 2004; Mishra and Basu (2013) Sonmez et al. 2004, Rezaei et al 2014, Ceryan et al 2016). Gokceoglu et al (2009) indicated that, the fuzzy model has a higher generalization ability than the regression model has. This is simply because of the flexibility of fuzzy modelling. Mishra and Basu (2013) said that both multiple regression analyses and the fuzzy inference system exhibited better predictive performances than simple regression analyses as far as estimation of UCS from rock mechanical indices are concerned. However, one should be cautious while employing multiple regression analysis as there is always a chance of cumulating plausible errors that remained within individual index test results. The authors indicated that the empirical equations developed using regression analyses in the said study should not be used for other rock types and even for the same rock types of different geology. In other hand, the fuzzy models developed in this study can be used for other engineering environments also (Mishra and Basu (2013). Rezaei et al (2014) concluded that intelligent system based on the Mamdani fuzzy model has a good capability in predicting UCS and more economical than other existing methods. Table 2. The some proposed model on soft computing methods to predictive UCS reported in the literature References

Technique

Input

Rock Type

R2

Meulenkamp and Alvarez Grima (1999)

ANN

EH, ρ, n, GS, RT

0.94

granodiorite, granite, dolomite, limestone, sandstone

Gokceoglu (2002)

FIS

PC

0.92

Agglomerates

Gokceoglu and Zorlu (2004)

FIS

Vp, BPI, PLI, TS

0.67

Greywacke

Sonmez et al. (2004)

FIS

PC

0.64

Agglomerate

Karakus and Tutmez (2006)

FIS

PLI, SHV, Vp

0.97

marble, limestone, dacite

Baykasoglu et al. (2008)

GP

Vp, WA, ρ

0.86

Limestone

Zorlu et al. (2008)

ANN

PD, C, Q

0.67

Sandstone

Yilmaz and Yuksek (2008)

ANN

n, Id, SHV, PLI

0.93

Gypsum

Gokceoglu et al. (2009)

FIS

CC, Id

0.88

clay-bearing sedimentary rocks

Canakci et al. (2009)

GP

Vp, WA, ρ

0.88

Basalt

Yilmaz and Yuksek (2009)

ANFIS

SHV, PLI, WC, Vp

0.94

gypsum

continued on following page 43

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Table 1. Continued References

Technique

Input

Rock Type

R2

Dehghan et al. (2010)

ANN

n, SHV, PLI, Vp

0.86

travertine

Cevik et al. (2011)

ANN

Id, CC

0.97

laminated marl

Yagiz et al. (2012)

ANN

UW, SHV, n, Vp, Id

0.50

carbonate rocks

Monjezi et al. (2012)

GA–ANN

n, ρ, SHV

0.96

sandstone, limestone, dolomite, granite, chalk, gneiss, siltstone, tuff, gypsum, olivine, granodiorite, slate, schist, conglomerate, quartzite, gabbro, and amphibolite

Rabbani et al 2012

ANN

n, BD, Sw

0.96

reservoir rock in oilfields

Kumar et al. (2013)

GPR, RVM, MPMR

SHV, n, Vp, PLI

0.98 0.99 0.91

Travertine

Ceryan et al (2013)

ANN

n, Vm

0.81

carbonate rocks

Ozbek et al (2013)

GE

ne, WA, UW

0.99

ignimbrite, basalt

References

Technique

Input

R

Rock type

Mishra and Basu (2013)

FIS

BPI, PLI, SHV, Vp

0.98

granite, schist and sandstone

Manouchehrian et al. (2013)

GP

Q, ρ, n, SHV, CI

0.63

Sandstone

Yesiloglu-Gultekin et al. (2013b)

ANFIS

PC

0.83

granite, granodiorite

Beiki et al. (2013)

GP

ρ, n, Vp

0.83

carbonate rocks

Ceryan (2014)

SVM RVM

n, Vid

0.75 0.77

tuff, basalt, andesite and dacite

Rezaei et al. 2014

FIS

SHV, ρ, n

0.94

Ultramafic rocks, granitic rocks, metamorphic rocks and limestone, siltstone, marl, shale chalk, conglomerate

Torabi-Kaveh et al. 2014

ANN

Vp, ρ, n

0.95

porous limestone to marly limestone

Jahed Armaghani et al. (2015)

ANFIS

ρ, Vp, Q, Pl

0.99

Granitic rocks

Tonnizam Mohamad et al. (2015)

PSO–ANN

PLI, TS, ρ, Vp

0.97

shale, old alluvium, iron pan

Momeni et al. (2015

PSO–ANN

ρ, Vp, PLI, SHV

0.97

limestone, granite

Jahed Armaghani et al. (2016a)

ICA–ANN

SHV, PLS, Vp

0.94

Sandstone

Jahed Armaghani et al. (2016b)

ICA–ANN

n, SHV, Vp, PLI

0.92

Granite

Madhubabu et al. (2016)

ANN

n, ρ, Vp, ν, PLI

0.97

carbonate rocks

Ghasemi et al 2016

M5 model tree algorithm

UW, SHV, n, Vp, D

0.89

carbonate rocks

Heidarian et al 2016

ANFIS

dpt, ρ, Vp

0.89

reservoir rock in oilfields

Ceryan et al. 2016a

ANN

n, Vid

0.88

volcanic rocks

2

(ρ: density, n: porosity, ne: effective porosity, BD: Bulk density, UW unit weight, Vp: P-wave velocity, Vm: P-wave velocity in the solid part of the sample, Vid: P durability index, Sw:water saturation; WA: water absorption, WC: water content,, C: concave–convex, RT rock type, GS: grain size PD: packing density, Q: quartz content, CC: clay content, PC: petrographic composition, Pl: plagioclase content, dpt: 44

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

depth, BPI: block punch index, CI: Cone indenter hardness, EH: Equotip hardness, Id: Slake durability index, PLI: Point load index, SHV: Schmidt hammer rebound number,, TS: Tensile strength, ANN: Artificial Neural Network, ANFIS: Aadaptive Neuro-fuzzy Inference System, FIS: Fuzzy Inference System, GA: Genetic Algorithm, GE: Genetic Expression Programming, GP: Genetic Programming, GPR: Gaussian Process Regression, ICA: Imperialist Competitive Algorithm, MPMR: Minimax Probability Machine Regression, PSO: Particle Swarm Optimization, RVM: Relevance Vector Machine, SVM: Support Vector Machine) ANNs are digitized models of a human brain computer programs designed to simulate the way in which human brain processes information. ANNs learn (or are trained) through experience with appropriate learning exemplars just as people do, not from programming. (Agatonovic-Kustrin and Beresford 2000). The method is a relatively new nonlinear statistical technique and the most particular characteristic of a neural network system is its capability to learn from the data being processed. It can be used to solve problems that are not suitable for conventional statistical methods (Aqil et al, 2007). The development of an ANN is based on a set of input–output examples, which the network learns during the training phase. In this stage, the network parameters associated with the neurons and/or the interconnection links are determined using an optimization algorithm, which minimizes the errors between the true outputs and the network predictions over a set of training examples. After the training phase is completed, the NN model is capable of producing accurate estimations of the output variables given a new set of input data (Alexandridis et al. 2012) The method used to generate the examples to train the network and the training algorithm employed has a significant impact on the performance of the neural network-based model (Majdi and Beiki 2010, Bagheripour 2014). It has been reported that the feed forward back propagation (BP) algorithm is the most proficient learning procedure for MLP neural networks (Tawadrous and Katsabanis 2007). This algorithm aims to reduce the deviation between the desired objective function value and the actual objective function value (Aqil et al, 2007). Using ANNs, many researchers have attempted to estimate the UCS (e.g., Nie and Zhang 1994; Lindquist and Goodman 1994; Meulenkamp and Alvarez Grima 1999; Singh et al. 2001, Gokceoglu and Zorlu 2004, Kahraman and Alber 2006; Cobanoğlu and Celik 2008;Tiryaki 2008; Zorlu et al. 2008; Yilmaz and Yuksek 2009; Dehghan et al. 2010; Kahraman et al. 2010; Cevik et al. 2011; Yagiz et al. 2012; Ceryan et al. 2012; Ceryan et al. 2013a; Yesiloglu-Gultekin et al. 2013a; Majdi and Rezaei 2013;Yurdakul and Akdas 2013). Yagiz et al. (2012) examined the effects of the cycling integer of a slake durability index test on intact rock behavior and estimated certain rock properties, such as the UCS, from rock index parameters using an ANN and “regression techniques. The author said that Id4, slake durability index obtained by four cycle, has the most effective slake durability cycling integer for both characterizing carbonate rocks and predicting the UCS and E of rock materials via ANN and nonlinear regression methods. According the results of the said study, even though both modeling methods are acceptable for estimating the relevant rock properties, artificial neural networks give relatively more precise result in comparison with regressions. The study performed by Ali and co-workers (2014) was carried out to model uniaxial compressive strength (UCS) of anisotropic amphibolite rocks as a function of microfabric properties using multivariate statistical, artificial neural network (ANN) and fuzzy inference system techniques. In the said study, microfabric properties including grain size, shape factor and quartz content were selected as more influential parameters based on statistical criteria (T-test) that affect UCS of amphibolite rocks more than the other do. According to the said study, the developed three models are reliable to estimate the UCS and the superiority of the ANN model, which is indicated by the consistency of the performance index criteria. Besides, the predictive capability of the regression model is observed to be by far quite 45

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

accurate when compared to fuzzy inference system (FIS). The author indicated that one of the major disadvantages of neural networks compared to traditional statistical models is their opaqueness that is no understanding of the underlying relationship between inputs and outputs can be gained. ANFIS was developed by Jang (1993) based on the Takagi– Sugeno fuzzy inference system (FIS). It is known that fuzzy logic is easy to implement due to its linguistic rules and has an advantage of integrating human knowledge to overcome uncertainty and ambiguity. And, artificial neural network can learn from data and has an advantage of recognizing pattern, however these created pattern by ANN are although robust but not implemental. Therefore, researchers endeavor to take advantage of both methods to build a flexible intelligent system (Mohebia et al 2015). ANFIS is constructed by a set of if–then fuzzy rules with proper membership functions to produce the required output from the input data (Jahed Armaghani et al 2016b). An adaptive neural network is a network structure consisting of several nodes connected through directional links. Each node is characterized by a node function with fixed or adjustable parameters (Fattahi 2016). Once FIS is initialized, neural network can be utilized to determine the unknown parameters (premise and consequent parameters of the rules) minimizing the error measure, as conventionally defined for each variable of the system (Jang, 1993; Jang et al. 1997; Fattahi 2016). Due to this optimization procedure the system is called adaptive (ng, 1993; Jang et al., 1997; Fattahi 2016). Yilmaz and Yuksek (2009) constructed statistical and soft computing techniques, such as multiple regression, an ANN and neuro fuzzy models, to estimate the UCS of gypsum. The author indicated that constructed According to the results of the said study, ANFIS model exhibits a high performance for predicting UCS and E .The author said that the ANFIS is a good approach for minimizing the uncertainties in the rock engineering projects and the use of ANFIS will also may provide new approaches and methodologies, and minimize the potential inconsistency of correlations. In the study performed by y Yesiloglu-Gultekin et al. (2013b),the ANFIS model exhibits a slightly higher prediction performance than the nonlinear multiple regression model, indicating that the developed nonlinear multiple regression and ANFIS models can be used to predict UCS of the granitic rocks. Jahed Armaghani et al. (2016b) applied three non-linear prediction tools, namely non-linear multiple regression (NLMR), ANN and ANFIS, for estimating UCS of granitic rocks and compared their performances. They found that the prediction performance of the ANFIS models was higher than ANN and NLMR models. Regression tree (RTr) analysis is an innovative and powerful technique which can be applied to solve engineering problems with high degree of accuracy (Breiman et al. 1984). Regression tree technique for building predictive models approximates a regression relationship using a decision tree (from Tiryaki 2008). Unlike other soft computing techniques, model trees explicitly describe the inherent patterns and relationships in data through rules and regression equations, which make them easier to understand. (Etemad-Shahidi and Ghaemi 2011). Such a tree partitions the data set into regions, using values of the predictor variables, so that the response variable is roughly constant in each region. RTr is non-parametric in nature and is able to handle data with high skew value. Moreover, there is no need to have many input parameters with a specific assortment in RTr technique compared to other multi-inputs models (Razi and Athappilly 2005). A regression tree is a sequence of questions that can be answered as ‘yes’ or ‘no’, and a set of fitted response values (from Tiryaki 2008). Each question asks whether a predictor satisfies a given condition (from Tiryaki 2008). Depending on the answers to one question, it either is proceeded to another question or is arrived at a fitted response value (from Tiryaki 2008). If the answer is ‘yes’ to a particular question, the left branch is taken to proceed (from Tiryaki 2008). According to

46

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

the result of the study performed by Tiryaki (2008), respective ANN models of UCS and E are more acceptable than multiple nonlinear regression models of UCS and E in predicting actual UCS and E values. However, regression trees technique has appeared to be the best in building predictive models for UCS and E. In the study performed by Liang et al. (2016), it was found that each rock index test, density test, Schmidt hammer test, point load strength test has a meaningful and acceptable relationship with UCS of the sandstone samples, by performing simple regression analysis. Additionally, two multi-input models namely multiple regression and regression tree were constructed to predict UCS. The results of the said study indicated that the RTr model can predict UCS with higher performance capacity compared to MR technique. Koza (1992) proposed genetic programming (GP) technique which is an extension to genetic algorithms. In genetic programming, populations of hundreds or thousands of computer programs are genetically bred. This breeding is done using the Darwinian principle of survival and reproduction of the fittest along with a genetic recombination (crossover) operation appropriate for mating computer programs (Koza 1992). Despite the good performance of ANNs, SVM and many of the other ML methods, they are considered as black-box models. That is, they are not capable of generating practical prediction equations. GP is considered as an efficient approach to deal with this issue (Lary et al: 2016). GP breeds computer programs to solve problems by executing the following three steps: (1) Generate an initial population of random computer programs composed ofthe primitive functions and terminals of the problem. (2) Iteratively perform the following sub-steps until the termination criterion is satisfied: (a) Execute each problem in the population so that a fitness measure indicating how well the program solves the problem can be computed for the program. (b) Create a new population of programs by selecting programs in the population with a probability based on fitness and then applying the following primary operations (Dadkhah and Esfahani 2013); (a)Reproduction: Copy an existing program to the new collected from various samples to be included in training population, (b) Crossover: Create new computer programs by crossover, (c) Mutation: Create new computer programs by mutation, (d) Choose an architecture-altering operation to one selected program and (e) The single best computer program in the population produced during the run (best solution so far) is designated as the result of genetic programming. Genetic expression programming GEP was developed by Ferreira (2001) using fundamental principles of the genetic algorithm (GA) and genetic programming (GP). Although GEP performs the symbolic regression using most of the genetic operators of GA, there are some differences between GEP and GA (Ozbek et al. 2013). Any mathematical expression defined as symbolic strings of fixed- length (chromosomes) in GA is represented as nonlinear entities of different sizes and shapes (parse trees) (Ozbek et al. 2013). But in GEP, it is encoded as simple strings of fixed-length, which are subsequently described as expression trees of different sizes and shapes (Munoz, 2005; Cevik et al., 2010, Ozbek et al. 2013). Perhaps, one of the pioneer studies in estimating UCS was done by Baykasoglu et al. (2008). They applied GP-based approaches including multi expression programming (MEP), gene expression programming (GEP) and linear genetic programming (LGP) to to the uniaxial compressive strength (UCS) and tensile strength prediction of limestone. Beiki et al. (2013) investigated the applicability of one of the latest soft computing tools called genetic programming to build predictive models of the UCS and E of carbonate rocks. According to the results of the said study, al GP equations produced in the said study better result than corresponding regression models for UCS prediction from porosity. And, the multiple power regression models for both UCS and E prediction are most reliable than the other developed simple and multiple regression models.

47

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

In the past decade, a new alternative kernel based technique called a support vector machines (SVM) have been derived from statistical learning theory (Vapnik, 1995). SVM model using sigmoid kernel function is equivalent to a two-layer perceptron neural network. Using a kernel function, SVMs are alternative training methods for polynomial, radial basis function, and multilayer perceptron classifiers in which the weights of the network are found by solving a quadratic programming problem with linear constraints, rather than by solving a non-convex, unconstrained minimization problem as in standard ANN training (Huang et al., 2010). SVM is popular for estimating geological material behaviors due to certain advantages over ANN (Gill et al. 2007; Goh and Goh 2007; Samui 2008; Zhao 2008; Yao et al. 2008; Pal 2009; Zhi-xiang et al. 2009; Khandelwal 2010; Samui 2011; Martins et al. 2012; Mohamadnejad et al. 2012; Samui and Karthikeyan 2013; Ceryan et al. 2013b, Ceryan 2014). Relevance vector machines (RVM) is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation (Tipping 2000). It can be seen as a probabilistic version of the SVM (Scholkopf and Smola 2002) and effectively be used for regression and classification problems. RVM is based on a hierarchical prior, where an independent Gaussian prior is defined on the weight parameters in the first level, and an independent Gamma hyper prior is used for the variance parameters in die second level (Pal 2009). Major advantages of RVM over the SVM are: a-) reduced sensitivity to die hyperparameter settings, b-) ability to use non-Mercer kernels, c-) probabilistic output with fewer relevance vector for a given dataset and d-) No need to define the parameter C (Pal 2009). RVM have recently attracted much interest in various rock engineering and geotechnical applications (Samui 2012, Kumar 2013, Ceryan 2014). Kumar et al (2013) investigated the applicability of machine learning techniques, Relevance Vector Machine (RVM), Gaussian Process Regression (GPR) and Minimax Probability Machine Regression (MPMR) for determination of UC) and E of travertine samples. Point load index, porosity, P wave velocity and Schmidt hammer rebound number (Rn) have been taken as inputs of these soft computing models. According to the results of the said study, there models produce excellent performance, the RVM models gives equations for prediction of E and UCS and MPMR maximizes the minimum probability of future predictions being within some bound of the true regression function. Ceryan (2014) examined the applicability and capability of RVM and SVM models for predicting the UCS of volcanic rocks. The porosity and P-durability index representing microstructural variables are the input parameters in these soft computing models. According to the said study, RVM model performed better than the SVM models. In addition to the obtained measures, compared with the SVM model, the RVM model involved few relevant vectors and a controlling parameter (the Gaussian kernel parameter only), which thus minimized the possibility of overtraining and the computational time (Ceryan 2014) Hybrid systems combine two or more individual technologies (fuzzy logic, neural networks, and genetic algorithms, SVM, RVM and Particle Swarm Optimization) for building intelligent systems (from Rudas and Fodor 2008). The individual technologies represent the various aspects of human intelligence that are necessary for enhancing performance. However, all individual technologies have their constraints and limitations. Having the possibility to put two or more of them together in a hybrid system increases the system’s capabilities and performance, and also leads to a better understanding of human cognition (Rudas and Fodor 2008, Liu et al 2013, Liu et al. 2014). A hybrid neural network and genetic algorithm (Monjezi et al. 2012), a hybrid neural network and imperialist competitive algorithm (Jahed Armaghani et al. 2016a, b), and a hybrid artificial neural network and particle swarm optimization technique (Tonnizam Mohamad et al. 2015; Momeni et al. 2015) can be given as examples for hybrid systems to prediction UCS.

48

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

3. MATERIAL AND TESTING PROCEDURE The study area is located in the Eastern Pontides of NE Turkey (Figure 1). The igneous activity reveals a high diversity in age, composition and geodynamics, ranging from Jurassic to neo-tectonic eruptions in Pleistocene, and from arc through syn-collisional thickening to post-collisional extensional regime in the eastern Black Sea region of Turkey (Sahin 2004). The samples used in this study were included volcanic rocks and granitic rocks exposed in Eastern Pontides of NE Turkey (Figure 1). The dasitic and andesitic rock samples were from the excavated slopes throughout the GumushaneGiresun roadway in NE Turkey (Figure 1). The volcanic rocks aged Late Cretaceous are interceded sedimentary rock. The intact rock samples from the tuffs and basalts were obtained from the IyidereIkizdere quarry, Rize NE Turkey. The index and mechanical properties of these volcanic rocks samples were taken from Ceryan (2014). The index and engineering properties of the weathered samples from the stone walls in 1970 for environmental recreation at the Karadeniz Technical University, Trabzon, NE Turkey were from the study performed by Ceryan and Usturbelli (2011). The index and mechanical properties of the granitic sample from Dereli Granitoid and Harsit Granitoid were taken from Boynukalin (1990) and Ceryan et al (2008a). Harsit pluton is formed by granodiorite, quartz diorite, quartz monzonite, quartz monzodiorite, loco granite existing in the contact zones of the batholith. All these granitic rocks intruded into eastern Pontide volcanic arc during Upper Cretaceous to Eocene period. The pluton is limited by NE–SW oriented faults and the intrusion orientations of the pluton show a good accordance with fault orientations (Ceryan et al 2008a). Dereli Granitoid is formed by granodiorite, alcalen granite, tonalite, diorite, monzodiyorite, micro tonalite and micro granodiorite. These granitic rocks intruded during Upper Cretaceous to Eocene (Boynukalin 1990). Figure 1. The study area location (1. Paleozoic granitoids, 2. Late Cretaceous volcanic and volcanosedimentary rocks, 3. Late Cretaceous–Eocene granitoids, 4. The location of the samples investigated) (Sahin et al., 2004).

49

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

In the study, ultrasonic pulse velocity (UPV) tests were performed using the first method suggested by the ISRM (1981). The UPV measurements were performed on the samples under both dried and saturated conditions as in the studies performed by Ceryan et al. (2012). The total porosity (n) of the rock was estimated using the following equations (Eq.7) n = 1−

ρs ρd



(7)

where ρs is the dry density, and ρd is the grain density. The slake durability test was performed using the standard testing method developed by Franklin and Chandra (1972), recommended by the International Society for Rock Mechanics (ISRM, 1981), and standardized by the American Society for Testing and Materials (ASTM 1990). Slake durability testing was performed using 10 samples of each rock type for four cycles. The slake durability index corresponded to each cycle and was calculated as the percentage ratio of the final to the initial dry weights of the rock in the drum after the drying and wetting cycle (Figure 2). Ultrasonic pulse velocity (UPV) tests were performed using the first method suggested by the ISRM (1981). The UPV measurements were performed on the samples under both dried and saturated conditions as in the studies performed by Ceryan et al. 2012. The P-wave velocity in rock samples that lack pores and fissures, Vm, was calculated using Eq. 8 (Barton 2007): Figure 2. Index and strength properties of the samples examined (n: porosity, Vp: P-wave velocity, Pids: P-durability second index suggested in this study, UCS: uniaxial compressive strength)

50

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

φ 1 1−φ = + Vp Vfl Vm

(8)

where Vp is the P-wave velocity in the sample, Vfl is the velocity in the fluid, ϕ is the ratio of the path length in the fluid to the total path length (i.e., the porosity), and Vm is the P-wave velocity in the rock samples that lack pores and fissures (i.e., the P-wave velocity for the solid portion of the rock The uniaxial compressive strength (UCS) tests were performed in accordance with the International Society for Rock Mechanics (ISRM 2007). The core samples were prepared using a 2.5:1 height/diameter ratio with a 50 mm diameter. The experiments were performed using 10 samples under dry conditions for each group. During the test, the samples were loaded to be broken in 10 and 15 min. The stress was calculated as the ratio of the compressive force and sample cross-section area at the beginning of the test, and the uniaxial compressive strength was the ratio of the maximum applied force and cross-section area (Table 3, Figure 2). (A, B, D: andesitic, basaltic and dasitic rock samples, respectively, GSC: granitic rock sample from Harsit Granitoid (Ceryan et al.2008), GSB: granitic rock sample from Dereli Granitoids (Boynukalin 1990), n: porosity (%), Vp: P-wave velocity (km/s), Id: Slake durability index (%), Vid: P-durability index (Ceryan 2014), Pids: P-durability second index suggested in this study)

4. MODELING TECHNIQUES AND THEIR APPLICATION 4.1. The Input Parameters for Modelling Technique The intrinsic properties affecting UCS of rock material can be divided into two groups; one is pore characteristics, and the second is microstructural variables consisting of mineralogical composition and rock texture (Ceryan 2014). Mineralogical and petro-physical properties including density, cation packing index, content of specific mineral such as quartz and clay are widely used for characterizing microstructural variables and weathering grades of rock materials (e.g. Aydin and Duzgoren-Aydin, 2002, Ceryan 2009, Zorlu et al. 2008, Ceryan 2012, Manouchehrian et al 2012, Mishra and Basu 2013, Ceryan 2014, Ceryan 2015a). Relations between UCS of rock materials and porosity have been reported by a number of researchers. Considering the results of the studies, Ceryan (2014) used porosity to represent the effect of pore characteristics on UCS. In this study, porosity, slake durability, P wave velocity, P-durability index and P-durability second index were used in the regression model to predict UCS of the magmatic rocks. P-durability second index (Pids) suggested in this study was defined as following equations (Eq. 4) Pids = Vp *I d

(9)

Where Vp is the P-wave velocity in dried rock samples Considering the results of the regression analysis performed in this study, porosity and P-durability second index was selected as input parameters in proposed ANN and LS-SVM models. In these models, porosity represent the pore characteristics while P-durability second index represent the microstructural variables. The input and output data were normalized to prevent the model from domination by vari-

51

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Table 3. The porosity, P-wave velocity in dried condition, slake durability index P-durability index, Pdurability second index and uniaxial compressive strength of the samples investigated Sample

n

Vp

A5B1

5.9

3.352

GSC13

5.9

A5B2

6.9

GSC12

Id

Vid

Vp

σc

Id

Vid

81.9

80.5

3.164

2.698

D1C1

10.2

3.046

72.5

66.3

2.608

2.019

2.625

107

85.2

3.658

2.237

3.246

89.4

81.9

3.115

2.658

BZ2

1.5

3.896

162.6

93.8

3.976

3.654

D1C2

11.4

3.178

63.5

74.5

2.748

2.368

4.3

3.065

110.3

95.5

4.387

2.927

D1B2

1.8

4.048

216.3

99.5

4.353

4.028

D4E1

8.1

3.228

73.2

74.9

2.911

2.418

D1E1

9.4

3.4

95.5

87.6

3.402

2.978

A5B4

8.2

3.285

76.5

GSC7

1.7

4.227

137.4

77.8

3.021

2.556

D1D2

8.7

3.517

101

89.7

3.614

3.155

98.7

5.582

4.172

D1E2

10.6

3.265

75.4

78.9

3.157

2.576

A5B5

6.9

3.246

87.5

81.6

3.259

2.649

BZ6

3.8

2.674

94.6

78.5

2.611

2.099

BZ1

1.1

4.202

175.7

98.1

4.25

4.122

D1A1

8.2

3.404

118.5

88.3

3.369

3.006

GSC9 BZ7

2.6

4.006

137.8

98.7

5.428

3.954

D1D1

7.6

3.55

115

91.8

3.633

3.259

2.8

3.575

159.5

94.7

3.774

3.386

BZ3

0.6

4.559

199.3

96.3

4.453

4.39

BZ8

5.2

2.578

78.6

72.1

2.239

1.859

D4-A2

2.5

3.857

173.9

98.8

4.059

3.811

GSC6

1.9

4.285

164.5

99.6

5.767

4.268

D4E2

10.6

2.876

52.2

68.4

2.313

1.967

D3-A1

4.1

3.757

168.3

97.8

3.999

3.674

GSC14

4.3

2.524

95.5

68.6

3.11

1.731

GSC4

1.8

2.891

144.2

97.2

4.862

2.81

D4-C2

2.3

3.571

175.8

96.5

4.089

3.446

A2A1

3.3

3.886

172.5

94.6

4.024

3.676

GSB7

3.1

3.92

106.5

86.7

4.161

3.399

D3-A2

3.1

3.7

170.9

96.2

3.923

3.559

A2A4

3

4.1

180.4

96.3

4.006

3.948

GSC1

1.6

4.053

134.8

99.5

5.69

4.033

D4-C1

4.7

3.629

123.8

93.8

3.77

3.404

A2A2

2.7

4.09

206.3

98.8

4.163

4.041

A3A2

3.7

3.722

140.4

94.9

4.129

3.532

D3C1

11.9

3.173

87.7

89.7

3.17

2.846

A3A3

5.4

3.73

114

89.1

3.729

3.323

GSB6

2.15

4.26

158.9

92.5

4.374

3.941

D4B1

3.1

3.841

170.4

97.3

4.018

3.737

A3A1

4.1

3.691

158.2

95.4

3.908

3.521

GSB5

3.84

3.6

91.1

85.3

3.941

3.071

GSB4

3.37

4.03

113.9

88.6

4.322

3.571

D4E3

6.5

3.144

86.3

79.3

2.979

2.493

D3C2

12.5

3.288

79.9

85.6

3.464

2.815

D4B2

4.7

3.836

153.4

93.7

4.005

3.594

GSB3

2.6

3.91

115.5

89.2

4.126

3.488

GSC2

1.7

3.504

104.7

97.5

4.853

3.416

A3A4

2.7

3.538

143.2

94.2

3.726

3.333

D4-C3

4.1

3.661

147.8

90.4

3.712

3.31

GSB2

3.01

4.25

129.1

91.3

4.382

3.88

A2A3

3

4.043

179.8

96.2

4.262

3.889

D3B1

10.3

3.417

94.3

91.1

3.477

3.113

GSC5

2.9

3.404

77.2

97.5

4.456

3.319

GSC10

2.4

3.833

111.8

98.3

4.937

3.768

D1A2

8.3

3.471

121.2

93.4

3.65

3.242

BZ4

0.7

4.625

212.2

98.6

4.681

4.56

D1B1

2.1

4.124

202

99.2

4.432

4.091

DB2

5.8

3.479

102.9

94.5

3.663

3.288

GSC8

1.9

4.157

138.5

98.8

5.669

4.107

GSB1

0.67

4.03

170.03

96.1

4.359

3.873

BZ5

2.9

3.057

125.1

88.7

3.266

2.712

A5B3

5

3.332

98.9

79.2

3.067

2.639

D4-A1

3.9

3.883

184.5

97.4

4.138

3.782

52

σc

Pids

Sample

n

Pids

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

ables with large values, which is common in artificial intelligence models. In this study, the data were normalized using Eq. (10). z i = x i / x max

(10)

Where zi is the scaled value, xi is the original data, d xmax is maximum values of the original data.

4.2. Regression Analysis The regression analysis is a statistical tool that can be applied to examine the relationships between variables. In this technique, the relationship between independent (predictor) variable and dependent (output) variable is systematically determined in the form of a function (Jahed Armaghani et al 2016a). The major conceptual limitation of all regression techniques is that you can only ascertain relationships, but never be sure about underlying causal mechanism. In these model, the alternative causal explanations are not often considered. A simple regression analysis is one of the techniques to establish a prognostic model using available input parameters to estimate unknowns in rock engineering. Two main regression methods in statistics are simple and multivariable analysis (Yagiz et al 2012). The simple regression analyses provide a means of summarizing the relationship between two variables (Yagiz et al 2012). The model is linear when each term is either a constant or the product of a parameter and a predictor variable. A linear equation is constructed by adding the results for each term. This constrains the equation to just one basic form (Eq. 11): Response = constant + parameter * predictor + ... + parameter * predictor Y = bo + b1X1 + b2X 2 + ... + bk X k

(11)

While a linear equation has one basic form, nonlinear equations can take many different forms including power (y = axb), logarithmic(y = a lnx + b), and exponential (y = aebx) functions. That covers many different forms, which is why nonlinear regression provides the most flexible curve-fitting functionality. Multiple regression analysis is a powerful technique used for predicting the unknown value of a variable from the known value of two or more variables- also called the predictors. By performing linear multiple regression (LMR) technique a linear multiple relationship between input and output parameters can be obtained, while non-linear multiple regression (NLMR) is a technique to achieve a non-linear relationship between these parameters (Jahed Armaghani et al 2016a).The assumption of multiple nonlinear regression (MNLR) models is that the relationship between the dependent variable Yi and the p vector of regressors Xi is nonlinear. The following represents a MNLR equation (Ivakhnenko, 1970): Y = a + β1X i + β2 X j + β3 X i 2 + β4 X j 2 + ……… + βk X i X j

(12)

Where a is the intercept, β is the slope or coefficient, and k is the number of observations. For forecasting purposes, the multiple nonlinear regression equation will fit a forecasting model to an observed

53

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

data set of Y and X values. The fitted model can be used to make a forecast of the value of Y with new additional observed values of X.

4.3. Fuzzy Interface System The term “fuzzy logic,” as currently used in the literature, has two distinct meanings. In the narrow sense, it is viewed as a generalization of the various many-valued logics that have been investigated in the area of mathematical logic since the beginning of the 20th century (Demicco 2014). In the alternative, broad sense, fuzzy logic is viewed as a system of concepts, principles, and methods for dealing with modes of reasoning that are approximate rather than exact (from Demicco 2014). In this sense, fuzzy logic is based upon fuzzy set theory. Fuzzy set theory, introduced by Zadeh (1965), is an outgrowth of classical set theory. Fuzzy set theory is flexible about boundaries of sets. Each fuzzy set is specified by its own membership function. A membership function of a fuzzy set maps an input value to its proper membership value. In crisp mathematics, elements either belong to or not belong to a particular set. (Sing et al 2009). Contrary to the classical concept of a set, or crisp set, the boundary of a fuzzy set is not precise. A crisp set only allows full membership or no membership to every element of a universe of discourse, whereas a fuzzy set allows for partial membership. In classical logic, the membership value of any member is equal to 1 if it is included in the set; if not, that value is equal to 0. The membership or non-membership of an element x in the crisp set A is represented by the characteristic function μA, defined by if x ϵ A, µA (x ) = 1 and if x ∉ A µA (x ) = 0

(13)

On the contrary, the members of a fuzzy set can take the membership values ranging between 0 and 1 in fuzzy logic (Eq. 14) A = {x , µA (x )} |x ∈ X

(14)

Here, the fuzzy set “A” is represented by a membership function such as μA(x) in the information universe of X. This membership function μA(x) defines the membership degree of each member in the set. There is strong relation between fuzzy set theory and classical set theory. Operations in this case are union, intersection, complement, etc. Let μA and μB be the membership functions of elements of set A and B, respectively. Then, membership function of union of these two sets is given by Eq. 15 (Sing et al 2009). This operation of fuzzy sets is equivalent to OR operation in Boolean algebra. m AUB = max (m A , m B )

(15)

Their intersection can be calculated as following equation (Eq. 16) (Sing et al 2009). This operation of fuzzy sets is equivalent to AND operation in Boolean algebra m AUB = min (m A , m B )

54

(16)

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

The complement is determined by Eq. 17. This operation of fuzzy sets is equivalent to NOT operation in Boolean algebra. m

/ A

= 1 − mA

(17)

In effect, the use of linguistic variables and fuzzy IF-THEN- rules exploits the tolerance for imprecision and uncertainty. In this respect, fuzzy logic mimics the crucial ability of the human mind to summarize data and focus on decision-relevant information.. In a more explicit form, if there are I rules each with K premises in a system, the ith rule has the following form. If a1 is Ai,1 θ a1 is Ai,2 θ…….. θ ak is Ai,k then Bi In the above equation a represents the crisp inputs to the rule and Aand B are linguistic variables. The operator θ can be AND or OR or XOR.Several rules constitute a fuzzy rule-based system (Wang 2007). Evaluation of IF-THEN fuzzy rule consist of two steps. The first is to determine the result of the antecedent component, which includes fuzzification of input parameters and applying proper operators. The second step involves the use of the obtained result to evaluate the consequent, and this is called implication (Singh et al. 2009) The Fuzzy Inference System (FIS) is a famous computing system which is based on the concepts of fuzzy set theory, fuzzy if–then rules, and fuzzy reasoning (Ross, 2004). The methods are classified in direct methods and indirect methods. Direct methods, such as Mamdani’s and Sugeno’s, are the most commonly used (these two methods only differ in how they obtain the outputs). Indirect methods are more complex. Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. However, Mamdani-type FIS entails a substantial computational burden (Kaur and Kaur 2012). On the other hand, Sugeno method is computationally efficient and works well with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for dynamic non linear systems (Kaur and kaur 2012). According to kaur and Kaour (2012), the most fundamental difference between Mamdani-type FIS and Sugeno-type FIS is the way the crisp output is generated from the fuzzy inputs.Other differences are that Mamdani FIS has output membership functions whereas Sugeno FIS has no output membership functions. Mamdani FIS is less flexible in system design in comparison to Sugeno FIS as latter can be integrated with ANFIS tool to optimize the outputs (Kaur and Kaur 2012). Mamdani’s method is the most commonly used in applications, due to its simple structure of ‘minmax’ operations. each one of the steps of the method was given following briefly (Chen and Pham 2001)). Firstly, heir membership values of the inputs (crisp values) are obtained. This process is called ‘input fuzzification’. If the antecedent of the rule has more than one part, a fuzzy operator (t-norm or t-conorm) is applied to obtain a single membership value. (Step 1. Evaluation of the antecedent for each rule) Given the consequent of each rule (a fuzzy set) and the antecedent value obtained in Step 1, a fuzzy implication operator are applied to obtain a new fuzzy set. Two of the most commonly used implication methods are the minimum, which truncates the consequent’s membership function, and the product, which scales it. (Step 2. Obtaining each rule’s conclusion)

55

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

In the step 3, we combine the outputs obtained for each rule in step 2 (obtain conclusion) into a single fuzzy set, using a fuzzy aggregation operator. Some of the most commonly used aggregation operators are the maximum, the sum and the probabilistic sum (Step 3. Aggregate conclusions) In the last step, defuzzification, when trying to solve a decision problem, It is desired that the output to be a number (crisp value) and not a fuzzy set. We need to transform the fuzzy set we obtained in step 3 into a single numerical value. One of the most popular defuzzification methods is the centroid, which returns the center of the area under the fuzzy set obtained in step 3. There are many defuzzification methods which are briefly defined as the transformation of a fuzzy set into a numerical value (Wang 2007, Kaur and Kaur 2012).

4.4. Artificial Neural Networks (ANN) Many papers and books provide a detailed description of the ANN (Haykin 1994; Hagan et al. 1996; Ham and Kostanic 2001; Sonmez et al. 2006a; Ceryan et al. 2013a). In this study, a feed forward neural network structure was used, and a brief description of the method is provided herein. The most commonly used neural network structure is the feed forward hierarchical architecture. Feed forward neural network (FFNN) has a parallel and distributed processing structure. It is composed of three layers. Each layer in a network contains sufficient number of neurons depending on the specific application. The neurons in a layer are connceted to the neurons in the next successive layer and each connection carries a weight (Atkinson and Tatnall, 1997). The basic concept of the ANN is that they are typically made up of neurons. And in ANN, the neurons are organized in the form of layers (Figure 3). Input layer, which is ued to present data to the network. In fact, the input layer receives the data from different sources. Hence, the number of neurons in the input layer depended on the number of input data sources. Hidden layer(s), which are used to act as a collection of feature detectors. In ANN algorithms, construction of network architecture requires both optimum number of hidden layers between the input and output layers and optimum number of neurons in each layer. This is one of the most important and difficult task, Since there is no unified theory for the optimal architecture (Shahin et al, 2009; Gullu and Ercelebi, 2007). The number of hidden layers and their neurons are often determined by trial and error. Output layer, which is used to produce an appropriate response to the given input. The output layer contains a single neuron representing elastic modulus. Figure 3. ANN structure used in this study (Huang and Wandstedt, (1998) modified)

56

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

There are also three important components of an ANN structure: weights, summing function and activation function. The importance and functionality of the inputs on ANN models are obtained with weights (W). So the success of the model depends on the precise and correct determination of weight values. The summing function (net) acts to add all outputs; that is, each neuron input is multiplied by the weights and then summed. After computing the sum of weighted inputs for all neurons, the activation function f (.) serves to limit the amplitude of these values. The activation functions are usually continuous, non-decreasing and bounded functions. Various types of the activation function are possible but generally log-sigmoid function is preferred in applications (Ham and Kostanic 2001). This activation function generates outputs between 0 and 1 as the input signal goes from negative to positive infinity (Eq. 18). f (.) ≅

1 1 + e −(.)

(18)

In addition to the structure and its components of ANN, the running procedure is also important which involves typically two phases; forward computing and backward computing. In forward computing, each layer uses a weight matrix (W (v), for v =1, 2) associated with all the connections made from the previous layer to the next layer (Figure 3). The hidden layer has the weight matrix W (1) ∈ R x ∈R

nx 1

hxn

, the output layer’s weight matrix is W (2) ∈ R

, the output of the hidden layer x out ,1 ∈ R

hx 1

mxh

. Given the network input vector

can be written as Eq.19

x out,1 = f (1)[net (1) ] = f (1)[W (1)x ]

(19)

which is the input to the output layer. The output of the output layer, which is the response (output) of the network y = xout ,2 ∈ R

mx 1

, can be written as Eq. 20

y = x out ,2 = f (2)[net (2) ] = f (2)[W (2)xout ,1 ]

(20)

Substituting (Eq.17) into (Eq.18) for xout,1 gives the final output y = xout,2 of the network as y = f (2)[W (2) f (1)[W (1)x ]]

(21)

After the phase of forward computing, backward computing which depending on the algorithms to adjust weights is used in the ANN. The process of adjusting these weights to minimize the differences between the actual and the desired output values is called training or learning the network. If these differences (error) are higher than the desired values, the errors are passed backwards through the weights of the network. In ANN terminology, this phase is also called the back-propagation. Once the comparison error is reduced to an acceptable level for the whole training set, the training period ends, and the network is also tested for another known input and output data set in order to evaluate the generalization capability of the ANN (Ham and Kostanic 2001).

57

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

Depending on the techniques to train ANN models, different back propagation algorithms have been used for modeling of UCS. These modeling studies generally include the standart feed forward back propagation (FFBP) algorithms such as gradient-descent (Yilmaz and Yuksek 2008; Sarkar et al. 2010), gradient-descent with momentum rate (Singh et al. 2001; Kahraman et al. 2010; Yilmaz and Yuksek 2009), conjugate gradient (Canakci and Pala 2007) etc. As the standart FFBP algorithms have some disadvantages relating to the time requirement and slow convergency in training, Levenberg-Marquardt algorithms, which are alternative approaches to standart FFBP algorithms, were also used in some engineering applications (Meulenkamp and Grima 1999; Tiryaki 2008; Fistikoglu and Okkan 2011; Okkan 2011). The running procedure typically involves two phases: forward computing and backward computing. In forward computing, each layer uses a weight matrix associated with each connection from the previous layer to the next layer (Ceryan et al. 2013a). The hidden layer has the weight matrix Wij and the activation function f (1); the output layer has the weight matrix Wjm and the activation function f (2). Given the network input vector x ∈ R of the network y ∈ R

mx 1

nx 1

, the output from the output layer, which is the response (output)

, can be written as follows Eq.22.

   m   n ym = f (2) ∑  f (1) ∑ x i Wij + bj  Wjm + bm    j =1   i =1     

(22)

In this study, three widely used transfer functions, namely tangent sigmoid, linear, and log-sigmoid, were evaluated in trial experiments. After the forward computing phase, backward computing, which depends on the algorithms to adjust the weights, was performed. The process of adjusting these weights to minimize the differences between the actual and desired output values is referred to as network training or learning. If the differences (errors) are greater than the desired values, the errors are passed backwards through the weights in the network. In ANN terminology, this phase is also referred to as the back-propagation algorithm. Depending on the techniques used to train the feed-forward neural network models, different backpropagation algorithms have been developed. In this study, the Levenberg-Marquardt back-propagation algorithm (Ceryan et al. 2013a) was used for training. In this study Levenberg-Marquardt algorithm was used for training. This algorithm is a second order nonlinear optimization technique that is usually faster and more reliable than any other standart back propagation techniques (Meulenkamp and Grima 1999; Tiryaki 2008; Fistikoglu and Okkan 2011; Okkan 2011, Okkan and Dalkilic 2011). It represents a simplified version of Newton’s method (Marquardt 1963) applied to the training ANN (Hagan and Menhaj 1994). Consider ANN structure shown in Figure 3, the running of the network training can be viewed as finding a set of weights that minimized the error (ep) for all samples in the training set (Q). If the performances function is a sum of squares of the errors as Eq. 23. E (W ) =

58

1 P 1 P 2 ( d − y ) = (ep )2 , P = mQ ∑ ∑ p p 2 p =1 2 p =1

(23)

 Prediction of The Uniaxial Compressive Strength of Rocks Materials

where Q is the total number of training samples, m is the number of output layer neurons, W represents the vector containing all the weights in the network, yp is the actual network output, and dp is the desired output. When training with the Levenberg-Marquardt optimization algorithm, the changing of weights ΔW can be computed as follows ∆W = −[J kT J k + µk I ]−1J kT ek

(24)

where J is the Jacobian matrix, I is the identify matrix, µ is the Marquardt parameter which is to be updated using the decay rate β depending on the outcome. In particular, µ is multiplied by the decay rate β (06) earthquake along the segment is about 2000 years. The Gökçeyazı segment is capable of sourcing an M=6.95 earthquake (Sözbilir et al., 2016) according to empirical relationships between magnitude (Mb) and fault length (cf. Wells & Coppersmith, 1994). Ayşebacı trench performed on the Kepsut segment (Figure 19) provides evidence for three events. First event has occurred before 599±49 AD and can be correlated with 160 or 253 AD earthquakes in the historical records. There is no record of any large earthquake in the Balıkesir area which can be correlated with the penultimate event observed in the trench, however, the latest event can be attributed to the 1897 earthquake, which is the latest large earthquake record in the Balıkesir area (Table 5). Based on the paleoseismology results along the segment, the recurrence interval of surface ruptured (M>6) earthquakes is calculated as about 1000 years, and the time elapsed since the last surface rupture along the segment is 118 years. According to empirical relationships between magnitude (Mb) and fault length

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 Criteria for Surface Rupture Microzonation of Active Faults

Table 5. List of recorded historical earthquakes in the Balıkesir region No

Date

Coordinate Lat. (N)/Long. (E)

Locality affected

I

M

Reference

1

160

40.00/27.50

Yenice, Gönen, Biga

-

MS: 7.1

6, 8

2

253

39.10/27.15

Bergama

IX

-

1, 3

3

17.07.1296

39.10/27.45

Bergama, Dikili, Soma, Balıkesir

VII

MW: 6.9

1, 5

4

21.09.1577

39.70/27.70

Balıkesir

?

-

4

5

10.10.1688

39.65/27.80

Balıkesir

VIII-IX

MW: 6.6

7, 2

6

08.02.1826

39.50/28.00

Balıkesir

VIII

-

1, 3

7

10.08.1870

39.90/27.30

Balıkesir and Çanakkale

VII

MW: 5.8

1, 3

8

?.12.1897

39.60/27.90

Balıkesir

VIII

-

1, 3, 2

9

28.02.1898

39.60/27.90

Balıkesir

VIII

MW: 6.9

3

References: (1) Ergin et al. (1967); (2) Shebalin et al. (1974); (3) Soysal et al. (1981); (4) Ambraseys & Finkel (1995); (5) Ambraseys & Jackson (1998); (6) Ambraseys (2002); (7) Papazachos & Papazachou (2003); (8) Ambraseys (2009). I– intensity M– magnitude. MW values are taken from Stucchi et al. (2013).

(cf. Wells & Coppersmith, 1994) suggest that Kepsut segment is clearly capable of sourcing an M=6.7 earthquake (Sözbilir et al., 2016). Paleoseismological findings from the trench studies reveal that the earthquake recurrence interval of the segments of the HBFZ is ~1000 years and unfortunately irregular (Sözbilir et al., 2016). In view of the fact that there was no surface ruptured earthquake for the last 2000 years, an important seismic gap on the Gökçeyazı segment has been stated. Therefore, this segment needs to be considered potential for a future earthquake. For that reason, a fault-avoid zone should be declared for the Gökçeyazı and Ovacık segments, and for the Ayşebacı segment as a surface microzonation of active faults for future earthquake hazards in urban areas (Figure 23).

FUTURE RESEARCH DIRECTIONS Since we are obliged to produce scientific data to ensure that even a living thing does not die because of an earthquake disaster, microzonation of surface rupture along the active faults appears as a vital problem during the accelerated urbanization. Progress has been made in this problem with support of some governments, such as USA (Christenson et al., 2003), New Zealand (Kerr et al., 2003), Turkey (Gökçe et al., 2014; Sözbilir et al., 2016), and Italy (Working Group MS, 2008). Although, that kind of research initiatives gives us courage to study on prediction and/or reduction of earthquake hazard, many key uncertainties remain regarding the geologic and paleoseismologic data within the urban areas. Under the light of related literature in this chapter and our experience on active faults, in Sections 2 and 3, we build up a framework for evaluating of microzonation criteria along a damage zone due to the surface faulting in an urbanized area. Due to the facts that the most significant earthquakes have been occurred on the strike-slip and normal faults, it’s clear that the damage zones related with these faults are well studied and therefore, well described. On the other hand, most of the active significant reverse faults on the Earth are buried under water or blind. Hence, the microzonation along the reverse faults is still

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 Criteria for Surface Rupture Microzonation of Active Faults

Figure 23. Implications of paleosesmic trench results on active fault microzonation based on national criteria of Gökçe et al. (2014). Please see text for building setback distance and fault hazard overlay along the faults.

poorly defined, and needs further scientific attention. Therefore, our first recommendation on microzonation issue is improving our understanding of reverse faults with more scientific surveys and more solid geological/geophysical data. So, here we address the future studies to focus on microzonation of reverse faults to improve estimates of rupture dimensions, magnitudes, and damage zones of related earthquakes.

CONCLUSION In general, there are three reasons for earthquakes with magnitude larger than 6 to be destructive on man-made structures; (1) settlement on unstable soil, (2) constructing poor quality structures, (3) settlement directly on surface rupture. Today, unstable soil can be improved and poor quality structures can be made resistant to destructive earthquake. However, there is still no cure for settling directly on a surface rupture during a strong earthquake. For that reason, lots of studies have been carried out in order to find a solution for the settlement to avoid from surface rupture hazards. This chapter focused on criteria for establishing surface rupture microzonation that we should be avoided from during a destructive earthquake in terms of settlement.

219

 Criteria for Surface Rupture Microzonation of Active Faults

As the classification of active faults changes from country to country, the surface rapture and related microzonation maps are also various. It is not easy to set fixed standard rules on this, the faults in the world that need to be microzonation mapped are Holocene (10,000 years >) in age. But, some Quaternary faults classified as potential active fault in some countries must be evaluate as “restricted area” rather than “building setback”, considering the importance of building will be constructed. To determine the pre-historic and historic activities of important structures, paleoseismology studies should be done on all active faults in the world. For example, it is aimed in The National Earthquake Strategy and Action Plan of Turkey that all active faults in the country are going to determine with the paleoseismology studies until 2023. Palaeoseismological studies in Turkey are revealed that the segments of the North Anatolian Fault have a dramatic recurrence interval of surface ruptured events. While the northern branch is ranging between 150 and 350 years (Ikeda et al., 1991; Rockwell et al., 2001 and 2009; Hartleb et al., 2006; Kozacı et al., 2009 and 2011; Özaksoy et al., 2010), different intervals have been calculated for the fault segments of other branches, like; Düzce fault 400–500 years (Sugai et al., 2001), Yenice Gönen fault ~650 years (Kürçer et al., 2008), Ladik fault 600–900 years (Yoshioka et al., 2000). Additionally, recurrence interval is calculated as 100–360 years for the East Anatolian Fault (Çetin et al., 2003; along the Palu–Hazar fault), ~650 years for the Dead Sea Fault Zone (Akyüz et al., 2006; along the Hacıpaşa fault), 250–1900 years for the Büyük Menderes Fault Zone (Altunel et al., 2009), 1000–2000 years for the HBFZ (Sözbilir et al., 2016), and ~10000 years for the Tuzgölü Fault (Kürçer, 2012). Apart from Turkey, only the New Zealand is generated this kind of criteria in the world, and are officially using it. But, the recurrent periods of the faults in New Zealand are higher. Therefore, they are using a slightly different classification for active fault definition. For example, Kerr et al. (2003) suggests 2000 years for reoccurrence interval, and 125000 years for announcing the activity of fault. Because of this, the present study suggests a new classification (Table 6) based on deformational characteristics and pattern for official regulation in Turkey. According to this classification (Table 6); • • •

Due to the fact that the time after the last surface ruptured earthquake (1897 AD) is 120 years, and estimated recurrence interval of surface-ruptured earthquakes is about 1000 years, the Kepsut segment is classified as Class C. Palaeoseismologic trench data yield a recurrence interval of 1043 and 1136 years for the Ovacık fault segment. But the time after the last surface ruptured (1296 AD) earthquake is 721 years; therefore, this segment should be evaluated as Class B. Knowing that the recurrence interval of segment is calculated as 1000 years, the Gökçeyazı segment of the HBFZ is silence since 2000 years. Hence, this segment should be considered as a seismic gap, and has to be qualified as Class A. These results strongly suggest that the surface rapture microzonation results of this study must be implicated, and a fault-avoid zone should be declared for the HBFZ, immediately.

At the end, this study evaluated all active fault classification criteria used in active tectonic studies and prepared geological maps enriched with seismologic, geologic, geodetic and solid paleoseismological data in order to achieve the best results. In order to identify active faults in a given region, following steps have to be taken: (i) preparation of a geological map at correct scale, (ii) determination of macroand micro-seismic activity, (iii) geodetic data should be acquired and monitored; (iv) performance of trench-based paleoseismological studies on major faults and especially their branches if they deform 220

 Criteria for Surface Rupture Microzonation of Active Faults

Table 6. Building occupancy classification and its application for construction on active faults considering recurrence interval of fault (modified from Kerr et al., 2003; King et al., 2003; Van Dissen et al., 2003; Official Gazette of Turkey, 2007; Langridge & Ries, 2016). Building Code and / Importance Coefficient (I)

Building Occupancy Classifications

Critical buildings for governance continuity or industrial structures containing dangerous or explosive substances Buildings where people stay for long time & densely, and valuable materials are stored Buildings where people stay for short time but densely Others buildings

Hospitals & health centres, fire brigade buildings and facilities, communication facilities, access stations and terminals, energy production and distribution facilities, municipality management buildings, Disaster planning settlement

1a

Toxic, explosive or flammable material production buildings or related warehouses

1b

Schools, other educational buildings and facilities, dormitories, military structures and prisons, etc.

2a

Museums

2b

Sport complexes and facilities, cinemas, theatres and concert halls, etc.

3

Residences, houses, civil institutions, hotels, industrial factories, etc.

4a

Temporary buildings, simple farm houses, bungalows, storages and shacks, etc.

4b

1.5

1.4

1.2

1.0

DEVELOPED AND/OR ALREADY SUBDIVIDED SETTLEMENTS Active Fault Class

ARI or ARI–LSE* (year)

A

3000

3000-1000

1000-300

300-50

600 m/s corresponds to very hard soil. Soil classification is carried out and it is observed that most of the sites consist of very Figure 9. Surface anomaly map of Vs2, Vs10, Vs30 and Vp2,Vp10, Vp30

268

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

dense soils and soft rocks with velocity of 500 m/s. Most of the sites do not show the VS > 600 m/s up to 30 m depth, which indicates deeper bed rock at the study locations. Seismic zonation studies in Quaternary alluvial deposits of the Denizli city were implemented by the non-invasive surface wave methods. By this study, sediment conditions were determined and the variations of the velocity throughout the soil profiles were characterized by the Multi-Spectral Analysis of Surface Wave Method at 195 locations. The active surface wave method gave compatible results with each other especially in terms of Vs2, Vs10, Vs30 and Vp2, Vp10, Vp30 values. This allowed checking of the reliability of the seismic survey results and enabled to assign shear wave velocity values to the corresponding layers. Therefore, the depositional environments and their products could be identified.

Magnetic Susceptibility (MS) Randomly selected samples were collected from especially weathering parts of Harşit granitoid that crop out nearby Doğankent (Giresun NE Turkey). While center of the Harşit pluton made of granotoid in composition, quartz diorite, quartz monzonite, quartz monzodiorite, leucogranite are found contacts of the batholith (Figure 10). In selected samples places of weathering are gradual and have enough thickness to determine easily their rock material and mass properties. Studied samples collected from those places have original rock structure with uncollapsed material and then weathering developed in a constant volume. Measurements and experiments were carried out fresh rocks to highly weathered materials then cores were taken from the same place was used for laboratory MS measurements. Essentially unaltered granitic rocks were ranged from mafic diorite, tonalite through granotoid, monzogranite to syenogranite. These rocks are composed of hornblende, biotite, magnetite, plagioclase, feldspar, and quartz. Previous studies showed that geochemical analyses are expensive and also time consuming process relative to MS measurements. We think that there is a need to develop petrographic distribution-MS calibration curves to avoid expensive geochemical measurements. The purpose of this Figure 10. Map of the location of the study area and geological map and units of Harşit Granotoid, NE of Türkiye

269

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

study then is to investigate the relation between measured MS and mineralogy by using Harşit granotoid intrusion samples of southern Giresun (Figure 10). Field MS measurements were performed by using search loop type sensor MS2D of Bartington susceptibility measurement system with 2x10-6 SI sensitivity, operating frequency at 0.958 kHz. Search loop was designed for rapid assessment of the concentration of ferromagnetic paramagnetic or diamagnetic minerals in the top 100 mm (approximately) of the granotoid surface. Field measurements were made using an instrument that employs a small coil that is placed against the rock surface during measurement. Results were reported relative to sample volume in SI or CGS. At each locality of the field, two MS values were recorded with the Bartington MS2D coil, and mean value was recorded. During measurements a good contact between coil and surface of granotoid was provided. A total of 1033 field measurements were taken from the surface of the granotoit pluton which covers about an area of 18 km2. A total of 78 granotoid samples were collected. Numbering and location of the samples are shown in Figure 11. MS measurements and Modal Analysis conducted on these rock samples were counted. Also, 40 cores of 78 samples from the granotoid intrusion were analyzed for modal mineral ration (Gedikoğlu, 1978; Köprübaşı, 1992), and MS values were measured for the samples taken from the same places. In order to investigate magnetic features of granotoid outcrops, Modal Analysis measurements were carried out on the rock cores by using a magnetic susceptibility meter MS2, Bartington Instruments Ltd., linked to MS2B dual frequency sensor at 0.470 and 4700 kHz. Laboratory measurement employed an instrument custom built specifically for small cylindrical or cubic samples. This instrument was also capable of measuring diamagnetic samples with good precision. Results of MS measurements were reported relative to sample mass in SI, Modal Analysis of the Harşit granotoid was determined to characterize source rock composition and to facilitate the interpretation of the trace element and isotopic data. Standard petrographic thin-sections of thirty-eight samples were used for point counting. Counts varied from 800-1600 data points depending on the grain size of the granotoids sample. Different results were taken from the Harşit Granotoids’s Modal Analysis and MS measurements. Granotoid is discriminated six groups by the results from Modal Analysis, which are sorted such as; diorite, granite, granodiorite, quartzdiorite, quartz monzodiorite, leucogranite, tonalite; it could be said to the I-type granotoid for including magnetit. MS values of the Harşit granotoid are in general high, in the order of 10-2-10-3, hence corresponding to the typical values of the I-type granotoid. MS measurements from leucogranite have susceptibilities lower, in the order of 150-300x10-6 SI. MS values of core samples are between 150 to 1250x10-6 SI. These values typify high MS granotoids, and suggest that the susceptibility is produced by ferromagnetic minerals (Rochette, 1987). The petrographic studies of Harşit granotoid samples revealed that magnetite was the main ferromagnetic contributor. Therefore, the map showing the spatial variation of MS in (Figure 11) mainly MS reflects the magnetite content and petrographic boundaries. Two MS anomaly at north and south of investigation area reach anomalously high values, of 1150 and 1250x10-8 SI, respectively. The MS zonations of cores show minimums in the south part of the area, gradually increasing towards north of the granotoid. This distribution agrees with petrographic observations from thin sections, which reveal a decreasing in quartz ratio content from the high MS values to the low values, at the both high MS values where the magnetite ratio is more abundant. There is good correlation between the MS distributions and plagioclase ration. These relationships viewed very poorly on the other mineral ratios of the granotoid. The Harşit granotoid displays most features of I-type granites and are characterized by ferromagnetic minerals, such as magnetite, paramagnetic minerals, such as biotite, muscovite and ilmenite. The field MS magnitudes of the Harşit quartz monzodiorite (1030 to 1250x10-6 SI) are consistent with those of 270

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

Figure 11. Petrographic, core susceptibility and elements maps of the units of Harşit Granotoid, NE of Türkiye

271

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

paramagnetic minerals; biotite, more abundant paramagnetic mineral, is presumably at the origin of the MS. The principal contributor of the paramagnetic minerals for the MS is true. The ferromagnetic behavior of the granotoid had also been related by the quantitative contribution of each mineral to the rock MS and the quantitative contribution of magnetite to the rock susceptibility is larger than 50% in most samples (Ferre & Ameglio, 2000). Thin section analysis of sides of granotoid indicate that the magnetite occurs either as very small grains scattered in the matrix of the granotoid samples, seldom and small grains associated with iron-bearing silicates (biotites and amphiboles). Magnetite forms irregular or sub-ellipsoidal grains. Magnetite minerals located in the center of granotoid contained in fracture planes and characteristic of approximately 0.41-3.08% of the sites. Variations in MS magnitudes from one site to another are very slowly. This is very usual phenomenon in magnetite bearing granites due to the fact that, as each grain of magnetite has a high MS. Characterization of such as granotoid plutone dominated effected by lithogenic and atmospheric sources was possible. The main MS anomaly is situated in the pluton where composed units show increased values towards west of the granotoid. The given maps and graphs demonstrate a visual connection between MS of granotoid, Modal Analysis amount of rocks and may be atmospheric effects (Figure 11 and Figure 12). These results conforms the applicability of MS mapping as a powerful method to investigate the lateral chemical and climatic variations of such as plutones. It is very important for marble sector that the setting of weathering and petrographic boundaries of granitic rocks. Also, this information is very important for detected quality boundaries such economical rock. This study gave to us these valuable informs. The Harşit Granotoid has high MS values (MS from 100 to 1250x10–6 SI) and MS are primarily carried by magnetite, biotite and ferromagnetic minerals. Most MS values of the Harşit Granotoid are high and fall within the domain of weakly magnetic or ferromagnetic granotoid which are equated to the I-type granites. This is in apparent harmony with the results Figure 12. The relationship between MS and weathering degree of the Harşit Granotoid. Dots show the points of the field MS measurements

272

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

of the MA, which identified I-type granotoid. MS and MA may indicate that the alteration of magnetite took place sides of granotoid, likely in the magmatic stage of the granite development. Petrographic boundaries of the granotoid are shown very easily helping MS signature in the core samples and field measurements. This signature was used in Harşit Granotoid to narrow the search for limiting the quality rock boundaries. MS values of the rock vary dependent on MA in the rock composition.

GPR (Ground Penetrating Radar) It is use this method over the very important water sources of Denizli city which are Kozlupınar and Bentpınar springs where take place between Pamukkale University and the Bağbaşı village have been investigated to find the feeding directions of groundwater and to obtain the fresh water for near counties. Those springs are the East part of the Aegean horst and graben systems (Figure 13). GPR method was carried out about 6.4 km horizontally using georadar. As a result of hydro-geological and geophysical studies, affective feeding area of the springs is investigated in the area. This study may be the good example to investigate the feeding direction and the location of the springs for similar type of projects. Kozlupınar and Bentpınar Springs are located between Pamukkale University and Bağbaşı village. Those springs take place at the east part of the Aegean horst and graben systems,in the drainage systems of Gökpınar Dam and on the top of the formations as slope debris and some deposited cones which are located along the north slopes of Bağbaşı village that are much higher as topographically at southern mountainous parts. In the study area the talus composed of block, gravel, sand, silt and clay sized, are rather angled and heterogenous materials observed at south land of Kınıklı, Bağbaşı and Tekkeköy. They are irregular and unatteched drifts which are developing due to the fault zone. The type of the materials that are forming the talus is changing according to the bedrock which they broken from. The grain sizes decrease while going to the north and they create a graded topography at the same direction. The alluvial fan unit composed of block, gravel, sand, silt and clay materials which are moved from the creeks that take place at southeast and southwest of Bağbaşı city. The unit at the bottom is with medium tightness and Figure 13. The geology map and GPR profiles (blue), location map of DES points and profiles (white)

273

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

not attached in top levels. The end faults bordering the mountains which surround the study area from south are active seismically. And it is characterised by the Babadağ fault which is forming the south border of Denizli rift. Babadağ fault is the component element of block faulting system. It is seismically active and according to this the tectonic drift still continues today. In Figure 14, it is given the geology map and GPR profiles (blue), location map of DES points and profiles (white). An interpreted example of a GPR record of study area is given in Figure 13. Primarily in geophysical studies, the GPR profile records are taken in total length of 1125 mt. at the parts that are predicted as shallow from 25 mt. at the surroundings of Kozlupınar and Bentpınar springs and according to those GPR datas, the level of the ground water was determined. The hydrohips map and groundwater isobath map were drawn belonging to groundwater of springs and surroundings (Figure 15). The geophysical fieldworks show that, the feeding of ground water that forms the springs by falls, depends on the position of the weakness zones which are the products of tectonic activity. In other words, the movement of very rich ground water reserves are controlled by especially Babadağ fault and some other faults, cracks, fractures having different shape and size growing as parallel or bias to that fault. The aquifer like units, which are feeding the springs and spreading away from the study area to wider areas,are observed as heterogen and anisotrop because of different origin and tectonism. The hydrohips which show the location and movement of ground water expand by the position of buried faults and shaping the aquifer like units. The predicted feeding direction of surroundings of the springs is primarily south –southwest and west and east. According to the datas of geophysical studies the most intense feeding take place at the slopes where there is no residential area in the southwest direction. There is Figure 14. An interpreted example of a GPR record of study area

274

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

Figure 15. Hydrohips map of groundwater of springs and surroundings (Arrows show the movement direction of ground water) an isobath map of groundwater

no evidence escape the topographic slope to support the existence of strong ground water which feed the springs in the east direction. In this case; in order to maintain some legal process for the springs and surroundings, the strong prevention must be taken at mountainous zone in south-southwest parts and at the slopes of these mountainous zone in the south. A GPR cross section of an active shallow fault line is traced at SW Denizli in young sediments (Figure 16). GPR is a very effective to detect it if the lithology is being changed.

4. FUTURE RESEARCH DIRECTIONS The most essential concern for employing the geophysical methods is matching the data with geological structure. It means a geophysicist has to understand the geological processes and their consequences and Figure 16. GPR crosssection of a shallow fault

275

 Geophysical Surveys in Engineering Geology Investigations With Field Examples

on the other hand a geological/geotechnical engineer should recognize the limitaions of the equipment and/or method. For instance GPR should not be operated if the area is covered by a thick clay layer. The future developments in electronics will definitely improve the efficieny of the geophysical methods. The increased penetration depth and resolution, equipment size and more geological/engineering parameters of geophysical instruments will make the methods more effective, popular and practical. The computer applications both to acquire and assess the data will continue to grow.

5. CONCLUSION Geophysical tecniques applied by geophysic engineers are becoming popular in the geology, environmental, archeological and civil engineering fields. A geophysical study in an engineering geological research targets to visualise discontinuities in the rock masses, physico-mechanical properties of soils and rocks, groundwater level, faults, landslides etc. Additionally the static and dynamic geotechnical parameters of soils and rocks are possible by such studies and these can be achieved relatively in short times and economically. The popular geophysical methods are seismic, magnetometric, vertical electrical sounding (VES), Very Low Frequency (VLF) Electromagnetics methods and ground penetration radar (GPR). Weathering zones are recognized by lower wave propagation velocities, whereas the resistivity may differ depends on fluctuating hydrogeological conditions. The employed geophysical method in groundwater exploration may be different depends on the underground formations, hydrologic cycle, groundwater quality, and number and type of aquifers. Surface geophysical methods, especially electrical and electromagnetic techniques are also commonly employed the groundwater investigation methods. Some case studies given in the text show that the explained methods are effective in engineering geology/geotechnical engineering applications. However, the users should recognize the limitations of the methods and/or equipment. The developments in the technology will move the efforts one step forward in future.

REFERENCES Akyol, E., Kaya, A., & Alkan, M. (2016). Geotechnical land suitability assessment using spatial multicriteria decision analysis. Arab J Geosci, 9(7), 498. doi:10.1007/s12517-016-2523-6 Akyol, E., Tasdelen, S., & Aydin, A. (2015). Adverse Effects of Soil Grouting on Sandy Soils. Applied Mechanics and Materials. Altunel, E. (1996). Pamukkale travertenlerinin Morfolojik Özellikleri, Yaşları ve Neotektonik Önemler. M. T. A. Derg., (118), 47-64. Aydın, A., Akyol, E., Gungor, M., Soyatik, N., & Tasdelen, S. (2015). Seismic Microzonation for Denizli Metropolitan Area, Turkey. In Applied Mechanics and Materials (Vol. 802, pp. 40–44). Trans Tech Publications. Aydın, A., & Ceryan, S. (2011). Assessment of Petrophysical Surface Data of the Hars¸it Granitoid in Trabzon, in Northeastern, Turkey. Pure and Applied Geophysics. doi:10.1007/s00024-011-0294-2

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Aydın, A., Güngör, M., Taşdelen, S., & Akyol, E. (2016). Investigation of hydrodynamic and tectonical properties using the geophysical methods Gokpinar and Derindere (Denizli) springs. World Multidiciplinary Earth Sciences Symposium, Abstract collection book. Aydın, A., & Taşdelen, S. (2010). Kozlupınar ve Bentpınarı (Bağbaşı-Denizli) Kaynakları Hidrodinamik Özelliklerinin Jeofizik İncelemesi. 19. Uluslar Arası Jeofizik Kongre ve Sergisi Genişletilmiş Özet CD si, 57, Ankara. Aydın, A., Tasdelen, S., & Denis, T. (2011). Investigation of the Geological Structures of Kozlupinar and Bentpinar (Bağbaşi-Denizli) Water Source System by Using by Geoelectric and electromagnetic methods. The 6th Congress of Balkan Geophysical Society, Budapest, Hungary. Aydın, A., Yağız, S., Özpınar, Y., & Semiz, B. (2005). Investigation of travertine properties using geophysical methods. Proceedings of 1. International Symposium on Travertine, 305-308. Benn, K., Ham, N. M., Pignotta, G. S., & Bleeker, W. (1998). Emplacement and deformation of granites during transpression: Magnetic fabrics of the Archean Sparrow pluton, Slave Province, Canada. Journal of Structural Geology, 20(9-10), 1247–1259. doi:10.1016/S0191-8141(98)00065-0 Benn, K., Rochette, E., Bouchez, J. L., & Hattori, K. (1993). Magnetic susceptibility, magnetic mineralogy and magnetic fabrics in a Late Archean granitoid-gneiss belt. Precambrian Research, 63(1-2), 59–81. doi:10.1016/0301-9268(93)90005-M Borradaile, G. J., & Henry, B. (1997). Tectonic applications of the magnetic susceptibility and its anisotropy. Earth-Science Reviews, 42(1-2), 49–93. doi:10.1016/S0012-8252(96)00044-X Borradaile, G. J., & Werner, T. (1994). Magnetic anisotropy of some phyllosilicates. Tectonophysics, 235(3), 223–248. doi:10.1016/0040-1951(94)90196-1 Ceryan, S. (1999). Harşit Granitiyidi’nin ayrışması, sınıflandırılması ve ayrışmanın mühendislik özelliklerine etkisi (PhD Thesis). KTÜ. (in Turkish) Ceryan, Ş., Ceryan, N., & Aydın, A. (2005). Determination of weathering in engineering time using interaction matrices. Proceedings of 1. International Symposium on Travertine, 297-304. Dekkers, M. J., & Rochette, P. (1992). Magnetic properties of CRM in synthetic and natural goethite: Prospects for a NRM/TRM intensity ratio test to assess the paleomagnetic stability for goethite. Journal of Geophysical Research, 97, 17291–17308. doi:10.1029/92JB01026 Ellwood, B. B., MacDonald, W. D., Wheeler, C., & Benoist, S. L. (2003). The K-T boundary in Oman: Identified using magnetic susceptibility field measurements with geochemical confirmation. EPSL, 206(3-4), 529–540. doi:10.1016/S0012-821X(02)01124-X Ferre, E. C., & Ameglio, L. (2000). Preserved magnetic fabrics vs. annealed microstructures in the syntectonic recrystallised George granite, South Africa. Journal of Structural Geology, 22(8), 1199–1219. doi:10.1016/S0191-8141(00)00026-2 Gedikoğlu, A. (1978). Harşit Granitik Karmaşıklığı ve Çevre Kayaçları. Doçentlik Tezi. Trabzon: KTÜ Yerbilimleri Fakültesi. (in Turkish)

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Gungor, M., Aydin, A., Akyol, E., & Tasdelen, S. (2015). The Relations between Seismic Results and Groundwater near the Gokpinar Damp Area, Denizli, Turkey. World Academy of Science, Engineering and Technology International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 9(9). Gurel, H. (1997). Kaklık-Yokuşbaşı-Belevi(Denizli) Yakın Çevresinin Jeolojik İncelemesi. PAÜ., Fen Bilimleri Enstitüsü Yüksek Lisans Tezi (Yayınlanmamıştır), 74 s., Denizli. Hayashi, K. T., Inazaki, T., & Suzuki, H. (2005). Buried channel delineation using a passive surface wave method in urban area, International Institute of Seismology and Earthquake Engineering. Lectures Notes, 2005, 25. Ilki, A., Karadogan, F., Pala, S., & Yuksel, E. (2009). Seismic Risk Assessment and Retrofitting. London: Springer. doi:10.1007/978-90-481-2681-1 Inel, M., Ozmen, H. B., & Bilgin, H. (2007). Re-evaluation of building damage during recent earthquakes in Turkey. Engineering Structures, 30(2), 412–427. doi:10.1016/j.engstruct.2007.04.012 Kaymakci, N. (2006). Kinematic development and paleostress analysis of Denizli basin (w Turkey): Implications of spatial variation of relative paleostress magnitudes and orientations. Journal of Asian Earth Sciences, 27(2), 207–222. doi:10.1016/j.jseaes.2005.03.003 Köprübaşı, N. (1992). Aşağı Harşit Bölgesinin Magmatik Petrojenezi Ve Masif Sülfitlerde Jepkimyasal Hedef Saptama Uygulamaları (PhD Thesis). KTÜ. (in Turkish) Miller, R. D., Xia, J., Park, C. B., & Ivanov, J. M. (1999). Multichannel analysis of surface waves to map bedrock, Kansas Geological Survey. The Leading Edge, 1999(12), 1392–1396. doi:10.1190/1.1438226 Özkul, M., Alçiçek, M.C., Heybeli, H., Semiz, B., & Erten, H. (2001). Denizli Sıcak Su Travertenlerinin Depolanma Özellikleri ve Mermercilik Açısından Değerlendirilmesi. MERSEM 2001, Türkiye III. Mermer Sempozyumu Bildiriler Kitabı, TMMOB Maden Müh. Odası Afyon Temsilciliği, 57-72. Ozpınar, Y., Heybeli, H., Semiz, B., Koçan, B., & Baran, A. (2001). Kocabaş (Denizli) Travertenleri ve Kömürcüoğlu Travertenlerinin Jeolojik ve Petrografik İncelenmesi ve Bunların Teknolojik açıdan değerlendirilmesi. MERSEM 2001, Türkiye III. Mermer Sempozyumu Bildiriler Kitabı, TMMOB Maden Müh. Odası Afyon Temsilciliği, 133-152. Özpınar, Y., & Semiz, B. (2003). Kömürcüoğlu Traverten, 3k Traverten ve Diva Mermer Sahalarında Yapılan Sondajların Yorumlanması. Kömürcüoğlu Mermer’e Rapor. 29s. Park, C. B., Miller, R. D., & Xia, J. (1999). Multi-channel analysis of surface waves. Geophysics, 64(3), 800–808. doi:10.1190/1.1444590 Rochette, P. (1987). Magnetic susceptibility of the rock matrix related to magnetic fabric studies. Journal of Structural Geology, 9(8), 1015–1020. doi:10.1016/0191-8141(87)90009-5 Rochette, P., Jackson, M., & Aubourg, C. (1992). Rock magnetism and the interpretation of anisotropy of magnetic susceptibility. Reviews of Geophysics, 30(3), 209–226. doi:10.1029/92RG00733

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Shizhou, Y. U., Tamura, M., & Kouichi, H. (2008). Evaluation of Liquefaction Potential in terms of surface wave method. The 14th World Conference on Earthquake Engineering, Beijing, China. Stokoe, K. H., Wright, S. G., Bay, J. A., & Roesset, J. M. (1994). Characterization of geotechnical sites by SASW method, Geophysical Characterization of Sites (ISSMFE TC#10) by R.D. Woods (pp. 15–25). Oxford, UK: IBH Publ. Sun, R. S. (1990). Denizli-Uşak Arasının Jeolojisi ve Linyit Olanakları. İzmir, MTA Raporu, No 9985. Taşdelen, S., Akyol, E., & Çelik, S. B. (2015). Işıklı Beldesi (Denizli) Yerleşim Alanının Jeolojik ve Jeoteknik Özellikleri. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 3, 1–15. Taşdelen, S., Çelik, S. B., & Akyol, E. (2015). Irgıllı Beldesi (Denizli) Yerleşim Alanının Jeolojik ve Jeoteknik Özellikleri - Geological and Geotechnical Properties of Irgilli Town (Denizli). Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 22(3), 213–219. Taşdelen, S., Güngör, M., & Aydın, A. (2016). Underground lakes of Insuyu Cave (Burdur, Turkey). World Multidiciplinary Earth Sciences Symposium, Abstract collection book. Taşdelen, S., Güngör, M., & Aydın, A. (2016). Çukurköy (Denizli) Dolayının Sığ Yeraltı Suyu Hidrojeoloji İncelemesi. Pamukkale Üniversitesi. Mühendislik Bilimleri Dergisi, 22(3), 206–212. Xia, J., Miller, R.D., Park, C.B., Ivanov, J., Tian, G., & Chen, C. (2004). Utilization of high frequency Rayleigh waves in near surface geophysics. The Leading Edge, 23(8), 753–759.

KEY TERMS AND DEFINITIONS Denizli: Is an industrial city in the southwestern part of Turkey and the eastern end of the alluvial valley formed by the river Büyük Menderes, where the plain reaches an elevation of about three hundred and fifty metres (1,148 ft). Denizli is located in the country’s Aegean Region. Dynamic Geotechnical Parameters: Geotechnical investigations are performed by geotechnical engineers or engineering geologists to obtain information on the physical properties of soil and rock around a site to design earthworks and foundations for proposed structures and for repair of distress to earthworks and structures caused by subsurface conditions. Geophysical Tecniques: Exploration geophysics is an applied branch of geophysics, which uses physical methods, such as seismic, gravitational, magnetic, electrical and electromagnetic at the surface of the Earth to measure the physical properties of the subsurface, along with the anomalies in those properties. Ground-Penetrating Radar (GPR): Is a geophysical method that uses radar pulses to image the subsurface. This nondestructive method uses electromagnetic radiation in the microwave band (UHF/ VHF frequencies) of the radio spectrum, and detects the reflected signals from subsurface structures. Magnetic Tecniques: These tecniques are that dimensions, and amplitude of an induced magnetic anomaly is a function of the orientation, geometry, size, depth, and magneticsusceptibility of the body as well as the intensity and inclination of the earth’s magnetic field in the survey area. Seismic Tecniques: Reflection seismology (or seismic reflection) is a method of exploration geophysics that uses the principles of seismology to estimate the properties of the Earth’s subsurface from reflected seismic waves.

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The Groundwater Investigation: Groundwater Exploration project pass through various surveys. The main objective of these surveys is to study and understand the hydrological cycle of the region, to understand overall concept of type, nature, no: aquifers and quality of groundwater. Vertical Electrical Sounding (VES): Is a geophysical method for investigation of a geological medium. The method is based on the estimation of the electrical conductivity or resistivity of the medium. Electromagnetics methods. Weathering Zones: A distinctive layer of weathered material that extends roughly parallel to the ground surface. It differs physically, chemically, and mineralogically from the layers above and/or below. A broad distinction may be drawn between the weathering zones in drift, which are normally distinguished by degrees of oxidation and by carbonate content, and those on bedrock, which are usually separated according to the relative proportions of corestones and weathered matrix.

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Chapter 8

A New Acoustic EnergyBased Method to Estimate Pre-Loads on Cored Rocks Murat Karakus University of Adelaide, Australia

William Thurlow University of Adelaide, Australia

Ashton Ingerson University of Adelaide, Australia

Michael Genockey University of Adelaide, Australia

Jesse Jones University of Adelaide, Australia

ABSTRACT The Acoustic Emission (AE) due to the sudden release of energy from the micro-fracturing within the rock under loading has been used to estimate pre-load. Once the pre-load is exceeded an irreversible damage occurs at which AE signals significantly increase. This phenomenon known as Kaiser Effect (KE) can be recognised as an inflexion point in the cumulative AE hits versus stress curve. In order to determine the value of pre-load (σm) accurately, the curve may be approximated by two straight lines. The intersection point projected onto the stress axis indicates the pre-load. However, in some cases locating the point of inflexion is not easy. To overcome this problem we have developed a new method, The University of Adelaide Method (UoA), which use cumulative acoustic energy. Unlike existing methods, the UoA method emphasises the energy of each AE, the square term of the amplitude of each AE. As the axial pre-load is exceeded, the micro cracks become larger than the existing fractures and therefore energy released with new and larger cracks retain higher acoustic energy.

DOI: 10.4018/978-1-5225-2709-1.ch008

Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

INTRODUCTION Whilst there have been many methods used for locating the point of inflexion in Kaiser Effect (KE), all methods have some degree of ambiguity, generally arising from data which does not contain the ‘ideal’ characteristics. No studies had been conducted regarding the correlation between the corresponding acoustic emission (AE) energy and its proximity in relation to the rock’s maximum stress memory. The conventional method of evaluating the Kaiser Effect (KE) uses cumulative acoustic emission (AE) hits versus stress. The maximum pre-load is clearly observed by using this method on a sub-core oriented in the direction of the maximum pre-load whereas the confining pre-load was never visible. However, in a sub-core oriented in the direction of the confining pre-load, the AE technique has the ability to detect the maximum axial pre-load, confining pre-load or in some cases both loads on the same curve. Contrary to current literature on the application of the KE, it was demonstrated that the AE curve can give rise to multiple clear inflexion points, particularly in sub-cores oriented in the direction of the confining pre-load. We believe that larger cracks generating higher voltage are more closely associated with the KE inflexion point than smaller cracks which generate lower voltage AE. When plotting stress versus cumulative AE hits, it was postulated that assigning a weighting to each AE event, which is equal to the square of its voltage will simultaneously decrease the influence of AE events associated with ‘settling’ of the rock, whilst increasing the influence of large voltage AE events associated with new damage of the rock. This method, subsequently termed ‘The University of Adelaide (UoA) Method’ successfully estimated the maximum pre-load in all tests within 3% accuracy. Furthermore, this method was successful in both lateral and axial sub-cores, and therefore the orientation of the sub-core is believed not to influence the load being estimated, as it is the maximum axial load which is always prominent. The Felicity Ratio (ratio of the estimated stress to the pre-loaded stress) was used to measure the accuracy of the estimate. The average value of the Felicity Ratio (FR) of each estimate using the UoA method was equal to one, which demonstrated that the confining pressure had no influence on the estimation of the principal stress. Currently, no literature exists in relation to the occurrence of multiple inflexion points on an AE curve and the ability to identify multiple orthogonal pre-load stresses on a single AE curve. Therefore, it is recommended that a research could be based on the ability of the AE technique to detect multiple stress states on a single curve. It is also recommended that further study be carried out on rocks with multiple known stress histories, as this research would assist in clarifying the effect of time dependence in the KE, a matter which is subject to conflicting literature.

BACKGROUND Acoustic emissions are bursts of high frequency elastic waves emitted by the localised failure of a material when it is subjected to a loading (Villaescusa et al., 2002; Karakus, 2014; Karakus et al., 2015, 2015; Yuan and Li, 2008). The generation of AE within rock materials occurs when they are subjected to a stress of sufficient magnitude to induce pore compression and micro cracking (Seto et al., 1997; Yamamoto, 2009). Therefore, as a rock material is subjected to a stress that exceeds the maximum previously applied stress, irreversible damage occurs within the rock and the rock forms a new ‘stress memory’ (Lavrov, 2002; Kurita and Fujii, 1979; Karakus, 2014; Karakus et al., 2015; Yamshchikov et al., 1994).

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The phenomenon behind the use of the AE technique is that a lack of micro cracking, and accompanying AE activity, occurs when a rock material is loaded at levels below its previous stress state (σm) as illustrated in Figure 1 (Nikkhah et al., 2013; Villaescusa et al., 2002; Wood and Harris, 1991). At the previously ‘memorised’ maximum stress (σm), there is an abrupt increase in the level of micro cracking and collapsing of pores within the material as illustrated in Figure 1 (Karakus, 2014; Karakus et al., 2015; Lockner, 1993; Yuan and Li, 2008; Friedel and Thill, 1990; Villaescusa et al., 2002; Seto et al., 1997). This closure and propagation of fractures is associated with a significant increase in AE activity, signifying the phenomenon of the KE, first discovered by Joseph Kaiser in the mid-20th Century (Kaiser, 1953; Villaescusa et al., 2005; Karakus, 2014; Karakus et al., 2015; Seto et al., 1997; Lavrov, 2002). Since the 1950’s, the KE has been thoroughly researched and is well summarised by Yuan and Li (2008) as the absence of detectable AE until the supplied load exceeds the previously applied stress level. A number of researchers have investigated the AE technique within geomaterials since the 1970’s, with a general focus on the factors that influence the stress memory recollection of rocks (Seto et al., 1997; Villaescusa et al., 2002; Yamshchikov et al., 1994). Lavrov (2003) considered the influence of principle axis rotation, inefficient coring, the loading rate and time delay on the AE activity. Further he investigated the work of Rudnicki (2000) and concluded that when using AE stress measurement techniques in rocks, the in-situ rock is subjected to a true tri-axial stress state and therefore requires a tri-axial stress state to establish reliable in-situ stress predictions. Villaescusa et al. (2002) investigated the reliability of AE techniques when estimating in-situ stresses from cored rock samples under uniaxial loading conditions. Villaescusa et al. (2002) had similar conclusions to Tuncay and Ulusay (2008) in which the AE technique results recollected the in-situ stress values Figure 1.

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reasonably well in comparison to standard overcoring methods. According to Yuan and Li (2008), there is effectively no AE activity prior to exceeding the memorised stress value of a rock. However, Li and Nordlund (1993) reject the conclusions of Yuan and Li, instead identifying that there is substantial AE at the start of the load application, likely due to the settling of the rock fractures, that quickly diminishes as the load applied increases. Yamshchikov et al. (1994) indicated that the KE could be quantified within AE techniques through identifying the point of inflexion, corresponding to a sudden increase in cumulative AE hits, known as the ‘take off point’. Although AE techniques have been extensively studied (Lavrov, 2002; Kurita and Fujii, 1979; Karakus, 2014; Karakus et al. 2015; Villaescusa et al., 2002), significant challenges are still associated with the location of the ‘take off point’. Kent et al. (2002) and Sun et al. (1995) investigated the occurrence of the KE in a range of rock types and highlighted the difficulties in locating the ‘take off point’, particularly in ultramafic rocks where multiple peaks in AE activity lead to confusion in the identification of the in-situ stress as shown in Figure 2. Significant research has been conducted into the application of the KE within rocks, particularly focusing on its ability to accurately measure the in-situ stress state (Lehtonen et al., 2011; Holcomb, 1993a; Park et al., 2001; Seto et al., 1997; Villaescusa et al., 2002-2005). However, despite the recent developments in understanding the KE within AE, there remain a number of unanswered fundamental questions. There is a lack of knowledge surrounding what the KE truly represents in a heterogeneous Figure 2.

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rock mass, what the influencing factors are and how it can be reliably located. These unanswered questions form the basis for the aims of our research and associated literature review.

Rock Damage Mechanisms It is widely accepted that damage during uniaxial compression of a rock specimen is caused by the initiation, propagation and coalescence of microcracks, and the deformation and fracture processes within brittle rock have been extensively studied by Brace (1964), Bieniawski (1967), Wawersik and Fairhurst (1970), Lajtai and Lajtai (1974) and Martin and Chandler (1994). Brace (1964) and Bieniawski (1967) proposed a model to describe a rock’s damage process that divided the damage process into five distinct stages: crack closure, linear elastic deformation, crack initiation and stable crack growth, crack damage and unstable crack growth, failure and post peak behaviour (see Figure 3). Kent et al. (2002) slightly modified the model developed by Brace (1964) and Bieniawski (1967) when relating it to AE, deeming that the ‘crack closure’ phase was more accurately defined as the ‘bedding-in’ phase. Crack closure occurs when pre-existing fractures close. In other words, when the axial stress, σ, is less than the crack closure threshold, σcc, existing cracks within the rock mass begin to close together (see Figure 4). The crack closure stage finishes when all pre-existing cracks within the rock mass have closed. Kent et al. (2002) found that the identification of the KE was difficult during the ‘bedding-in’ phase of deformation. This was primarily due to the fact that subtle changes in the AE count were concealed by larger emissions emanating from the closure of voids between cracks. The extent of the AE generated Figure 3.

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Figure 4.

in the ‘bedding-in’ phase is highly dependent on the rock type being tested and its condition (Panisiyan et al., 1990). It has been identified that highly fractured rocks have a significantly prolonged bedding-in phase in comparison to more intact specimens. Similarly, Scholz (1968) demonstrated that within rocks, a high porosity generally corresponded to a high level of AE activity. Boyce et al. (1981) and Slatalla and Alber (2008) later supported these views when they proposed that the level of AE in the ‘crack closure’ phase represented the collapsing of pores, closure of existing micro cracks and rearrangement of mineral grains. Hsieh et al. (2014) disputed this theory, claiming that the increased AE activity in this initial phase was due to contact between the sample ends and loading platens. Surface roughness and free particles (residual particles after grinding) were thought to reduce the true area in contact with the end plates, in turn increasing the stress. The ‘crack initiation’ stage commences when the applied stress reaches a level sufficient to cause micro fracturing within the rock sample. This occurs when the stress exceeds the crack initiation threshold, σci. The lateral and volumetric strain curves are non-linear during this phase, as shown in Figure 4. Bieniawski (1967) later defined stable fracture growth as the propagation of fractures whilst maintaining a definitive relationship between crack length and applied stress. In the stable crack growth phase, Kent et al. (2002) determined that due to the movement of preexisting fractures and formation of new fractures, increased levels of AE are observed that tend to conceal the KE. However, in general, the stable crack growth region was found to produce a recognisable KE.

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Bieniawski (1967) identified that when the crack initiation relationship becomes redundant and the propagation of fractures cannot be controlled by the applied stress, the sample is in the ‘unstable crack growth’ phase. Martin and Chandler (1994) found that the sample enters this phase when the volumetric strain curve undergoes a reversal whereby the axial stress, σ, exceeds the crack damage threshold, σcd (see Figure 4). Throughout this phase, crack growth continues even if the load is kept constant and there is an abundance of AE activity (Bieniawski, 1967 and Eberhardt et al., 1998). During the period of unstable crack growth, cracks tended to propagate and coalesce throughout the rock specimen under uniaxial compression (Kent et al. 2002 and Tang and Hudson, 2010). Due to the high levels of AE activity, it is virtually impossible to identify the KE within the ‘unstable crack growth’ stage. Boyce et al. (1981) developed a number of variations for the generalised AE curve defined by Mogi (1962), and later cited by Friedel and Thill (1990). Boyce et al. (1981) proposed four variations of the of the damage model, each with differing characteristics: Type 1: Mogi-type – the generalised AE curve as defined by Mogi (1962) Type 2: Unstable – the lack of a stable crack growth region (C-D in Figure 5) Type 3: Dense – the lack of a crack closure/ consolidation region (A-B in Figure 5) Type 4: Dense unstable – a combination of type 2 and 3 (a lack of region A-B and C-D) Friedel and Thill (1990) investigated the application of the KE in six rock types and determined that, in general, the AE curves would exhibit the characteristics of one of the four curve variations defined by Boyce et al. (1981). However, it was noted that the AE curves generated from rocks could be substantially affected by how the AE data was processed, or even displayed (Friedel and Thill, 1990). It was also demonstrated that the type of AE curve was not only dependent upon the rock type, but in particular the crack defects, loading conditions and mechanisms of failure of the rock (Friedel and Thill, 1990). The research also suggested that the use of pattern recognition technology could be a method by which AE curve characterisation could be used to interpret the data (Friedel and Thill, 1990). However, as previously noted, often the KE occurs within a transitional zone that doesn’t possess a precisely definable region (Hughson and Crawford, 1986). Consequently it is apparent that characterisation of the AE curve alone is unlikely to be an effective method by which the point of the KE can be determined. Figure 5.

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General Testing Procedure The accuracy and diligence within the sample preparation and testing phases of an AE experiment have a significant influence on the visibility of the ‘take off point’ and the clarity of the KE (Lavrov, 2002; Seto et al., 1997; Villaescusa et al., 2002; Yuan and Li, 2008). Typical AE testing setup is shown in Figure 6 (Karakus, 2014).

Rock Specimen Geometry and AE Sensors The geometry of the rock sample has a significant impact on the generation of AE activity and therefore on the clarity of the associated ‘take off point’ (Hsieh et al., 2014; Villaescusa et al., 2002). AE experiments are often carried out on cylindrical rock specimens with a typical diameter and length of 20 mm and 50 mm respectively (Villaescusa et al., 2002; Karakus, 2014; Hsieh et al., 2014; Yamamoto et al., 2009). Friedel and Thill (1990) conducted tests with far smaller core samples and discovered that they were inadequate for the testing apparatus and the AE sensors. Tamaki and Yamamoto (1992) conducted tri-axial pre-loading of 20 mm cubic samples prior to uniaxial loading. It is generally recommended that at least three specimens were prepared for each selected orientation in order to obtain representative results (Villaescusa et al., 2002; Yamamoto et al., 2009). The AE monitoring system (see Figure 6) consisted of AE sensors with pre-amplifiers, which were connected to a trigger signal generator with a NI PCI-6133 data acquisition unit. Frequency bandwidth of AE pico sensors is 200-800 kHz. Recording and visualisation of signals were done using a signal processing software developed in house using Labview and a sampling frequency of 1.8 MHz is used throughout our tests. A set of 2/4/6 series filters with 20/40/60 dB gain single ended differential preamplifier was used to amplify signals 60 dB. Excessive background noise, whether from the crushing or frictional movement of the specimen ends, has a significant impact on the clarity of the KE (Li and Nordlund, 1993; Kent et al., 2002; Villaescusa et al., 2002). Often filters and threshold values are used to account for constant levels of low frequency noise such as that employed by Karakus (2014) and Karakus et al. (2015) with a filter of 60 dB and a threshold value of 100 mV (just above the background noise).

Loading Rate and Rotation of the Principle Axis AE Experiments are carried out using an Instron servo-controlled hydraulic testing machine with a typical loading capacity of 300 kN (Li and Nordlund, 1993; Karakus, 2014; Villaescusa et al., 2002; Sun et al., 1995; Kent et al., 2002). Loading rates within AE experiments vary significantly with Li and Nordlund (1993) using a rate of 0.2 MPa/sec, Villaescusa et al. (2002) using 7.5 MPa/min, Karakus (2014) incorporating 3 kN/min and Tamaki and Yamamoto (1992) varying loading rates between 6.7-8.3 MPa/min. Nikkhah et al. (2013) determined that when subjecting a rock specimen to cyclic loading, the ratio of successive loads is critical to the quality and clarity of the KE and the re-loading stress should be at least 1.5 times of the previous stress. Nikkhah et al. (2013) also recommended that at stress levels exceeding 55% of the rocks peak strength, significant discrepancies were incurred between the predicted and actual in-situ stress values. Villaescusa et al. (2002) defined the maximum stress level as a function of the depth of the core, uniaxial compressive strength of the rock and orientation of the under-cored sample.

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Figure 6.

Although significant research has been conducted into the optimum-loading rate, Yoshikawa and Mogi (1981) had a conflicting view that the loading rate has no (or very little) influence on the effectiveness of the KE for the majority of rock samples within the range of 0.005-0.3 MPa/sec. Subhash and Nemat-Nasser (1993) observed that when a rectangular sample was reloaded in the same direction as the first cycle, strain hardening was observed, whereby inelastic strains were lower than that in the first cycle. However, when the samples were reloaded in an orthogonal direction, the inelastic strain was constant between loading cycles and there was no KE. Stuart et al. (1995) identified that the detection of a pre-stress level in a particular direction appears to be unaffected by the application of stresses in orthogonal directions during the period before re-stressing. This indicates that any multiple spikes in the AE curve are completely unrelated to the orthogonal pre-stresses (Stuart et al., 1995). Holcomb and Costin (1986) identified that when taking oriented sub-cores from a sample, the clarity of the KE is very sensitive to the deviation angle at which the sub-cores were taken and at angles beyond 10° from the pre-load stress, no KE was observed. Lavrov (2003) later confirmed that the findings of Holcomb and Costin (1986) also applied to cyclic Brazilian tests where the KE disappeared at rotation

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angles greater than 10° from the pre-loaded stress. The results are critical to uniaxially testing sub-cores and the orientations of cores should be taken to within 10° of the original stress.

The Influence of Pre-loading Stress The pre-loading stress magnitude and loading duration are critical to the clarity of the KE (Lavrov, 2002). Nikkhah et al. (2013) discovered that an increase in pre-loading time increased the clarity of the KE. Karakus (2014) recommended that the pre-load stress be in the order of 40% of the specimens uniaxial compressive strength and be held for 10 minutes. Lavrov (2003) discovered that for brittle rocks, the KE is best defined if the pre-load stress remains below the stress at which dilatancy, the increase in volume, of the rock begins. It was concluded that the further the pre-load stress is from the ultimate strength of the rock, the more defined the KE will be during the reloading cycle. It is suggested for these reasons that the pre-load stress should range from between 30% to 80% of the ultimate strength of the rock (UCS) (Seto et al., 1997; Lavrov, 2003). Lavrov (2003) found that if the pre-load stress exceeds 80% of the UCS then there is a known tendency for the KE to translate towards lower stresses during the reloading cycle. On the other hand, if the pre-load stress is below 30% of the UCS, then it is unlikely that substantial AE will be detected. However, a comparison of the literature does suggest that there is a level of inconsistency regarding the upper limit of the optimal pre-load stress range (Villaescusa et al., 2002; Yoshikawa and Mogi, 1981; Seto et al., 1997; Lavrov, 2002). Hsieh et al. (2014) determine that the compressive stress level at which dilatancy occurs is between 20% to 40% or 50% to 70% of the UCS of rocks, for various rock types. Consequently, it is apparent that the level at which dilatancy occurs is largely rock dependent and should be determined in order to establish an upper limit on the pre-load stress, prior to investigating the KE in the specific rock type. Lavrov et al. (2002) found that for ductile rocks, the KE is well defined at stress levels below and above the stress level at which dilatancy occurs.

The Inflexion Point of the KE The ideal scenario, in which the KE occurs, is when a material is subject to stress and will only emit AE once the stress exceeds the previous maximum stress that the material had been subjected to beforehand (Lavrov, 2003). In this scenario a clearly defined inflexion point will be observed on the cumulative AE hits versus stress curve. The inflexion point marks the point of the KE, with the corresponding stress representing the KE stress. However, it has been noted in a number of studies that there are cases when the point of inflexion may not be easily detectable (Hughson and Crawford, 1986; Yoshikawa and Mogi, 1980; Villaescusa et al., 2002; Siquing et al., 1999; Lehtonen et al., 2011). In such cases, there may be variable AE prior to and post the stress exceeding the previous maximum stress, resulting in a poorly defined inflexion point. In a study of the KE and its uses by Hughson and Crawford (1986), it was noted that “The KE does not occur abruptly at a precisely definable point but within a transitional zone”. Consequently, the experimental AE data often requires further study and analysis to locate and define the point of the KE. A review of existing literature reveals that a number of techniques have been developed that attempt to locate the inflexion point of the KE. The following section of this literature review will discuss and analyse a number of these techniques.

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Method of Tangents In a review of the KE in rocks, Lavrov (2003) noted that one method that may be used to locate the inflexion point of the KE is to construct two tangents to the cumulative AE hits versus stress (or load) curve. The tangents should be located to either side of the apparent inflexion point of the curve, with the point of intersection of the tangents indicating the inflexion point. The stress (or load) value that corresponds to the point of inflexion then represents the stress value of the KE. However, it is immediately apparent that the disadvantage of this method is that it relies upon a subjective determination of where the inflexion point lies. As suggested by Lavrov (2003), the maximum stress value should be assumed (or estimated), in order to determine the appropriate location of the two tangents. Furthermore, this method can also be used when comparing hit rate, or hit rate squared, against stress, where hit rate is the number of AE per second or per 1 MPa increment of stress. Lavrov (2003) notes that the square of the hit rate can produce an inflexion point with greater clarity than it would with the conventional cumulative AE hits versus stress curve. Hughson and Crawford (1986) modified the Method of Tangents in a study into the KE, its uses for in-situ stress measurement and the evaluation of the maximum stress that a material may withstand prior to becoming unstable or failing. Two tangents were drawn from either side of the transition zone, the intersection point of which defined the inflexion point. The corresponding stress (or load) to the inflexion point is then identified as the KE stress (see Figure 7). It is apparent however that the method of tangents and the modification proposed by Hughson and Crawford (1986) are reliant upon the subjective determination of the extent of the transitional zone. Consequently, the method would be best restricted to certain AE curves that have a number of easily characterised regions.

Regression Analysis Bilinear regression may also be used to locate the inflexion point of the cumulative AE hits versus stress curve. Bilinear regression is itself similar to the method of tangents. Two lines are constructed to approximate the cumulative AE hits versus stress curve, where the intersection of the two lines marks the inflexion point of the curve. The projected stress of the inflexion point represents the KE stress. Lavrov (2003) makes note of the pivot point method developed by Shen (1995) that can be used to aid bilinear regression analysis. Hardy and Shen (1992) and Shen (1995) postulate that a pivot point method can be used to increase the accuracy of bilinear regression analysis. The location of the starting point for the second regression line is the point at which the incremental value of the cumulative AE count begins to increase substantially (Jayaraman, 2001).

Maximum Curvature Method One other technique that has been developed to determine the point of the KE is to locate the point of maximum curvature of the cumulative AE hits versus stress curve. Lavrov (2003) notes that this method assumes that the stress level at the point of maximum curvature corresponds to the KE stress.

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Figure 7.

Villaescusa et al. (2002) conducted a study that compared the KE and deformation rate analysis methods for use in stress estimation of oriented core. The criterion was an adaption of the maximum curvature method, where the maximum variation in the normalised slope of the AE curve represents the point of the KE.

Difference Method Yoshikawa and Mogi (1980) investigated a new method to be used for situations in which the KE is not precisely definable due to significant AE. A new method of cyclic loading was evaluated, and the influence of water and temperature upon the KE was examined. The new method of cyclic uniaxial loading involved reloading a rock sample twice and examining the difference between the two generated cumulative AE versus stress curves. A sample of Shinkomatsu andesite was initially pre-loaded uniaxially to a defined point to create a previous maximum stress (Yoshikawa and Mogi, 1980). The sample was uniaxially reloaded two times, with each reloading cycle exceeding the previous maximum stress (the pre-load stress). It was shown that the generated cumulative AE versus stress curves for each reloading cycle coincided well, up to the previous maximum stress (Yoshikawa and Mogi, 1980). Once the reload stress exceeded the previous

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maximum stress, the cumulative AE versus stress curves for the two reload cycles would diverge, with the second reload curve characterised by substantially less cumulative AE than the first (Yoshikawa and Mogi, 1980). The point of divergence is considered to be the point of the KE (see Figure 8). The accuracy of this method was evaluated in a number of tests. It was shown that if the pre-load stress was low compared to the fracture strength, the previous maximum stress (determined point of the KE) was in close accordance to the actual previous maximum stress (Yoshikawa and Mogi, 1980). However, if the pre-load stress approached the fracture strength, then the previous maximum stress would be underestimated (Yoshikawa and Mogi, 1980). It must be noted that whilst there was an insufficient number of tests conducted to determine the optimal pre-load stress to fracture strength ratio, it was tentatively demonstrated that a ratio of pre-load stress to fracture strength of 0.4 resulted in an underestimation of the previous maximum stress by approximately 6%. This research by Yoshikawa and Mogi (1980) demonstrated that this method could be used to accurately determine the previous maximum stress by examination of the difference between the first and second reload cycles, provided the pre-load stress was sufficiently low compared to the fracture strength. However, the tests conducted by Yoshikawa and Mogi (1980) relied upon uniaxial pre-loading and reloading. As in-situ rock is subject to a tri-axial stress state, it remains to be seen what effect tri-axial pre-loading might have upon this technique. Figure 8.

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Felicity Ratio and the Felicity Effect Hughson and Crawford (1987) conducted a study investigating the influence of confining stress upon the KE. The KE was investigated in a sample of Berea Sandstone. It was recognised that if the previous maximum stress was low (in comparison to material strength), the KE would occur at a stress greater than the previous maximum stress (Hughson and Crawford, 1987). Likewise if the previous maximum stress approached the UCS of the rock material, the KE would occur at a stress lower than the previous maximum stress. These results are in agreement with the results and conclusions of a number of other studies (Yoshikawa and Mogi, 1980; Nordlund and Li, 1990). This phenomenon is defined as the Felicity effect, with the FR representing the ratio of the KE stress to the previous maximum stress. Hughson and Crawford (1987) postulated that a comprehensive understanding of the Felicity effect, for a particular rock, could be used to adjust and optimise the measured previous maximum stress in the rock type. In Figure 9, Lavrov (2003) illustrates the relationship between the FR and the previous maximum stress (σm) divided by the rock UCS, in the second loading cycle (the first reloading cycle after pre-loading to σm). In the scenario where the FR is equal to one, then the measured KE stress is said to represent the true previous maximum stress. It is apparent that by testing a range of samples of a particular rock type, a FR profile may be generated. The profile could then be used to determine the accuracy of the measured KE stress in the specific rock type by comparing the ratio of the measured stress divided by the rock UCS against the FR profile.

Problems Encountered in the KE There are numerous potential issues associated with the KE and its use in predicting in-situ stresses. Some of these issues include the influence of water saturation, heating, the coring process and geological processes. Figure 9.

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Kurita and Fujii (1979) determined that the identification of the KE is significantly impacted by water saturation, even in a low porosity rock such as granite. The degradation of a sample is also apparent when the moisture content in the sample is changed. Yoshikawa and Mogi (1981) determined that high moisture conditions between successive loading cycles tended to lead to a high degradation of the stress memory. Furthermore, Lavrov (2003) determined that heating the core between successive loading cycles could also significantly alter the location of the take-off point of the KE. Similarly, Panasiyan et al. (1990) determined that heat had a major influence on the stress memory of a diorite sample. Coring has a significant influence on the stresses experienced by a rock mass and also introduces damage throughout the specimen (Lavrov, 2002). The vertical principal stress of a rock is reduced when a vertical borehole is in the immediate vicinity. Alternatively, horizontal principal stresses are not influenced until the coring bit encompasses the rock. At this time, horizontal stresses are greatly reduced (Holt et al., 2000-2001). According to Lehtonen et al. (2012), the KE is best suited to rock masses which have undergone only one deformation event, as represented by Process A in Figure 10. Therefore, the KE is best suited to predicting the in-situ stress of relatively young sedimentary rocks that have not been subjected to tectonic stress, erosion and faulting. Lehtonen et al., (2012) concluded that if a rock is subducted past the brittle/ductile transition boundary, as shown by Process B, the stress experienced is not recorded and hence, is not retained (see Figure 10). Lavrov et al., (2002) determined that the magnitude of KE decay is rock dependent, with limestone having a relatively short stress memory of a few months. Koerner and Lord (1984) discovered that the KE disappeared entirely after 1000 hours when gneiss, schist, carbonate, mudstone, limestone and sandstone were tested. Shin and Kanagawa (1995) found a well-pronounced KE after a 300-day delay when testing granite. Michihiro et al., (1992) disputed this theory when it was determined that the stress memory of granite was erased after 20 days. Therefore, the stress memory of a rock mass is still a relatively unknown phenomenon when employing the KE method.

Figure 10.

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METHODOLOGY In total, there were six primary stages in the methodology. These six primary stages were as follows as Master core preparation; UCS testing; Master core pre-loading; Sub-core extraction and preparation; Sub-core uniaxial loading; Data acquisition and analysis. Nine core samples were selected from 63.5 mm diamond drill core which was obtained at a depth between 1018.6 m to 1021.9 m in North of South Australia. Each sample was selected upon observation of the core, to ensure no obvious cracks, fractures or discontinuities existed, in addition to avoiding areas showing greatest variance in composition. Of these nine cores, three would be subject to UCS testing whilst the remaining six would be pre-loaded prior to having a number of sub-cores extracted from each. The six core samples which would be pre-loaded were termed ‘master cores’. Figure 11 depicts the nine selected core samples from within the range of supplied drill core. The selected nine core samples were cut using a diamond saw and the faces ground using a surface grinder fitted with a diamond wheel. The length of each core was set at 130 mm, resulting in a standard L/D ratio of 2.04. Samples 6, 8 and 9 were allocated for UCS testing whilst samples 1, 2, 3, 4, 5 and 7 were specified as the six master cores. The distribution of core samples into each group was conducted in a random fashion, except for core sample 7, which was identified as containing a discontinuity which ran around its full diameter. The average 211 MPa of the UCS value was obtained from these experiments.

Master Core Pre-loading The aim behind the master core pre-loading stage was to produce a known stress memory within the six master core rock samples. This known stress memory was to be created by loading each master core to a set load, for a length of ten minutes. In order to evaluate the effect of confining pressure and prestress directionality, half of the six master cores were subjected to uniaxial loading and the remaining half to tri-axial loading, achieved through the use of a Hoek Cell. To further enable ease of contrasting

Figure 11.

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the results, each master core was paired with another. Each pair included one uniaxial and one tri-axial pre-loaded master core, or two tri-axial pre-loaded master cores at different pressures. The pressures specified for each master core were determined in accordance to the range suggested by Lavrov (2002) that the pre-load be between 30 – 80% of the UCS of the specimen. Hence given the rock UCS of 211 MPa, the maximum suggested axial pre-load was 168.8 MPa (0.8 x UCS) and the minimum suggested axial pre-load was 63.3 MPa (0.3 x UCS). Consequently, the three pairs of master cores were axially pre-loaded in increments of 10 MPa, starting at 70 MPa. The confining pressure, on the other hand, was limited to a total of 32 MPa, due to limitations of the confining cell maintainer. Accordingly, the confining pressure varied from 20 MPa through to 30 MPa for those master cores subjected to tri-axial pre-loading. Table 1 summarises the pairing of each master core along with the pre-load type and the associated loads. The load rate was kept constant at approximately 0.87 MPa/s, however, for master core 2 and 7, the load rate was halved to 0.44 MPa/s as the digital maintainer was unable to match the load rate of 0.87 MPa/s. The ratio of uniaxial to confining load rate was kept constant for each master core at 1:1.

Sub-Core Extraction and Preparation In order to evaluate the effect of the directionality of pre-load stress, three master cores each had three 19 mm sub-cores extracted at different orientations. This enabled comparisons to be made with regard to the KE on sub-cores perpendicular to the direction of the master core axial load, and also perpendicular to the master core confining pressure. Furthermore, the three remaining master cores each had one 42 mm sub-core extracted, down their vertical axis. A total of twelve sub-cores were extracted from the six pre-loaded master cores, three were of 42 mm diameter and nine were of 19 mm diameter. Sub-core quantity and diameter was allocated to each master core on the basis of ensuring a level of consistency. Consequently pair 2 would have 19 mm sub-cores extracted, pair three would have 42 mm sub-cores extracted and pair 1 would have one set of 19 mm sub-cores and one 42 mm sub-core extracted. The sub-core diameters of pair 1 were odd due to time constraints within the laboratory. Prior to testing however each sub-core was fitted with appropriate instrumentation. One acoustic emission sensor was placed on each sample, using a gel compound to create a flush seal against the sample, with a rubber band holding the sensor steady. Two axial extensometers were fitted to opposite sides of the outer surface of the 42 mm sub-cores, with one lateral extensometer wrapping across the sub-core. The schematic setup of instrumentation, and the actual instrumentation, for both the 19 mm sub-cores and the 42 mm sub-cores are depicted in Figure 12. Table 1. Master core (63.5 mm) pre-load specifications Pair

Master Core

Pre-load type

Axial load (MPa)

Confining pressure (MPa)

1

1

Tri-axial

80

25

4

Uniaxial

80

0

2

5

Tri-axial

90

30

7

Tri-axial

90

20

2

Tri-axial

70

20

3

Uniaxial

70

0

3

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The sub-cores were subjected to three cycles of uniaxial loading. The first and second cycles were to a set axial load, which exceeded the axial pre-load, but remained below 80% of the rock UCS. The axial load was set to exceed the axial pre-load in order to produce the KE from within the sub-cores. The third cycle, however, continued until the specimen failed, in order to provide additional data for supplementary analysis, if required. The set axial load of cycles one and two was arbitrarily set to 30 MPa above the axial pre-load, which remained below 80% of the rock UCS, except for sub-core 3 which was set to 65 MPa above its respective axial pre-load. Initially, each sub-core was to be subjected to the same axial load of 1.5 times the maximum axial pre-load (90 MPa for master core 5 and 7), however, it was apparent upon completing testing for sub-core 3, that a set axial load 65 MPa above the axial pre-load was unnecessary and time consuming. Hence, for the remaining 11 sub-cores, the set axial load was 30 MPa above the respective axial pre-load. The load rate for each 42 mm sub-core was set at a constant rate of 10.6 MPa/min, whilst the load rate for the 19 mm sub-cores was set to 21.2 MPa/min. The different load rates were selected on the basis of test time, however it was not anticipated that any material effect would result from the different rates. A summary of the cycle one and two axial loads for each sub-core and the respective load rates is detailed in Table 2. Figure 12.

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Table 2. Sub-core test Specifications Pair

Master Core

Sub-cores

Pre-loading type

Axial pre-load (MPa)

Confining preload (MPa)

Set axial load: Cycle 1 and 2 (MPa)

Load rate (kN/ min)

1

1

S1-1#

Tri-axial

80

25

110

6.00

1

S1-2#

Tri-axial

80

25

110

6.00

1

S1-3

Tri-axial

80

25

110

6.00

4

S4*

2

3

#

Uniaxial

80

0

110

14.66

5

#

S5-1

Tri-axial

90

30

120

6.00

5

S5-2#

Tri-axial

90

30

120

6.00

5

S5-3

Tri-axial

90

30

120

6.00

7

S7-1

Tri-axial

90

20

120

6.00

7

#

S7-2

Tri-axial

90

20

120

6.00

7

S7-3#

Tri-axial

90

20

120

6.00

2

S2*

Tri-axial

70

20

100

14.66

3

S3*

Uniaxial

70

0

135

14.66

# #

* 42 mm; 19 mm #

During this loading phase the acoustic emission and strain data was recorded. Note the sub-core and load cycle naming convention used throughout this paper is as follows: S(Master core no.) – (Sub-core no.) cycle (cycle no.) In limited circumstances the cycle number will be defined as either A, B or C, representing cycle 1, 2 or 3 respectively. For instance, sub-core 3 extracted from master core 1 in loading cycle 2 is represented as either: S1-3 cycle 2 or S1-3B.

Data Acquisition Strain and load data was simultaneously recorded. The AE data was filtered within LabVIEW using a high-pass 51 tap FIR filter, at a frequency of 320 kHz. The data was filtered at 320 kHz as this is the established frequency at which machine noise produced from the Instron 1342 system is successfully eliminated. The filtered data was then analysed within LabVIEW through the implementation of a threshold voltage. The threshold voltage is the minimum voltage at which a signal event is defined as an AE ‘hit’ or as noise. Figure 13 depicts a simplified graphical explanation of the threshold voltage in use.

EVALUATION OF THE RESULTS Preliminary analysis of AE data for each sub-core was conducted using a number of threshold voltages. Due to varying levels of noise during the testing phase for each sub-core, the threshold voltages used for preliminary analysis were determined according to the base noise voltage specific to each sub-core.

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Figure 13.

All results were analysed according to the most appropriate method of estimation, upon evaluation of the shape of the cumulative AE versus stress curve. Curves such as that displayed in Figure 14 were deemed appropriate for use of the method of tangents whilst curves such as that displayed in Figure 15 were analysed through observation. The results indicate that the average accuracy of the estimated KE stress was 17%, with some subcores demonstrating substantially greater accuracy, including sub-core 1-3 cycle 1 which had an average accuracy of 3% and sub-core 7-1 cycle 2 which had an average accuracy of 6%. Figures 16-17 depict the accuracy of each of these two sub-cores. The Difference Method analysis was conducted at the same threshold voltage for the first and second reloading cycle, in order to ensure that the background noise between successive loading cycles was similar. Using the known loading rate, the time of each AE hit was determined. Acoustic emission counts per ten seconds were then plotted against time for both cycles to enable comparison of both reloading cycles. The results of the Difference Method are depicted in the Figure 18-19. Sub-core 1-1, a lateral core, appeared to show little correlation between successive loading cycles, as depicted in Figure 18. It is observed that the curves diverge after approximately 20 seconds. This corresponds to a stress of 7 MPa, far below both the axial and pre-loads applied. Sub-core 1-2, another lateral sub-core, features little correlation between the initial and second reloading cycle, as shown in Figure 19. The curves diverge after approximately 20 seconds of loading, which again corresponds to a stress of 7 MPa.

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Figure 14.

Figure 15.

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Figure 16.

Figure 17.

The axial sub-core investigated, Sub-core 2, is shown in Figure 20. There appears to be no correlation between the initial and second reloading cycle in the graph shown. Therefore, no point exists where the curves diverge. From the results, above, it can be concluded that the Difference Method was not effective in predicting the pre-load stress. When plotting the AE count per ten seconds against time, there was little coincidence

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Figure 18.

Figure 19.

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Figure 20.

between the initial and second reloading cycles. Therefore, the lack of a clearly defined KE when using the Difference Method indicates that the pre-load stress may not have been sufficiently small when compared to the fracture strength.

Effect of Sub-Core Orientation Throughout the analysis of the 19 mm sub-core AE data it was evident that, in several cases, a range of previous stress states were identified. As discussed in the literature review, there are numerous theories surrounding the effect of perpendicular stresses on the application of the KE. Although there has been a range of theories postulated, the existing theories conflict with each other and there remains a high degree of uncertainty when attempting to understand exactly what effect orthogonal stresses have on the clarity of the KE. It was determined that in several cases, the point of inflexion accurately predicted the axial pre-load stress with little to no activity surrounding the confining pre-load stress. Conversely, in other specimens the dominating point of inflexion closely represented the confining pre-load stress. In some cases both pre-load stresses were observed with the occurrence of multiple inflexion points. This prompted an investigation into the effect of a sub-core’s orientation, relative to its master core, on the ability of the KE to detect the axial and confining preload stresses using both the AE technique. When extracting each sub-core, it was critical to note the orientation of each core in order to determine if there is a relationship between sub-core orientation and the stresses identified through the KE. Sub-cores one and two were initially extracted laterally in a plane parallel to the master core faces. Sub-core three was subsequently extracted in the axial direction down the long axis of the master core (see Figure 21).

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Figure 21.

A single axial sub-core was extracted from each of the three master cores resulting in a quite small data set of six loading cycles. However, due to excessive background noise in acoustic emission testing and abnormalities in the results, the data set contained only two axial sub-core loading cycles. Although no sound conclusions could be drawn from such a limited data range, a small analysis was undertaken. When analysing the results from the AE testing, it was determined that in all cases the axial pre-load stress was easily identifiable within the axially oriented sub-cores. Interestingly, the confining pre-load stress was not clearly identifiable in either of two cases. Therefore it appears that, in general, the lateral confining pre-load stress is not observed in axially oriented sub-cores. This indicates that axially oriented sub-cores only detect stresses acting in the same direction as the sub-core orientation. It was evident that the point of inflexion of the KE coincided reasonably clearly with the axial preload stress in both of the axially oriented sub-cores (see Figures 22-23). The accuracy of the axial stress predictions ranged from 1.25% in Sub-core 1-3A to 12.2% in Sub-core 5-3A. Therefore, the average error of the axial pre-load stress prediction within the axially oriented sub-cores was approximately 6.7%. However, attempting to locate the confining pre-load stress in the axially oriented sub-cores proved to be far more difficult. In both cases, there was no obvious increase in acoustic emissions surrounding the confining pre-load stress. This tendency for the axially oriented sub-cores to represent the axial pre-load stress, and not the confining pre-load stress, is exemplified in the first cycle of Sub-core 1-3 (see Figure 22). Two lateral sub-cores were taken from three master cores, each in a plane parallel to the end faces of the master core. In total, six lateral sub-cores were extracted and subjected to AE testing with two

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 22.

loading cycles in each case. However, due to excessive background noise and abnormal results, only five curves were available to be analysed. When analysing the results from the AE testing, it was determined that in 60% of cases the lateral pre-load stress (or confining pre-load) was easily identifiable. However, interestingly, the axial pre-load stress was also regularly observed, being clearly identifiable in 80% of rock specimens. It was evident that the point of inflexion of the KE coincided reasonably clearly with both the confining and axial pre-load stresses in 40% of the laterally oriented sub-cores. Throughout numerous studies, including that of Lavrov in 2002, it was indicated that the KE could only detect a rock’s previous maximum stress state. However, our results indicate that there can be multiple inflexion points that each represent a different pre-load stress. This occurrence of multiple jumps in the acoustic emission curve is well represented in the second loading cycle of Sub-core 7-2, whereby the confining and axial pre-load stresses are observed with a reasonable degree of accuracy (see Figure 24). The results analysed suggest that there may be a vague link between the orientation of the sub-core and the stresses that are detected through the AE technique. In the case of an axially oriented sub-core, the results indicate that the vertical pre-load stress gives rise to a pronounced KE in all of the limited cases, whereas the horizontal pre-load stress has no clear representation in any cases. Whereas in the case of a laterally oriented sub-core, the lateral and axial pre-load stresses were observed in 60% and 80% of cases respectively. This indicated that a core oriented in the direction of the major pre-load stress (axially) will only detect the major pre-load stress state and the minor pre-load stresses will be invisible. However, in a core oriented in the direction of the minor pre-load stress (laterally), the AE technique

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 23.

has the ability to detect the major pre-load stress, minor pre-load stress or in some cases both stresses on the same AE curve. As previously discussed in the literature review, Lavrov (2002) and Filimonov et al., (2001) postulated that, in general, the AE technique would give rise to a single inflexion point, however it was not necessarily at a rock’s previous maximum stress state. It was thought that in most rock types, the inflexion point represented a linear combination of multiple orthogonal in-situ stresses. Our results tend to confirm the work of Filimonov et al. (2001) in the sense that the point of inflexion is not necessarily located at a rock’s maximum stress, but contradict the idea that a linear combination is involved. Rather, our results indicate that in numerous cases, multiple orthogonal in-situ stresses can be observed through the occurrence of multiple inflexion points on the AE curve. It has been determined that in approximately 29% of cases both the axial and confining pre-load stress states were clearly identifiable on the acoustic emission curve. This result refutes the views of Lavrov (2002) and Villaescusa et al., (2002) in which it was indicated that the KE can only detect a rock’s previous maximum stress state. However, all of the cases in which multiple stresses were identified were within laterally oriented sub-cores. Therefore, there seems to be significant advantages in testing laterally oriented sub-cores over axially oriented sub-cores, as both the axial and confining pre-load stresses can be observed.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 24.

When attempting to identify the in-situ stress memory within a rock, it should be considered that the point of inflexion of the KE would likely represent the stress that is in the same direction as the coring. It is also important to consider that in sub-cores oriented parallel to the minor pre-load stress (laterally), it was possible to identify multiple pre-load stresses acting perpendicular to each other.

A New Acoustic Emission Voltage Analysis Analysis of cumulative AE hits versus stress curves at high threshold voltages reveals the presence of a small number of large AE events at the occurrence of the KE. As highlighted in sub-core 1-1 cycle 1, with a high threshold voltage of 0.4V, the large AE events appear associated with both the confining preload and axial pre-load (see Figure 25). However, at low threshold voltages these large AE events may be masked by the large number of lower energy AE hits, effectively hiding their presence (see Figure 26). Furthermore, at low threshold voltages, some graphs do not demonstrate an abrupt increase in the rate of AE events at the previous maximum stress - that is they do not demonstrate the KE at low threshold voltages. In these scenarios, the results may be described as a decrease in the rate of AE with increasing stress. Analysis for such data must be conducted at high threshold voltages with few AE hits in order to observe the underlying KE or through methods measuring the percentage increase in cumulative AE hits (Karakus, 2014; Karakus et al., 2015).

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 25.

However, both methods have inherent disadvantages. High threshold voltage analysis may disregard relevant low to moderate voltage AE and result in minimal cumulative AE hits. On the other hand, the percentage increase method may not yield practical results until evaluation is conducted at a high threshold voltage. It will likely not result in prominent percentage increases in the cumulative AE hits curve and hence become subject to the disadvantages associated with high threshold voltage analysis. In order to overcome the distinct disadvantages associated with high threshold voltage analysis, in addition to enabling evaluation of AE data when a typical KE curve is not obtained, a new technique of analysing AE data was investigated. It was considered that if there was an association between large and small to moderate AE events at a particular point, then this point may be indicative of the point of the KE. However, due to the masking effect resulting from a significant number of low voltage AE events, this point may not be apparent under normal analysis techniques. The new method AE energy analysis, involved plotting the filtered AE data and analysing it at a threshold voltage slightly greater than the noise voltage. The energy of the resultant AE data, represented as voltage, was graphed against the stress at which each respective AE event occurs. The AE event voltage versus stress graph then enabled observations to be made in relation to the AE data for the specimen, taking into account both low and high energy events.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 26.

The advantage of this method is that the nature of the AE event may be investigated, as it will become apparent whether the high energy AE events have a clearly defined association with other smaller to moderate energy AE events. If this is shown to be the case, then there is potential merit in investigating techniques which assign a weighting to AE events based upon the energy of the AE event, in order to better estimate the point of inflexion in KE. Analysis of sub-core 1-1A revealed the most pronounced and distinctive correlation between groupings of low to high energy AE events and confining pre-load and axial pre-load. It is apparent that high energy AE events are correlated to pre-load stresses and that these high energy AE events are also associated with low to moderate energy AE events. However, these correlations may be masked by the overwhelming number of low energy AE events, particularly when conducting analysis at low threshold voltages. Figures 27-29 each depict the voltage analysis of the first cycle for sub-cores 1-1, 1-2 and 1-3 respectively. It must be noted that cycle 2 of sub-core 1-3 was not included in the analysis, as the raw AE data was too large and could not be processed by the analysis software at a threshold voltage close to, but slightly greater than, the noise voltage. In order to maintain consistency for each of the analyses conducted, it was determined that sub-core 1-3 cycle 2 would not be evaluated, as it would require analysis using a threshold voltage much greater than each of the other analyses (relative to the sub-core noise voltage).

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 27.

Figure 28.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 29.

Discussion and Analysis The results indicate that of the 15 sub-cores investigated, 73% demonstrated a strong correlation between a cluster of low to high energy AE events at or within 5 MPa of the axial pre-load. Furthermore, 62% demonstrated a strong correlation between a cluster of low to high energy AE events at or within 5 MPa of the confining pre-load. These results indicate that a relationship does exist between small to high energy AE events at, or in close proximity to, the pre-load stresses or previous maximum stresses that the sub-core has been subject to. Consequently it follows that if large energy AE events are associated with pre-load stresses, then there is merit in investigating techniques that assign a weighting proportional to AE energy, in order to better delineate and identify the inflexion point in KE. However, in a similar fashion to the occurrence of numerous ‘jumps’ observed in traditional cumulative AE hits versus stress curve analysis, there may be a large number of other clusters in addition to those in close proximity to pre-load stresses. It is postulated that these other clusters may be related to the previous stress history of the sub-core specimens. Currently, there has been no investigation into whether this may or may not be the case. It is noted however that Lavrov (2002) determined that the length of time between loading cycles resulted in the clarity of the KE diminishing significantly. Further studies have also shown that the KE is time dependent (Park et al., 2001). However, in contrast Koerner and Lord (1984), Yoshikawa and Mogi (1981) and Holcomb (1993b) demonstrated that the KE is still observable after periods of up to 10 hours whilst Seto et al. (1997) concluded that the KE remains observable after periods of up to two years.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Considering the conflicting literature regarding the effect of time upon the KE, an investigation into whether these clusters are related to previous stress history might be conducted upon specific sedimentary rock samples, close to the Earth’s surface, which do not have a complex and long geological stress history. If upon testing such specimens there is no observation of clusters other than those associated with pre-load stresses, then it may suggest that clusters, such as those observed, are indeed representative of previous stress history. Of additional interest is that a large number of the sub-cores demonstrated a correlation to both confining pre-load and axial pre-load, or a correlation to the pre-load in a plane 90° to the sub-core orientation. These results support those discussed in Section 5.4 where pre-load stresses were observed in orientations different to the orientation of the sub-core. Notably, this contests the literature (Subhash and Nemat-Nasser, 1993; Stuart et al., 1995; Holcomb and Costin, 1986) which states that the KE is only observed for the previous stress oriented in or at least 10° from the orientation of the sub-core. Consequently, further testing should be conducted using the ‘voltage analysis’ technique, and others, to confirm and validate, or reject, the results previously established in the literature.

Weighted Acoustic Emission Analysis The mechanisms which produce AE activity when loading a rock are understood to be a range of pore closure, crack closure, crack initiation and crack propagation. Therefore, we believe that when reloading a sub-core many of the AE events do not equate to new damage of the rock. Instead, this AE may be attributed to closure of cracks previously sustained when the rock was originally loaded, and possibly some AE due to seating of the specimen. These initial AE events that do not equate to new damage of the specimen are relatively small in magnitude, however, they are abundant, and therefore can have a significant impact when plotting cumulative AE hits versus stress. When the rock mass is stressed beyond its previous maximum stress, new cracks are generated, or existing cracks are propagated. The quantity of these events that occur may not necessarily exceed the quantity of low voltage events that occurred when the rock was subject to lower stresses, however the AE energy of these events would be larger than that of the lower stress events. In a stress versus cumulative AE hits graph this may result in no inflexion point at the previous maximum stress, or it could even result in a higher rate of acoustic emissions occurring at lower stresses, and a lower rate of acoustic emissions occurring at higher stresses, essentially the opposite of what is expected to occur. If AE activity with a higher energy is given a higher weighting, this will allow that AE activity resulting from new damage, to have a greater influence on the location of the inflexion point. Conversely, AE activity with a lower energy, which is not a product of new damage of the rock, would have less influence on the determination of the inflexion point. By conducting a weighted AE analysis, a KE point could be identified from data, which when using the conventional cumulative AE hits method could not be determined. This would subsequently reduce ambiguity when determining the point of inflexion in KE. Two methods were developed to assign a weighting to an AE event. The first method gave the AE event a weighting equal to its energy that is the weighting was directly proportional to the AE energy. The second method assigned each AE event a weighting equal to the square of its energy. The majority of AE activity can be attributed to seating, pore closure, crack closure and other mechanisms not associated with rock mass damage is generally in the range of 0-1V. By assigning a weighting proportional to the square of their energy, their influence on the shape of a stress versus cumulative weighted AE hits curve would be significantly reduced. Furthermore, we believe that the AE activity generated by crack 313

 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

initiation, crack propagation and other mechanisms causing damage to the rock are generally greater than 1V. By assigning this AE activity a weighting equal to the square of the energy, it would allow the AE activity due to rock damage to have a much greater influence on the stress versus cumulative weighted AE hits graph, ultimately resulting in a clear ‘jump’ at the pre-loaded stress memory of the rock mass.

Cumulative Acoustic Emission Voltage For the vast majority of sub cores, assigning a weighting to each AE event equal to the magnitude of the energy did not generate a graph significantly different to that simply generated using cumulative AE hits. This can be attributed to the fact that there is an extremely large quantity of low energy AE activity present, with a comparatively small quantity of high energy AE activity. Therefore, this weighting did not enable the small quantity of high energy AE hits to have a significant influence on the stress versus cumulative weighted AE hits graph. Sub core 7 displays this occurrence quite clearly. When comparing cumulative AE hits with cumulative weighted AE hits (see Figure 30) it can be seen that there is little difference between the curves. The reason for this becomes obvious when analysing the energy of each individual AE event (see Figure 31). Figure 31 shows that the vast majority of AE activity occurs at an extremely low energy (1V). These large energy AE events, that we believe are due to rock mass damage, have therefore failed to have a significant influence on the shape of the stress versus cumulative weighted AE hits, and have therefore not reduced the ambiguity surrounding identifying the point of inflexion. Figure 30.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 31.

Cumulative Acoustic Emission Voltage Squared When assigning a weighting to each AE event equal to the square of the amplitude in Volt, the axial load applied to the core was estimated with a success rate, to within an average of 3% of the axial load. This method, however, did not yield a high success rate when estimating the confining pressure. The confining pressure could only be estimated in 38% of samples, with an average error of 30%.

Table 3. Results of cumulative energy squared weighting when determining axial load Sub-core

Orientation

Pre-load axial stress (MPa)

Cycle

Estimated axial stress (MPa)

Error (%)

FR

1-1

Lateral

80

1

75.43

-6%

0.94

1-2

Lateral

80

1

77.8

-3%

0.97

1-3

Axial

80

1

80.26

0%

1.00

2

Axial

70

2

71.4

2%

1.02

3

Axial

70

2

69.47

-1%

0.99

7-1

Lateral

90

1

89.18

-1%

0.99

7-2

Lateral

90

2

87.92

-2%

0.98

7-3

Axial

90

1

97.65

9%

1.09

3%

1.00

Average

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Table 4. Results of cumulative energy squared weighting when determining confining pressure Sub-core

Orientation

Pre-load confining pressure (MPa)

Cycle

Estimated confining pressure (MPa)

Error (%)

FR

1-1

Lateral

25

1

20.05

-20%

0.80

1-2

Lateral

25

1

19.98

-20%

0.80

1-3

Axial

25

-

-

-

-

2

Axial

20

1

29.9

50%

1.50

7-1

Lateral

20

-

-

-

-

7-2

Lateral

20

-

-

-

-

7-3

Axial

20

-

-

-

-

Figure 32.

As the axial pre-load stress on the master core increases, there seems to be a weak relationship equating higher stresses with a greater estimation error. This can be appreciated when analysing the pre-load axial stress versus estimated axial stress (see Figure 33), pre-load axial stress versus error percentage (see Figure 34) and pre-load axial stress versus FR (see Figure 35). In the vast majority of instances, the axial pre-load simply could not be determined using cumulative hits alone. Figure 32 shows the effect that assigning a weighting equal to the square of the voltage of the AE event can have on generating ‘jumps’ at certain points when plotted against stress.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 33.

Figure 34.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Figure 35.

FUTURE RESEARCH DIRECTIONS We recommend further research into the mechanisms occurring within a rock mass when it is subject to load, to determine exactly which mechanisms (namely crack closure, pore closure, crack initiation and crack propagation) are occurring and what voltage frequency AE events each mechanism is responsible for. Further research into this could subsequently group AE events in ranges, with each voltage range equating to a specific mechanism such as pore closure, crack closure, micro cracking and macro cracking initiation and propagation. Following this, a more advanced version of The UoA Method could be implemented which weights the mechanisms that are not associated with the Kaiser effect extremely low (or they could be discounted completely), whilst those mechanisms associated with the Kaiser effect could have appropriately higher weightings assigned. Furthermore, this study could also aid in determining the threshold voltage for defining AE hits. This could further improve The UoA Method in determining the pre-load stresses of a rock mass.

CONCLUSION Assigning a weighting to each AE event equal to its energy was not successful due to the extremely large proportion of low energy AE events to high energy AE events. Essentially, the high energy AE events caused by new damage of the rock mass associated with the KE point, did not occur in a high enough quantity to have a dominant influence on the results.

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 A New Acoustic Energy-Based Method to Estimate Pre-Loads on Cored Rocks

Assigning a value to each AE event which is equal to the square of the energy produced extremely accurate results for estimating the axial pre-load. Furthermore, this method was successful regardless of the orientation of the sub-core being tested. Low energy AE signals is emitted due to pore closure and crack closure, or ‘settling’ of discontinuities which already exist within the rock mass, and are also not closely associated with the previous maximum stress, and that they may occur in random large ‘clusters’ at stress levels which are not the previously applied maximum stress. The second includes mechanisms that damage in the rock, such as crack initiation and crack propagation. It is believed that these mechanisms emit large energy AE emissions, and are associated with the previously applied maximum stress. By weighting the AE events by the square of their energy, the influence of AE activity associated with non-destructive mechanisms will be reduced while simultaneously increasing the influence of AE activity associated with destructive mechanisms. This will ultimately result in a clearer jump at the previously applied maximum stress level. The FR for these results ranges between 0.94 and 1.09, with an average FR of 1.00 (see Table 3 and Table 4). Whilst this narrow FR range confirms the accuracy of The UoA method, it also provides further information. As the average FR is equal to 1.00, this means that on average this method has estimated the axial load exactly. Therefore, the method does not tent to over or under estimate the stress. The research also was focused on determining whether a relationship exists between the orientation of a sub-core and the pre-load stresses which are clearly identifiable on the AE curve. It was observed that in a sub-core oriented in the direction of the major pre-load stress (axially), the major pre-load stress state is clearly observed, whereas the minor pre-load stresses was never visible. However, in a sub-core oriented in the direction of the minor pre-load stress (laterally), the AE technique has the ability to detect the major pre-load stress, minor pre-load stress or in some cases both stresses on the same curve. Therefore, this indicates that the orientation of a sub-core does have significant effect on which pre-load stress is observed within axially oriented sub-cores, but not within laterally oriented sub-cores. Contrary to all current literature on the application of the KE, it was indicated that the AE curve can give rise to multiple clear inflexion points, particularly in sub-cores oriented in the direction of the minor pre-load stress (laterally). It was observed that in 40% of laterally oriented sub-cores, multiple inflexion points allowed for the accurate prediction of both the lateral and axial pre-load stress states. This theory is supported by the results of the voltage analysis conducted upon the AE data for each sub-core, which demonstrated that a correlation exists between low to high energy AE events at or within close proximity (5 MPa) to the pre-load stresses. Therefore, it was postulated that assigning a weighting to each AE event, which is equal to the square of its voltage, will simultaneously decrease the influence of AE events associated with ‘settling’ of the rock mass, whilst increasing the influence of large energy AE events associated with new damage of the rock mass. This method successfully estimated the maximum pre-load stress in all of tests, to within 3% accuracy. Furthermore, this method was successful in both lateral and axial sub-cores, and therefore, the orientation of the sub-core is believed to not influence the stress being estimated, as it is the principal pre-load stress which is always prominent.

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Tamaki, K., & Yamamoto, K. (1992). Estimating in-situ stress field from basalt rock core samples of hole 794c, Yamato Basin, Japan Sea. Proceedings of the Ocean Drilling Program, Scientific Results, 127, 2-8. Tang, C. A., & Hudson, J. A. (2010). Rock Failure Mechanisms – Explained and Illustrated. Taylor & Francis. Tuncay, E., & Ulusay, R. (2008). Relationship between KE (KE) levels and prestresses applied in the laboratory. International Journal of Rock Mechanics and Mining Sciences, 45(4), 524–537. doi:10.1016/j. ijrmms.2007.07.013 Villaescusa, E., Seto, M., & Baird, G. (2002). In-situ Stress Measurements Using Oriented Core. International Journal of Rock Mechanics and Mining Sciences, 39(5), 603–615. doi:10.1016/S13651609(02)00059-X Villaescusa, E., Seto, M., & Baird, G. (2005). In-situ Stress Measurements Using Acoustic Emission from Cored Rock. International Journal of Rock Mechanics and Mining Sciences, 40, 514–517. Villaescusa, E., Windsor, C., & Machucha, L. (2011). Stress measurement from oriented core – a decade of results. WA School of Mines – Curtin University. Wawersik, W. R., & Fairhurst, C. (1970). A study of brittle rock fracture in laboratory compression experiments. International Journal of Rock Mechanics and Mining Sciences, 7(5), 561–564. doi:10.1016/01489062(70)90007-0 Wood, B.R.A. & Harris, R.W. (1991). Structural Integrity Evaluation Using Acoustic Emission Techniques. Reliability Production and Control in Coal Mines, 62-65. Yamamoto, K. (2009). A theory of rock core-based methods for in-situ stress measurement. Earth, Planets, and Space, 61(10), 1143–1161. doi:10.1186/BF03352966 Yamshchikov, V. S., Shkuratnik, V. L., & Lavrov, A. V. (1994). Memory effect in rocks [review]. Journal of Mining Science, 30(5), 463–473. doi:10.1007/BF02047337 Yamshchikov, V. S., Shkuratnik, V. L., & Lykov, K. G. (1990). Stress measurement in a rock bed based on emission memory effects. Soviet Mining Sciences, 26(2), 122–127. doi:10.1007/BF02506524 Yoshikawa, S., & Mogi, K. (1981). A new method for estimation of the crustal stress from cored rock samples. Tectonophysics, 74(3-4), 323–339. doi:10.1016/0040-1951(81)90196-7 Yuan, R. F., & Li, Y. H. (2008). Theoretical and experimental analysis on the mechanisms of the KE of acoustic emission in brittle rocks. Journal of University of Science and Technology Beijing, 15(1), 1–4. doi:10.1016/S1005-8850(08)60001-8

ADDITIONAL READING Gatelier, N., Pellet, F., & Loret, B. (2002). Mechanical damage of an anisotropic porous rock in cyclic triaxial tests. International Journal of Rock Mechanics and Mining Sciences, 39(3), 335–354. doi:10.1016/ S1365-1609(02)00029-1

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Griffith, A. A. (1921). The phenomena of rupture and flow in solids. Philosophical Transactions of the Royal Society, 221(582-593), 163–198. doi:10.1098/rsta.1921.0006 Hardy, H., Zhang, D., & Zelanko, J. 1989. Recent studies of the Kaiser effect in geologic materials, Proceedings of the fourth conference on acoustic emission/micro-seismic activity in geologic structures and materials, 27-55. Hsieh, A., Dight, P., & Dyskin, A. V. (2014). Ghost Kaiser effect at low stress. International Journal of Rock Mechanics and Mining Sciences, 68, 15–21. doi:10.1016/j.ijrmms.2014.02.005 Kent, L., Bigby, D., Coggan, J., & Chilton, J. 2002. Comparison of acoustic emission and stress measurement results to evaluate the application of the Kaiser effect for stress determination in underground mines, 21st International Conference on Ground Control in Mining, 21:270-277. Kuwahara, Y., Yamamoto, K., & Hirasawa, T. (1990). An experimental and theoretical study of inelastic deformation of brittle rocks under cyclic uni-axial loading, Tohoku Geophysical Journal, Faculty of Science. Tohoku University, 33, 1–21. Lavrov, A. (2001). Formation and manifestation of memory effects in rocks. Moscow State Mining University. Li, Y., & Schmitt, D. R. (1997). Effects of Poissons ratio and core stub length on bottomhole stress concentrations. International Journal of Rock Mechanics and Mining Sciences, 34(5), 761–773. doi:10.1016/ S1365-1609(97)00001-6 Momayez, M. J., & Hassuni, E. P. 1992. Application of Kaiser effect to measure in-situ stresses in underground mines, The 33th U.S. Symposium on Rock Mechanics. Shkuratnik, V., & Lavrov, A. 1998. Numerical simulation of the Kaiser effect under tri-axial stress state, Proceedings of the Third International Conference on Mechanics of Jointed and Faulted Rock, 381-385. Simmons, G., Siegfried, R. W., & Feves, M. (1974). Differential strain analysis: New method for examining cracks in rocks. Journal of Geophysical Research, 85, 7071–7100. Siqing, Q., Sijing, W., Long, H., & Liu, J. (1999). A new approach to estimating geo-stresses from laboratory Kaiser effect measurements. International Journal of Rock Mechanics and Mining Sciences, 36(8), 1073–1077. doi:10.1016/S1365-1609(99)00068-4 Stevens, J. L., & Holcomb, D. J. (1980). A theoretical investigation of the sliding crack model of Dilatancy. Journal of Geophysical Research, 79, 4383–4385. Strickland, F. G., & Ren, N. K. 1980. Use of differential strain curve analysis in prediction in-situ stress state in deep wells, 21st Proceedings U.S. Symposium on Rock Mechanics, 523-532. Stuart, C. E., Meredith, P. G., Murrell, S. A. F., & Van Munster, H. 1995. Influence of anisotropic crack damage developed on the Kaiser effect under true tri-axial stress conditions, Proceedings of the Fifth Conference on AE/MA in Geological Structures and Materials, Trans Tech Publications, 205-209. Walsh, J. B. (1965). The effect of cracks on the compressibility of rock. Journal of Geophysical Research, 70(2), 381–389. doi:10.1029/JZ070i002p00381

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Wang, H., Ren, X., & Tao, R. (2011). Identification methods of the deformation memory effect in the stress region above crack initiation threshold. Procedia Engineering, 26, 1756–1764. doi:10.1016/j. proeng.2011.11.2364 Wang, H. J., Hsieh, A., Dight, P., & Dyskin, A. V. (2012). The mechanism of the deformation memory effect and the deformation rate analysis in layered rock in the low stress region. Computers and Geotechnics, 44, 83–92. doi:10.1016/j.compgeo.2012.03.006 Watanabe, H, Tano, H, Akatsu, T, 1994. Fundamental study on pre-stress measurement of tri-axial compressed rock, J Coll Eng Nihon Univ, 35(A):11-19. Yamamoto, K., Kuwahara, Y., Kato, N., & Hirasawa, T. (1990). A New Method for In-situ Stress Estimation from Inelastic Deformation of Rock Samples under Uni-Axial Compressions, Tohoku Geophysical Journal, Faculty of Science. Tohoku University, 33(2), 127–147.

KEY TERMS AND DEFINITIONS Acoustic Emission (AE): Bursts of high frequency elastic waves emitted by the localised failure of a material when it is subjected to a loading. The generation of AE within rock materials occurs when they are subjected to a stress of sufficient magnitude to induce pore compression and micro cracking. The phenomenon behind the use of the AE technique is that a lack of micro cracking, and accompanying AE activity, occurs when a rock material is loaded at levels below its previous stress state. Cumulative AE Hits: Total number of acoustic emission signals over threshold voltage level during loading of rock samples. Felicity Ratio (FR): The ratio of the estimated stress to the pre-loaded stress. Kaiser Effect (KE): The phenomenon behind the use of the AE technique is that a lack of micro cracking, and accompanying AE activity, occurs when a rock material is loaded at levels below its previous stress state (σm). At the previously ‘memorised’ maximum stress (σm), there is an abrupt increase in the level of micro cracking and collapsing of pores within the material. This closure and propagation of fractures is associated with a significant increase in AE activity is known as Kaiser Effect, first discovered by Joseph Kaiser in the mid-20th Century. Master Cores: 63.5 mm core samples extracted from main borehole. The Inflexion Point: The inflexion point marks the point of the KE with the corresponding stress representing the previously applied stress (See Figure 1). The University of Adelaide (UoA) Method: The method that uses acoustic emission energy to locate point of inflexion by the authors of the chapter in 2014. Threshold Voltage: A voltage level used to eliminate outside noises during loading of rock samples.

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Seismic Microzonation and Site Effects Detection Through Microtremors Measures: A Review

Francisco Alberto Calderon National Technology University, Argentina

Hernan Rodriguez National Technological University, Argentina

Emilce Gisela Giolo National Technological University, Argentina

Miguel Tornello National Technological University, Argentina

Carlos Daniel Frau National Technological University, Argentina

Fabian Lujan National Technological University, Argentina

Marcelo Gerardo Jesús Guevara Rengel National Technological University, Argentina

Ruben Gallucci National Technological University, Argentina

ABSTRACT Seismic microzonation of a city can be a difficult and expensive undertaking depending on the method used. In the last years, the HVSR method has been one of the most popular ways to define the natural frequency of the soil and seismic amplification factor in order to make quick microzonations due to that it is an expeditious and cheap method. This is very important in developing countries and poor countries. The fundamental reason to use this method is the fact that the amplification factor has well correlation with damage distribution. Additionally with the help of another methods it is possible obtain the structure of the superficial soil strata. In this chapter, an introduction with seismic microzonation, site effects concepts, microtremors, description of the HVSR method, advantages and disadvantages of this method, limitations and comparison with other methods, are presented. Finally, highlight of the importance of the method in order to identify site effects are displayed as examples and the incorporation of these data to Geographic Information Systems is also shown.

DOI: 10.4018/978-1-5225-2709-1.ch009

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 Seismic Microzonation and Site Effects Detection Through Microtremors Measures

INTRODUCTION According to the Earthquake Glossary of U.S. Geological Survey, “Seismic Microzonation” is the identification of separate individual areas having different potentials for hazardous earthquake effects. This individualization of areas is needed to the territorial ordainment of cities that are near to seismic sources in order to reduce the seismic risk and the vulnerability of the buildings. Some of the potential hazards are liquefaction, landslide, rock fall, site effect, topographical variations, tsunamis and others. The liquefaction is a process by which water-saturated sediment temporarily loses strength and acts as a fluid, like when someone wiggles its toes in the wet sand near the water at the beach. This effect can be caused by earthquake shaking. Microzonation provides the basis for site-specific risk analysis, which can assist in the mitigation of earthquake damage. The first microzonations were based on properties of the superficial layers of the soil in a specific site, for examples the number of Standard Penetration Test, this is the case of the first seismic code of the city of Mendoza, Argentina, where there were three types of soil with different seismic demand, based on number of SPT and bearing capacity. More recently, predominant period, amplification factor, shear wave velocity, plasticity index and undrained shear strength are used for the identification of the different zones inside a city. Seismic microzonation of a city can be a difficult and expensive undertaking depending on the method used. For example the determination of Vs30 is one of the principal ways to define the Site (spectrum) (UBC, 1997) (IC 103, 2013), but this method is expensive, in certain occasions it is not possible to obtain and can be misleading in many cases (Pitilakis, 2004), also the use of Vs30 as a proxy to seismic amplification has been questioned by several recent works (Pitilakis, Riga & Anastasiadis, 2013). The modern seismic microzonation contemplated maps with faulting, site periods and amplification, peak ground velocity and peak ground acceleration. These maps usually are integrated in a geography information system. The Figure 1 shows the faulting in the nearby of Mendoza city and the Figure 2 shows the frequency of the soil of Mayagüez, Puerto Rico (Ritta, Suárez & Pando, 2012). In the last years, the HVSR (horizontal to vertical ratio spectra) method also called Nakamura´s, method or QTS (quasi transfer spectrum) has been one of the most popular ways to define the natural frequency of the soil, but not the seismic amplification, due to that it is an expeditious and cheap method (Nakamura, 1989), (Nakamura, 2000), (Nakamura, 2008), this is demonstrated by the fact that the first work of Nakamura has been cited more than 1000 times in international journals and books. Even this method has been used for the determination of the structure of the surface soil layer of the moon with the data recorded by the instruments placed by the Apollo 14 and 16 missions. Additionally with the help of another method it was possible to obtain the structure of the superficial soil strata (Dal Moro, 2015). Due to this and the troubles that the Vs30 presents, there are new proposals of Site Classification based on the fundamental period of soil deposit (Pitilakis, Riga & Anastasiadis, 2013). Therefore the two principal objectives of microzonatios are fulfilled by the HVSR, first the amplification factor (partially) and second the shape of the spectrum and that includes the fundamental period of soil deposit.

BACKGROUND When we talk of “site effect” we refer to amplifications of the movement due to seismic waves that take place in a punctual site. This effect, also called “bowl of jelly”, occurs when certain geological condi327

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Figure 1. Faulting map of Mendoza city, Argentina (Segemar)

Figure 2. Map of the resonance frequency of the soil of Mayagüez, Puerto Rico (Ritta, Suárez & Pando, 2012)

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tions that permit it exist, for example in certain type of sediments or alluvial basins. Local site effect can influence in the characteristic of a strong motion like amplitude, frequencies and the time that the motion is significant for the civil structures. A typical example of this phenomenon was the earthquake that occured in Mexico in 1985 (Mw = 8.0), where the epicenter was located to 400 km from the city of Mexico, however the duration of the motion was nearly double, and the PGA (pick ground acceleration) bigger than the epicenter surroundings. Hence the local site effect depends on several variables; the topographic of the site, the characteristic of the surface layers of soil, depth of the underground water level and of course the characteristic of earthquake. Physically, the site effect consists on a conservation of elastic energy. When the seismic wave through a layer with a different density (ρ) and shear wave velocity (vs), in order to maintain the same energy (of course, regardless of the energy dissipated by damping and diffusion) there is only one way, modify the velocity of the soil particle (ů), this is the energy flux ρvsů2 (Kramer, 1996). Of course, the amplification of the motion depends on the ξ (damping ratio) H (thickness of layer) and vs that represent the stiffness of the layer. A simple model of damped soil (one uniform layer of soil) response on rigid rock is represented by the equation 1. The figure 3 shows the response of the soil using this model. This is a 1D (one dimension) model of behavior of soil. It can also be developed 2D and 3D models and nonlinear behavior. It is also possible to make models where layers of soil are included. F (ω) =

1 2

cos (ωH / vs ) + [ξ(ωH / vs )]2



(1)

The topographic effects are produced by the form of the trough or crest of a valley. The amplification of the seismic waves depends on the convex or concave form of the topography. Faccioli (1991) presents a study on amplification in presence of topographic accidents where the influence of the shape of the site is showen (figure 4). An example of this effect is the accelerogram recorded in Pacoima Dam during the earthquake of San Fernando (1971) where the PGA is 1.25g which is large enough for a ML= 6.4 earthquake. Figure 3. Response for different damping ratios for a site with one layer of H= 100 m and vs= 360 m/s.

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Figure 4. Topographic irregularity (solid line) with wedge at points 1 and 2. Reproduced with permission from Ezio Faccioli.

Depth of the underground water level also caused a type of site effects called liquefaction. One of the most severe cases of liquefaction occurred in 1964 Niigata earthquake (Mw= 7.6) where several buildings were collapsed because the ground lost its bearing capacity. The liquefaction is more often in saturated sandy soils.

MICROTREMORS, SITE EFFECT, AND MICROZONATION One of the first authors that took of microtremors was Fusakichi Omori in Japan on early 1900. Omori was a pioneer in measurement of vibrations of soil and structures like buildings and bridges. He defines the microtremors as vibrations with periods usually less than 1 second, and whose amplitude is very small. He concluded that the period of microtremors is similar to the period observed during earthquakes (Omori, 1908). Then Kanai and Tanaka continued to study the properties of microtremor. They found good correlations between the frequency content of microtremors and earthquakes for the same places and relations between the geology of sites and the predominant period of microtremors (Kanai & Tanaka, 1961). But these correlations are not always fulfilled. For example Udwadia and Trifunac also studies the effect of microtremors (records of 40 seconds) and it relation with El Centro earthquake, but the results did not have good correlation (Udwadia & Trifunac, 1973). Alcock (1974) had the opinion that Udwadia and Trifunac failed to obtain positive correlations because they mistakenly interpreted sharp peaks in the Fourier spectra as being representative of the resonant period of the ground. He suggested that spectral amplitude be plotted on a logarithmic scale rather than a linear scale, thereby diminishing the high peaks while enhancing the weak trends. On the other hand Katz carried out long-time measurements of microtremors (records of 40 minutes) and had better results compared with transfers function of the same site (Katz, 1976). Then the study of several Japanese authors on microtremor led to the development of methods that linked the horizontal and vertical spectra study site properties, where the Nakamura’s method was the most popular (Nogoshi & Igarashi, 1971), (Shiono, Ohta & Kudo, 1979), (Kobayashi, 1980) and (Nakamura, 1989).

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HVSR METHOD Method Description It has been shown in several studies that the effects of earthquakes on structures vary considerably. This also occurs in the different layers of the soil, so it is important to study the dynamic characteristics of the soil surface. The microtremors also called ambient noise have been used since 1900 and currently have increased its use in dynamic studies of soil and structures. This kind of vibration can be caused by natural sources, characterized by long periods (also called microseisms) and mainly composed of Rayleigh waves, or artificial presenting short periods (known as microtremor) and are composed of S waves and Rayleigh waves. The technique of horizontal/vertical spectral ratio (HVSR) to evaluate the effect of the site, relates the horizontal and vertical components of the movement, so we can estimate the fundamental frequency of soil from ambient vibration measurements. This technique was developed by Nakamura and assumes that microtremors consist of many types of waves, waves of surface type (Rayleigh) and body waves (P and S) propagating in a soft sediment layer which overlies one rock basement and that both the horizontal component and vertical of movement have the same effect. If a surface source body generates waves in a medium consisting of a soft layer over a layer of rock with a great difference of impedance, these waves propagate in a complex pattern, depending on the original source and its location. Due to reflections, that occur between soil and rock, part of the P and S waves move from the basement to the free surface. Nakamura said that the vertical (Vf) and horizontal movement (Hf) of soil on the free surface can be interpreted as the sum of body waves traveling from the rock basement combined contribution of surface waves. The effect of Rayleigh waves can be estimated based on the function of transfer T (ω) (Nakamura, 2000). The Hf (ω) and Vf (ω) components are respectively the Fourier transforms of the time series of horizontal and vertical motion at the surface respectively considering the contribution of the two types of wave (body and surface). The Hf (ω) and Vf (ω) spectra are not useful to identify the natural frequencies of deposit because they also contain the dominant frequencies of the sources that generated waves. The ratio of Hf (ω) and Vf (ω), Nakamura called QTS by the acronym of Quasi Transfer Spectrum. Nakamura considered that Hb (ω) and Vb (ω) are equal to the spectra on a rocky outcrop, which called Hr (ω) and Vr (ω) (Figure 5). This can be considered as an approximation because although the peaks between these values match, the magnitudes do not. Figure 5. Typical geological structure of a sedimentary basement (Ritta, Suárez & Pando, 2012)

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If you have the amplitude spectra in the rock layer that is Hb and Vb, it could make the ratios between the horizontal and vertical components between the surface layer and the rock basement, obtaining transfer functions (Equation 2) Th (ω) and Tv (ω) that would eliminate the effect of the source and obtain the natural frequencies of the soil. If the value of Tv (ω) is close to 1, then the effect of Rayleigh waves is near zero. Vf ( ω ) TV (ω ) = Vb (ω )

Th (ω ) =

H f ( ω)

H b (ω )



(2)

It is often difficult to have the values of the components of movement in the rock basement Hb and Vb, so Nakamura proposes the use of a frequency function modified Tm (ω), which relates the functions of transfer Th (ω) and Tv (ω): Th (ω) H f (ω) / H b (ω ) Tm (ω ) = =  TV (ω ) Vf (ω) / Vb (ω )

(3)

Nakamura considers that for a wide range of frequencies where there is a firm substrate, the ratio Hb (ω) and Vb (ω) is approximately equal to one, then the ratio will be: Tm (ω ) =

Th ( ω)

TV (ω )

=

H f ( ω) Vf ( ω )



(4)

It can be shown both theoretically and empirically that regardless to the type of waves and their relative importance in the records of surface movements Hf (ω) and Vf (ω), the relationship between them is that the ratio H/V, presents a peak consistent with the fundamental frequency ωo the deposit of soil associated with the horizontal vibrations (Ritta, Suárez & Pando, 2012).

Data Processing Microtremors registers can be processed through the different software, for example: “Geopsy”, developed by the SESAME European project (SESAME, 2005) or Degtra by UNAM of Mexico (Ordaz & Montoya, 1999). Geopsy is a graphic interface, made specifically for this kind of studies. It was designed for seismology and geophysical prospection. Geopsy can determinate relations between horizontal and vertical Fourier spectrum amplitude components of the environmental noise recorded on each station. The H/V spectral relation was calculated following the next steps: 1. Signal acquisition: from recorded noise vibrations on each station in three directions: North to South, East to West and Vertical. Identification of every direction in the software and write the sensor’s settings.

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2. Windows selection: an anti-triggering algorithm was used to eliminate the frequency peaks associated with transient noise like heavy duty traffic. The method compares every window and it is previous with two values: a long term average and a short term average. 3. Fourier spectra calculation and softening: It was calculated using square media of both horizontal components. 4. Graphical representation of every window’s H/V spectral relation. 5. Determination and representation of mean H/V spectral relation. 6. Verification of every result using software tools and validation with nearby data. An example of the configuration parameters for data processing is shown in the table 1. The parameters used must be calibrated according to each measurement performed for example see (Komazawa, Morikawa, Nakamura, Akamatsu, Nishimura, Sawada, Erken, & Onalp, 2002) and (Özdag, Gönenç, & Akgün, 2015).

Advantages and Disadvantages of Method Among the advantages of the method proposed by Nakamura (HVSR), we can mention that it is a simple technique which gives quick information about the soil dynamic characteristics; it also offers easiness in collecting data since the instruments used for measurement are simple and easy to operate. This method can use both sensors that measure velocity as acceleration. It can be applied in different areas, with low, high or even without seismicity, the occurrence of a seismic event to use the method is not required. The method enables observation at any time and period of time, without restriction according to population activities, in contrast to other geotechnical studies difficult of making in urban areas. And the most attractive advantage is that the method only needs one sensor to obtain the predominant period and factor amplification of the site. Figure 6. Visualization of signals

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Figure 7. Windows selection with anti-triggering

Figure 8. Fourier spectra of vertical, and two horizontal component of microtremors

The H/V method (Nakamura, 1989) has proved to be a suitable technique for estimating the fundamental frequency of soft deposits. The extensive use of this method allows a mapping of this frequency within urban areas, as well as the predominant periods and relative amplification factor. If borehole data can be used for calibration, the procedure has the potential to allow estimates of average shear wave velocity of the unconsolidated sediments. The results can also be used to recognize thick layers of seismically soft soils, which are not resolved by borehole data (Fäh, Kind, & Giardini, 2000). Another important factor today is that the measurement of microtremors does not generate environmental impact. Regarding the disadvantages of the method, several authors agree that the proposed technique by Nakamura presents some theoretical gaps, because it is not clear that horizontal component considered for calculating the spectral ratio.

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Figure 9. Representation of H/V spectral relation with all the time windows (colors lines), average (continues line) and +/- standard deviation (dashed line)

Table 1. Configuration parameters for signal processing Configuration Parameter Type Time

Processing

Output

Description

Value

anti-triggering

Detects transient noises and remove them from the processing.

Software default

sta (short term average)

Average amplitude of short term signal, it varies from 0.5 to 2 seconds

Software default (1 sec.)

lta (long term average)

Average amplitude of long term signal, it varies within dozens of seconds.

Software default (30 sec.)

sta/lta

Relation between short term average and long term average; it may vary from a maximum of 2.5 seconds to a minimum of 0.2 seconds.

Software default (minimum 0.20; maximum 2.50)

smoothing type

Graphical smoothing method

Konno y Ohmachi

smoothing constant

Value of the smoothing method

40

Cosine tapering

Width (%) of cosine tapering.

5%

Horizontal component

To perform H/V calculations, a square average between horizontal components was selected.

Squared average

Sampling frequency

Range of frequency shown in the H/V graphics

from 0.10 Hz a 35 Hz.

Number of samples

It refers to the number of lines in exported plain text from graphical data.

400

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Parolai et al. (2004) made a study in Cologne, Germany. In this study they compared the site response from the H/V ratio using ambient noise recordings with that obtained from earthquake recordings for the area of Cologne. They observed variability in the amplitude of the peak of the H/V ratio of ambient noise (the amplification factor). Such variability indicates that the relative variations of the amplification in the H/V ratio from site to site should be checked with repeated measurements before using the results for engineering purposes. They demonstrate in their examples that all components of the Fourier spectra should be carefully reviewed before calculating the ratio H/V. In fact, strong and continuous industrial signals can lead to misinterpretation of the H/V ratio, especially in areas where it is expected that the peak fundamental frequency resonance occurs at frequencies greater than 1 Hz. For example a single way to know if there are industrial signal and anthropogenic noise is watching the spectrogram of each signal (two horizontal and vertical). Figure 10 shows a measure of ambient vibration where an industrial signal was detected with the help of the spectrogram. Also, if the signal has a rare frequency during all the record, it is possible to isolate the frequency through filters and to analyze the damping of this frequency. If the damping is near to zero, it is probable that this frequency comes from a machine. Another way to detect this type of signals is making the HVSR spectrum vs. time, as seen in figure 11, so when the HVSR is done, overlaying all windows is not possible to know if a peak lasts the entire record or only a short period of time. Besides the frequency of the soil is always present while a frequency of a machine or anthropogenic noise is present only in a certain period of time. Another disadvantage is that there is currently no general agreement on the methodology for data collection and subsequent processing thereof, as well as the interpretation of the results. Studies conducted in recent years highlighted the importance of the influence of an interface between the measuring equipment and soil, as well as the weather conditions, nearby structures, transient noise, etc. in the reliability of the results obtained (Chatelain, Guillier, Cara, Duval, Atakan, & Bard, 2008). The wrong way to operate the equipment can be seen reflected in subsequent results, so it is often necessary to check devices with accurate data in areas known. Very few studies have evaluated the influence on experimental results obtained with the Nakamura technique and other techniques for microtremor measuring. Figure 10. EW Component of a signal with an industrial frequency present in a part of time recorder

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Figure 11. HVSR vs. time recorder

Chatelain, et al. (2008) mention in their conclusions the importance of checking the recorded signal before proceeding to computational analysis of the H/V, because they can lead to severe misinterpretations due to ill recorded signal, for example, from recordings on ice. Also, in this paper, they show the results obtained when recording under different meteorological conditions and on different surfaces, for example, all the sensors installed on the firm ground, without any protection against wind, under the rain, with the sensor under lid cover and without cover; on grass without wind, on grass with some wind and grass with strong wind, among others. They also give various recommendations and guidelines to make the recordings more efficiently.

Limitations As for limitations, we can say that the method proposed by Nakamura, does not specify which of the horizontal components considered for analysis, and in places where the geology is complex cannot determine the seismic wave amplification. There is some discussion about the applicability of short period microtremor to estimate the dominant period in soft soil, mainly due to the difficulty of separating the source of the site effects. In fact, the shorter the period of microtremor, the stronger reliance on local sources that excites them, so it is difficult to interpret variations from one area to another (Aki, 1988). Many authors agree that the technique had disadvantages in accurate estimation of the amplification factor due to the effect of an unknown source. They suggested that it was impossible to separate the source of local site effects of what constituted the main obstacle to the use of microtremor measurements. However, Lermo and Chávez-García (1994) applied three different techniques for measuring microtremor in soft soil and evaluated response site, included both time domain as the amplification level, when the local geology is relatively simple. One of the techniques used was proposed by Nakamura, and sug-

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gested that it effectively compensates for the effects of origin microtremor measurements, eliminating an important limitation on application of this technique in seismic engineering. Bonefoy-Claudet et al. (Bonnefoy-Claudet, Köhler, Cornou, Wathelet, & Bard, 2008), emphasize the possibilities and limitations of the method H/V for horizontally stratified media, and conclude that the method provides a good estimate of the resonance frequency, whereas, in most cases (when surface waves dominate the ambient noise waves field), the method does not provide the correct amplification factor. At the peak frequency of the H/V ratio, the relative contribution of different types of seismic waves (body and surface waves) for the simulation of ambient noise wave field heavily depends on the characteristics of the source (especially in the direction of the force amplitude of origin), and on the impedance contrast between the sediment and the bedrock. In subsequent studies Nakamura (2000) has maintained his original opinion that H/V ratio in the range of peak frequency are not affected by the fundamental mode of Rayleigh waves, allowing a direct comparison with the S waves transfer function. He explained the peak as a result of the vertical incidence of SH wave. This contradicts the results of Lachet and Bard (1994), who suggested that the peak can be explained with the fundamental-mode Rayleigh wave. Also Fäh et al. (2000), have applied two methods to compute average the H/V ratios, the classical method in the frequency domain and other based on the frequency-time analysis, which allows one to locate P-SV wavelets in the time-series. Their results are in contradiction to the results obtained by Nakamura (2000). Even if the variability of the H/V ratio at the peak is high due to differences in the P–SV wave composition, no SH-wave resonance effect is needed to explain observed H/V ratios. The fundamental-mode Rayleigh wave is not the dominant wave type at this peak; rather, higher-mode Rayleigh waves are. Nevertheless, they are not able to draw conclusions about the contribution of SH waves to the peak of observed H/V ratios. From a single station measurement of ambient vibrations it is not possible to distinguish SH waves from P–SV waves. Guillier et al. (Guillier, Atakan, Chatelain, Havskov, Ohrnberger, Cara, Duval, Zacharopoulos, & Teves-Costa, 2008) presented the influence on the H/V spectral ratios of ambient vibrations, although these studies are of general interest for other kinds of seismological studies. They assess the reliability and accuracy of various digitizers, sensors and/or digitizer-sensor couples. In the case of sensors, three categories of sensors can be distinguished: accelerometers, broadband seismometers (below 0.2Hz), and short period seismometer (less or equal to 5 s). The purpose of their paper is to show the results of basic tests regarding the seismological equipment, documenting the influence of different combinations of commonly used digitizers and sensors on the final results of H/V spectral ratios. They showed that the use of accelerometers, at least those using the force balanced technology, is not recommended, as they are generally too unstable and not sensitive enough at low frequencies. In addition, they concluded that the digitizers are generally very accurate; most of the characteristics of the tested digitizers correspond well to the specifications given by the manufacturers, whereas the sensor influence is more complex and can generate some troubles. Another limitation of Nakamura´s method is that the solution does not allow to deduce the structure of a layered soil since a Quasi Transfer Spectrum can be the same (or nearly the same) for different layered soils structure. Dal Moro (2015) shows a HVSR similar for different layered soils (different thickness and Vs of layers) Horike et al. (Horike, Zhao, & Kawase, 2001) made a comparison of site response obtained from microtremors with Nakamura method and earthquake s-waves. They conclude that the fundamental frequency of the sites have good approaches but not in amplification. Seekins et al. (Seekins, Wennerberg, Margheriti, & Liu, 1996) also compared microtremors and wake motion s-waves and they find similar 338

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result than Horike. Additionally they conclude that the amplification factors at frequencies higher than 2 Hz are greater in microtremors data. Both papers also conclude that the higher modes of site are not visible with HVSR. On the other hand Mucciarelli (1998) carries out a controlled experiment on measurement of microtremor in order to apply HVSR and compared the result with earthquake data. The results of this experiment shows that, in controlled conditions, HVSR presents good approach to earthquake data.

COMPARISON WITH OTHER METHODS Method SPAC (Spectral - Auto Correlation) The method of spectral autocorrelation, also known as SPAC, is used to obtain the subsoil structure from the calculation of the coefficient of spectral auto correlation; this is accomplished from measurements of ambient vibration, using an array circular station for observing them. The SPAC coefficient can be calculated directly in the frequency domain using the Fourier transform of the observed microtremor. Aki (1957) showed that the ratio of the average of those different cross correlation functions and the autocorrelation function at a reference station (defined by him as the correlation coefficient) takes the form of a zero-order, first-kind Bessel function. In the argument of that Bessel function appears the fixed interstation distance, the frequency, and the phase velocity of the propagating waves. Then it was shown that a different arrangement of sensors to the circular array proposed by Aki could be used, which facilitated the observation of microtremor. In Figure 12, we can see a typical instrumental circular array. Chávez-García et al (2005) presented an extension of SPAC method, where phase velocity dispersion curves were obtained from data recorded using an irregular geometry of stations observation of microtremors. Often it is not possible to install the stations in the precise circular geometry required by SPAC (in an urban situation it becomes clearly impossible). However, if we are able to record microtremors Figure 12. Disposition of instrumental circular array. The (r,θ) are the polar coordinates of sensor.

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for a long enough time, and the ambient vibration wave field is stationary in time and space, we can substitute temporal average instead of the azimuthal average required by SPAC. However, this is valid only if the sources of ambient vibration or the subsoil structure do not impose a predominant direction of propagation. This allowed us to compute average cross correlation coefficients for many different distances using our datasets, as opposed with standard studies that use SPAC, where only two or three different distances are the norm (Chávez-García, Rodríguez, & Stephenson, 2005). In 2006, Chaves-García et al (Chávez-García, Rodríguez, & Stephenson, 2006) presented a new article, using a geometry of the array as different as possible from a circle at the same location that the previously article. In that paper, they presented the results of analysis, which support the use of the SPAC method without constraints in the geometry of the array. The results presented strongly suggest that the SPAC method does not require a particular geometry, thereby expanding greatly the possible applications for this method. However, using many distances to compute correlation coefficients makes it difficult to select a range of validity (in terms of wavelength) for the results. In terms of theoretical development, Aki (1957) considered the ground motion at two locations on the surface, (x , y ) and ( x + ξ, y + η ) , this can be written as u (x , y, t ) and u (x + ξ, y + η, t ) . The spatial autocorrelation function ϕ (ξ,η,t) for the two dimensional waves of a single velocity in terms of their spectrum density in space is defined as: φ(ξ, η, t ) ≡ u (x , y, t )u (x + ξ, y + η, t)

(5)

In the above equation, the bar indicates time averaging. On the other hand, the spectrum density in time of the wave Φ (ωn ) (Aki, 1957, equation 33) is:

(

)

2

2 1 U c (ωn ) + (U s (ωn )) Φ (ωn ) =  4 ∆ωn 2π

(6)

Where U c (ωn ) is the Fourier cosine coefficient and U s (ωn ) is the sine coefficient of u (x , y, t ) whit respect to time t for given x and y . Aki (1957), introduced the azimuthal average of the spatial autocorrelation: φ(r ) =

1 ∫ (r, Ψ )d Ψ 2

(7)

Where r and Ψ are the polar coordinates and (ξ, η ) have been defined as: ξ = r cos Ψ and η = r sin Ψ Aki (1957) showed a correspondence exists between the spatial autocorrelation function φ (r ) and the spectrum density Φ(ω) in equations 8 and 9 respectively as follow:

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Φ (ω ) =

φ (r ) =

∞  ω  π  r  r dr r J  ω φ ( ) 0 ∫ 2  c   c 0

1 π



∫ Φ (ω ) J 0

0

 ω   r  d ω   c 

(8)

(9)

Where ω is the angular frequency, J 0 is the Bessel function of first kind and zero order and c is the constant phase velocity. In the equation 10 of Aki (1957) is showed the result for the dispersive waves of two dimensions as: 1 φ (r ) = π

   ω  ∫ Φ (ω)J 0 c (ω) r d ω 0   ∞

(10)

In the above equation c (ω ) is a function of frequency ω for the constant velocity c. In the section 6, Aki (1957) mentioned the spatial autocorrelation of filtered waves, the azimuthally averaged autocorrelation function of the wave filtered (Aki, 1957) can be written as:  ω   φ (r ) ≡φ (r ,ω0 ) = P (ω0 )J  0  0 r  c (ω )    0

(11)

Where P is the power spectral density. The other terms have been defined previously. In equation 12, Aki (1957) have denoted the autocorrelation coefficient ρ (r ,ω0 ) as:  ω   0  ρ (r ,ω0 ) = J 0  r c (ω )    0

(12)

A more detailed deduction can be seen in Aki, K. (1957).

Method f-k – Frequency – Wavenumber - (Capon, 1969) The f-k method used the frequency and wavenumber. The estimation of the frequency-wavenumber power spectral density is of considerable importance in the analysis of propagating waves by an array of sensors. The conventional method of estimation employs a fixed wavenumber window, and, as a consequence, the wavenumber resolution is determined essentially by the natural beam pattern of the array of sensors. As a consequence, it has been shown that the wavenumber resolution of this method is determined primarily by the amount of incoherent noise which is present in the array of sensors, and,

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 Seismic Microzonation and Site Effects Detection Through Microtremors Measures

to a lesser extent, by the natural beam pattern of the array (Capon, 1969). The f-k method need greater amount of station that SPAC technics. “When microtremors are recorded through an array of vertical seismometers simultaneously recording, the characteristics of the Rayleigh wave propagation in the medium can be extracted and subsequently the soil structure and the local S-wave velocity profile can be estimated. For obtaining the Rayleigh wave dispersion curve, the frequency–wavenumber analysis (f–k) can be employed. The seismic noise can be characterized by a frequency–wavenumber spectral density function, which provides the information concerning the power as a function of frequency and the vector velocities of the propagating waves. This method is based on the fact that a stationary random process can be characterized by means of a spectral density function, which provides the information concerning the power as a function of frequency. In a similar manner, seismic noise can be characterized by a frequency–wavenumber spectral density function, which provides the information concerning the power as a function of frequency and the vector velocities of the propagating waves” (Mundepi, Galiana-Merino, Kamal & Lindholm, 2010). Capon, J. (1969) used a conventional method for estimating the f-k spectrum. He assumed that K sensors can be used to estimate the frequency-wavenumber spectrum P (λ, k ) , this estimate is based for the cross-power spectral density fjl (λ) (Capon, 1969): ∞

f jl (λ ) =

∑ ρ (m ) ε jl

imλ



(13)

m =−∞

ρjl (m ) =

π

∫ f (λ)ε

−im

jl

−π

d   2π

(14)

Where ρjl (m ) is the covariance matrix of the noise and = 2πfT is the normalized frequency. Also f is the frequency in Hertz and T is the sampling period of the data in second. The direct segment method the number of data points L in each channel is divided into M non overlapping blocks of N data points, L = MN . The Fourier transform of the data in the nth segment, jth channel, and normalized frequency λ, is [3]:

S jn (λ) = N

−1 2

N

∑am (N )j,m +(n−1)N

m =1

εimλ 



(15)

j = 1, …., K n = 1, …., M Where, am are weights which are used to control the shape of the frequency windows, and is assumed

that am = 1 . In the eq. 16, he assumes that P (λ, k ) as follow: 342

 Seismic Microzonation and Site Effects Detection Through Microtremors Measures

K

ik .(x −x ) 1 Pˆ (λ, k ) = 2 ∑w j w j* fˆjl (λ )ε j l K j ,l =1

(16)

Where, w j are weights which are used to control the shape of the wavenumber window used to

estimate P (λ, k ) and w j = 1 . A more detailed deduction can be seen in Capon, J. (1969).

SSR Method (Standard Spectral Ratio) This method was presented by Borcherdt (1970). The method is very simple and consists in a ratio between Fourier spectra of soil-site (Rs(ω)) and nearby rock-site (Rr(ω)) simultaneous records. A seismic record is composing by four factors, which can be expressed in time domain (convolution) or frequency domain (product): R(ω) = S (ω) ⋅ P (ω) ⋅ L(ω) ⋅ I (ω)

(17)

Where R(ω) is the Fourier spectra of the seismic record, S(ω) is the Fourier spectra of the source signal, P(ω) represent the Fourier spectra of the propagation between the source of the signal and the site, L(ω) represent the Fourier spectra of the local soil condition and I(ω) represent the Fourier spectra of the influence of the instrument of recording. When two sites (soil and rock) are closely the source, propagation and instrument influences are similar, then the ratio between both records is express by: Rs(ω) Rr (ω)

=

Ls(ω) Lr (ω)



(18)

This ratio provides the amplification of the frequencies on the site respect to the bedrock site. There are other methods like Generalized Inversion Scheme Technique (Andrews, 1986) and Coda Waves (Phillips & Aki, 1986) similar to the SSR method; they can be seen in respective references. A classification of different methods is presented by Pitilakis (2004). Unlike the methods described above, the method HVSR explained by Nakamura only needs one point of observation to obtain the natural period of the site in addition to good approximation of the extent to which the seismic wave is amplified in the site; the f-k and SPAC methods use a geometric arrangement of stations to obtain, from an investment of dispersion curves, a velocity profile shear wave site. All three methods use both vertical and horizontal component for analysis. The technique proposed by Nakamura analyzes Rayleigh waves, while technical f-k and SPAC methods analyze both Rayleigh waves such as Love waves. Lermo et al. (1994) evaluated the response in soft soils through microtremor measurement. They evaluated three techniques commonly used for the study. A common feature of these techniques is that all three assume that site effects are due to a soft single soil layer overlying an elastic half-space. They showed in the results that measurements of microtremor can be used to estimate the dominant period of a site with a very acceptable reliability in a frequency range of 0.3 to 5 Hz. The best results were obtained with the Nakamura’s technique, which also gave a rough estimate of the amplification seismic waves when the local geology is relatively simple. 343

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APPLICATION EXAMPLES Jimenez et al (Jiménez, García-Fernández, Zonno, & Cella, 2000) conducted a study of the use of graphical environment GIS to microzonation for the city of Barcelona, ​​Spain. This work introduces to Barcelona the concept of “hazard scenarios” through a single GIS tool. Predictive hazard maps have been obtained by several computational models and integrated by the software, bringing homogeneous results and allowing to update every model and data with new knowledge. Several simulated earthquakes have been set for different hazard scenarios. Then it could be validated by real ground shake maps. After collecting geotechnical data, local geology data, regional seismicity and reference earthquakes, a set of hundreds of essays through microtremores using Nakamura’s technique was added to the set. With all the ground data, calculations were performed. This joint application of empirical and analytical techniques brings a fast and efficient procedure to delimitate the areas where an in-depth surveys must be performed. A different approach was made by Cid et al, regarding seismic zonation of Barcelona and estimation of site effects. The authors made a classification of zones with similar behavior using manly transfer function using linear method (1–D) and PSA/PSV spectral values from different values of dumping. Given the uncertainty associated with data obtained, a Montecarlo simulation process was performed. Therefore, four zones were established using transfer function and PGA amplification. A comparison between this map and Nakamura’s method map were made. Variations prove that seismic noise data have to be taken carefully. Sometimes, main frequency is not related with the damage map like (Guéguen, Chatelain, Guillier, Yepes, & Egred, 1998). A comparison between damage zone and main frequency map using seismic noise recordings exhibit some discrepancies. Using H/V technique, a second peak was detected on the most damaged zone, associated with alluvial deposits. This frequency is similar to the typical adobe local constructions. As noted in Mexico, the amplitude level and spectral content of earthquakes can be strongly modified by local site conditions, even in areas with moderate seismic hazard like Barcelona, Spain (Cadet, Macau, Benjumea, Bellmunt, & Figueras, 2011). Therefore, the estimation of the effects of site became a key issue in order to avoid damage caused by earthquakes. Empirical relationships have to be considered for a cheap and reliable outcome evaluation. The relations based on the time average speed during the first 30 meters of depth, and f0, the fundamental resonance frequency, provides prediction equation site amplification.

GIS (GEOGRAPHIC INFORMATION SYSTEM) The results of the methods explained in the preceding paragraphs are processed using a mapping software GIS (geographic information system). This graphical environment allows to overlay traditional maps, points, forms, etc., allowing the user to perform calculations such as estimation values between points (reverse to the distance triangulation). Points coordinates surveyed with values of its natural frequency are marked on the map. An interpolation of intermediate values to obtain a color map with a range of values of natural frequencies, or contour maps of iso-frequency or iso-periods is then performed. Categorization of regions with similar values of natural frequencies is the first step to a seismic zoning, dividing the study area into smaller areas and marking the zones of greatest risk. The integrated GIS allow to extract

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information regarding intermediate or inaccessible areas for studies and correlate data from different analysis. The main advantage of using GIS software is the possibility to incorporate different maps and combine them together obtaining risk analysis in a single graphical tool. Data from local geology, geotechnical models, calculation of soil response through 1-D analytical method can be added as well. The amplitude level, the spectral content of seismic movements and the level of damage from earthquakes can be affected by local conditions of each site, even in areas with moderate seismic hazard, amplifications damage may occur. For example, with a database geared to GIS with values of shear velocity in 30 meters depth (VS30) inferred from geotechnical testing like SPT, can be combined with areas of similar values of natural frequency to achieve a deeper analysis of local site effects. For more accuracy with respect to local vulnerability, GIS map can be created with different types of constructions in each zone and typical structure main frequencies. This allows us to analyze the possibility of resonance between soil and structures in all areas under study. Together with these data in GIS, modeling of various scenarios of large-scale earthquakes is possible. In Mendoza, Argentina a nearby fault earthquake of Mw= 7.0 can be modeled, due to the presence of such structures in the subsoil of the region; as well as a large-scale earthquake in the subduction area of the Nazca and South American plates (border with Chile). With these scenarios, models allow us to predict the percentage of total damage level to structures, total number of deaths associated, the areas of greatest damage; and allows the correlation with maps of major damage in earthquakes recorded in the area as model validation studies. It is possible to infer the thickness of the sediment layer located in each region across the map of geological and geotechnical zoning combined with the graph of the fundamental periods obtained by the method of spectral ratios (H/V), and contrast these studies with geotechnical surveys for validation.

CONCLUSION In this chapter a brief review of seismic microzonation and site effects were made. The importance of these two issues is that cities and infrastructures are located in different geological sites and it is necessary to know the behavior of these sites in the face of seismic events. The HVSR technique is a powerful tool for the determination of the predominant frequency of the site through the measurement of environmental vibrations or earthquake records. The major advantage of this method is that in comparison with other methods, only one seismic station (a seismometer or an accelerometer) is needed and this makes the application of HVSR faster and cheaper, which is an advantage in developing countries. One of the major limitations of this method is that the dynamic amplification factor cannot be determined with environmental vibration measurements. On the other hand, for a fully seismic microzonation more studies are needed, like determination of the velocity of the shear wave (Vs), geology of the place, fault geology mapping, and dynamic amplification factor in order to have a better comprehension of the site effect. With these data sets it is also possible to generate computational models that simulate the response of a city to the occurrence of an earthquake. An example of this is presented in the work of Podestá and Sáez (Podestá, & Sáez, 2017) in the city of Viña del Mar, Chile. The incorporation of these parameters to a GIS is a powerful tool for a land use planning and the assessment of the seismic vulnerability of cities; as well as the incorporation of maps in seismic codes with the goal to reduce the seismic hazard.

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Dal Moro, G. (2015). Joint analysis of Rayleigh-wave dispersion and HVSR of lunar seismic data from the Apollo 14 and 16 sites. Icarus, 254, 338–349. doi:10.1016/j.icarus.2015.03.017 Fäh, D., Kind, F., & Giardini, D. (2000). A theoretical investigation of average H/V ratios. Geophysical Journal International, 145(2), 535–549. doi:10.1046/j.0956-540x.2001.01406.x Faccioli, E. (1991). Seismic Amplification in the Presence of Geological and Topographic Irregularities. International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics. Guéguen, P., Chatelain, J. L., Guillier, B., Yepes, H., & Egred, J. (1998). Site effect and damage distribution in Pujili (Ecuador) after the 28 March 1996 earthquake. Soil Dynamics and Earthquake Engineering, 17(5), 329–334. doi:10.1016/S0267-7261(98)00019-0 Guillier, B., Atakan, K., Chatelain, J. L., Havskov, J., Ohrnberger, M., Cara, F., & Teves-Costa, P. et al. (2008). Influence of instruments on the H/V spectral ratios of ambient vibrations. Bulletin of Earthquake Engineering, 6(1), 3–31. doi:10.1007/s10518-007-9039-0 Horike, M., Zhao, B., & Kawase, H. (2001). Comparison of Site Response Characteristics Inferred from Microtremors and Earthquake Shear Waves. Bulletin of the Seismological Society of America, 91(6), 1526–1536. doi:10.1785/0120000065 Reglamento INPRES – CIRSOC 103. (2013). Reglamento Argentino para Construcciones Sismorresistentes. Parte I: Construcciones en general. INPRES, INTI – CIRSOC, Buenos Aires, Argentina. (in Spanish) Jiménez, M. J., García-Fernández, M., Zonno, G., & Cella, F. (2000). Mapping soil effects in Barcelona, Spain, through an integrated GIS environment. Soil Dynamics and Earthquake Engineering, 19(4), 289–301. doi:10.1016/S0267-7261(00)00007-5 Kanai, K., & Tanaka, T. (1961). On Microtremors VIII. Bulletin of Earthquake Research Institute, 39, 97–114. Katz, L. J. (1976). Microtremor analysis of local geological conditions. Bulletin of the Seismological Society of America, 66(1), 45–60. Kobayashi, K. (1980). A method for presuming deep ground soil structures by means of longer period microtremors. Proceedings of the 7th World Conference on Earthquake Engineering, 237-240. Komazawa, M., Morikawa, H., Nakamura, K., Akamatsu, J., Nishimura, K., Sawada, S., & Onalp, A. et al. (2002). Bedrock structure in Adapazari, Turkey—a possible cause of severe damage by the 1999 Kociaeli earthquake. Soil Dynamics and Earthquake Engineering, 22(9-12), 829–836. doi:10.1016/ S0267-7261(02)00105-7 Kramer, S. L. (1996). Geotechnical Earthquake Engineering. Prentice Hall. Lachet, C., & Bard, P. Y. (1994). Numerical and Theoretical Investigations on the possibilities and limitations of Nakamuras technique. Journal of Physics of the Earth, 42(5), 377–397. doi:10.4294/ jpe1952.42.377

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Lermo, J., & Chávez-García, F. J. (1994). Are Microtremors Useful in Site Response Evaluation? Bulletin of the Seismological Society of America, 84(5), 1350–1364. Mundepi, A. K., Galiana-Merino, J. J., Kamal, , & Lindholm, C. (2010). Soil characteristics and site effect assessment in the city of Delhi (India) using H/V and f–k methods. Soil Dynamics and Earthquake Engineering, 30(7), 591–599. doi:10.1016/j.soildyn.2010.01.016 Mucciarelli, M. (1998). Reliability and applicability of Nakamuras technique using microtremors: An experimental approach. Journal of Earthquake Engineering, 2(4), 625–638. doi:10.1080/13632469809350337 Nakamura, Y. (1989). A method for dynamic characteristic estimation of subsurface using microtremors on the ground surface. Quarterly Report of Railway Technical Research Institute., 30, 25–33. Nakamura, Y. (2000). Clear identification of fundamental idea of Nakamura’s technique and its applications. Proceedings of the 12th World Conference on Earthquake Engineering. Nakamura, Y. (2008). On the H/V spectrum. Proceedings of the 14th World Conference on Earthquake Engineering. Nogoshi, M., & Igarashi, T. (1971). On the amplitude characteristics of microtremor (Part 2). [in Japanese with English abstract]. Journal of the Seismological Society of Japan, 24, 26–40. Omori, F. (1908). On Micro-tremors. Bulletin of the Imperial Earthquake Investigation Committee, 2, 1–6. Ordaz, M., & Montoya, C. (1999). Degtra 2000. Software. II-UNAM – CENAPRED. Özdağ, O. C., Gönenç, T., & Akgün, M. (2015). Dynamic amplification factor concept of soil layers: A case study in İzmir (Western Anatolia). Arabian Journal of Geosciences (Prague), 8(11), 10093–10104. Phillips, W. S., & Aki, K. (1986). Site amplification of coda waves from local earthquakes in Central California. Bulletin of the Seismological Society of America, 76, 627–648. Podestá, L., & Sáez, E. (2017). Geophysical study and 3D modeling of site effects in the basin of Marga Marga, Viña del Mar, Chile. Proceedings of the 16th World Conference on Earthquake Engineering. Poraloi, S., Richwalski, S. M., Milkereit, C., & Bormann, P. (2004). Assessment of the stability of H/V spectral ratios from ambient noise and comparison with earthquake data in the Cologne area (Germany). Tectonophysics, 390(1-4), 57–73. doi:10.1016/j.tecto.2004.03.024 Pitilakis, K. (2004). Site effects. In A. Ansal (Ed.), Recent advances in earthquake geotechnical engineering and microzonation (pp. 139–197). Dordrecht: Kluwer. doi:10.1007/1-4020-2528-9_6 Pitilakis, K., Riga, E., & Anastasiadis, A. (2013). New code site classification, amplification factors and normalized response spectra based on a worldwide ground-motion database. Bulletin of Earthquake Engineering, 11(4), 925–966. doi:10.1007/s10518-013-9429-4 Ritta, R., Suárez, L., & Pando, M. (2012). Determinación del periodo fundamental del suelo usando vibración ambiental y el cociente espectral Horizontal/Vertical. [in spanish]. Asociación Argentina de Mecánica Computacional., 31, 1399–1419.

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Seekins, L. C., Wennerberg, L., Margheriti, L., & Liu, H. (1996). Site Amplification at Five Locations in San Francisco, California: A Comparison of S Waves, Codas, and Microtremors. Bulletin of the Seismological Society of America, 86(3), 627–635. SESAME European Research Project. (2005). SESAME European Research Project Guidelines for the Implementation of the H/V Spectral Ratio Technique on Ambient Vibrations Measurements. Processing and Interpretation Open File. Shiono, K., Ohta, Y., & Kudo, K. (1979). Observation of 1 to 5 sec microtremors and their applications to earthquake engineering, Part VI: existence of Rayleigh wave components. Journal of Seismology Society of Japan, 32, 115-124 (in Japanese with English abstract) Udwadia, F. E., & Trifunac, M. D. (1973). Comparison of earthquake and microtremor ground motions in E1 Centro, California. Bulletin of the Seismological Society of America, 63, 1227–1253. United States Geological Survey. (2016a). Earthquake glossary. Retrieved from http://earthquake.usgs. gov/learn/glossary/?term=microzonation United States Geological Survey. (2016b). Earthquake glossary. Retrieved from http://earthquake.usgs. gov/learn/glossary/?term=liquefaction Argentine Mining Geological Service. (2017). Maps of faults in Mendoza city Argentina. Retrieved from http://sig.segemar.gov.ar/

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General Trends and New Perspectives on Landslide Mapping and Assessment Methods Murat Ercanoglu Hacettepe University, Turkey Harun Sonmez Hacettepe University, Turkey

ABSTRACT Landslides and their consequences are of great importance throughout the world and they constitute an important responsibility on the damages and fatalities among the natural or man-made hazards. Landslide mapping and assessment studies have become a very important issue for the geoscientists and the decision makers to prevent from the consequences of the landslides, particularly in the last decades. In addition to the increase in population and poor economic conditions, unconsciously built settlements, located in the landslide-prone areas, were the most influencing factors on these losses and damages sourced from the landslides. This section particularly focuses on the landslide mapping and assessment methods considering the chronological development of these methods. In addition, this section also summarizes the landslide inventory, susceptibility, hazard and risk concepts, considering the scientific landslide literature. Furthermore, past-actual trends and new perspectives on these issues were also compiled to show the readers how this subject emerged and evolved progressively.

1. INTRODUCTION Natural hazards such as earthquakes, landslides, tsunamis, floods have been occurring on the Earth since the beginning of the planet. Actually, these events have been involved in the auto-dynamics of the Earth. But, if the human beings and their living environments are included in these natural events, they are transformed into the natural hazards. It means that if a natural event affects the people and DOI: 10.4018/978-1-5225-2709-1.ch010

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 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

the inhabitants around them, it is called a natural hazard. These natural events, to some extent, natural hazards, not only affects the people but also harms their living environment including plants, animals, buildings, transportation lines etc. In other words, although these events are natural and they occur in nature, their deleterious consequences affect all living creatures and their environment. If so, who is guilty? Nature? Faults, soils and rocks as the geological materials? Of course, they are not. Human beings are mainly responsible for these consequences since they unconsciously build their residences, roads, living environments in the hazardous regions on the Earth, and cause irretrievable damages to their environment for their own needs. Of these natural events, the landslides play an important role on these losses and damages and they affect many people and the environment throughout the world. For example, when EM-DAT (Emergency Events Database) launched by CRED (The International Disaster Database, the Centre for Research on the Epidemiology of Disasters, CRED) database is examined, it is clear that the natural hazards affect many people and cause dramatic consequences all over the world. Based on a recent research from the internet link (http://emdat.be) performed for this chapter selecting the years between 1900-2015, disaster types as hydrological, meteorological and geophysical, number of total affected people sums up to 4954745590 with total deaths of 11260586, injured of 7387664 and 26033499164 (‘000 $) of total damage. In this research, disaster type includes earthquake, extreme temperature, flood, landslide, mass movement (dry), storm and volcanic activity. Some research results related to all types in concern with respect to number of disasters and total economic damage are shown in Figure 1 and Figure 2. These figures practically summarize the situation at which the number of disasters and the total damage tend to increase proportionally as the time passes. Of these events, landslides constitute an important effect on the results throughout the world. During the period between 1900 and 2015, 660 landslides affected 13768377 people, caused 62296 deaths and 8875998 (‘000 $) economic damage. 91 countries from different continents were exposed to landslide events. The same conclusions can be done for the landslides, and the analyses results can be seen in Table 1, using the same database (EM-DAT) and selecting the disaster type as landslides. Table 1 should be considered and interpreted according to only the recorded events, i. e. landslides, related to the considered database. Of course, there might be some deficiencies and lacking data in the considered database, but, it gives a general idea about the situation and summarizes the cause-effect (landslides-consequences) relations overall the world. For example, China is the most affected country Figure 1. Number of disasters versus years (1900-2015)

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 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

Figure 2. Total economic damage versus years (1900-2015)

Table 1. Effects of landslides during the period between 1900 and 2015 based on EM-DAT database. Country Name

Landslide Occurrence

Total Deaths

Affected People

Injured People

Homeless

Total Affected

Total Damage (‘000 $)

Afghanistan

19

1509

327896

251

8170

336317

3000

Albania

1

57

---

26

---

26

---

Algeria

1

15

---

46

650

696

---

Angola

1

13

---

---

---

---

---

Argentina

4

103

32350

14

32364

---

15000

Australia

2

28

100

1

---

101

---

Austria

8

358

10380

---

---

10380

41570

Azerbaijan

1

11

---

---

---

---

---

Azores Islands

1

29

---

5

50

55

16300

Bangladesh

4

103

56130

153

---

56283

---

Bolivia (Plurinational State of)

8

311

176290

163

5600

182053

600000

Bosnia and Herzegovina

1

6

400

3

---

403

---

Brazil

24

1730

4091000

214

147100

4238314

231027

Bulgaria

1

11

---

---

---

---

---

Burundi

1

11

---

---

2870

2870

---

Cameroon

1

20

---

---

100

100

---

Chile

4

229

65000

170

17671

82841

6000

China

64

5445

2214046

1711

26689

2242446

1850400

Colombia

40

3079

20270

3577

6363

30210

400

Congo (the Democratic Republic of the)

5

223

748

7

1328

2083

---

Congo (the)

1

154

500

---

168

668

---

Costa Rica

1

7

200

---

---

200

---

Côte d’Ivoire

1

27

---

6

10000

10006

---

continued on following page 352

 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

Table 1. Continued Country Name

Landslide Occurrence

Total Deaths

Affected People

Injured People

Homeless

Total Affected

Total Damage (‘000 $)

Czechoslovakia

2

24

---

---

---

---

---

Ecuador

15

1080

81306

120

180

81606

500000

El Salvador

2

44

---

---

---

---

---

Ethiopia

2

26

---

10

184

194

---

France

10

233

200

26

60

286

10790

French Guiana

1

10

---

5

---

5

---

French Polynesia

3

23

500

11

---

511

---

Germany

1

5

---

---

---

---

6230

Guatemala

9

657

3806

139

51100

55045

505000

Guinea

1

7

480

12

---

492

---

Haiti

2

262

---

60

1000

1060

---

Honduras

1

2800

---

---

---

---

---

Hong Kong

7

212

1360

127

2800

4287

790

Iceland

4

50

63

20

---

83

5789

India

44

4919

231300

531

3616485

3848316

54500

Indonesia

53

2423

356696

540

40015

397251

120745

Iran

4

116

100

44

---

144

---

Israel

1

20

13

---

---

13

---

Italy

15

2588

13713

199

5685

19597

1359210

Jamaica

1

53

---

---

---

---

---

Japan

22

1084

25530

243

---

25773

248000

Kazakhstan

1

48

---

---

---

---

---

Kenya

4

56

---

26

---

26

---

Korea (the Republic of)

9

405

5675

39

2000

7914

248700

Kyrgyzstan

8

249

53986

20

14155

68161

37500

Malaysia

4

96

---

41

250

291

---

Mexico

12

332

200

---

120

320

---

Morocco

1

1

10000

---

2216

12216

---

Mozambique

1

87

---

---

2500

2500

---

Myanmar

6

163

146351

16

1200

147567

---

Nepal

24

1883

362770

160

80200

443130

15000

New Zealand

1

---

600

---

---

600

2466

Nicaragua

1

29

5751

18

---

5769

---

Nigeria

3

32

---

---

1800

1800

---

Norway

1

73

---

---

---

---

---

Pakistan

21

780

30535

206

3300

34041

18000

continued on following page 353

 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

Table 1. Continued Country Name

Papua New Guinea

Landslide Occurrence

Total Deaths

Affected People

Injured People

Homeless

Total Affected

Total Damage (‘000 $)

11

494

1063

40

15000

16103

---

Peru

32

8534

780740

162

9776

790678

1013500

Philippines (the)

30

2441

294072

462

23012

317546

33281

Puerto Rico

2

126

---

---

---

---

---

Réunion

1

10

---

---

---

---

---

Romania

1

---

---

---

330

330

---

Russian Federation (the)

7

414

800

8

---

808

---

Rwanda

3

45

2000

17

5920

7937

---

Saint Lucia

1

---

175

---

---

175

---

Sierra Leone

1

16

---

5

---

5

---

South Africa

1

34

---

---

---

---

---

Soviet Union

6

12282

---

---

2500

2500

423000

Spain

1

84

---

129

---

129

20000

Sri Lanka

6

360

1597

---

330

1927

---

Sweden

1

13

---

50

---

50

11000

Switzerland

10

295

3851

---

---

3851

1215000

Syrian Arab Republic

1

80

---

23

---

23

---

Taiwan (Province of China)

1

14

106

28

---

134

---

Tajikistan

12

365

36464

23

60897

97384

214700

Tanzania, United Republic of

1

13

---

---

150

150

---

Thailand

3

47

10100

10

33000

43110

---

Trinidad and Tobago

1

2

1200

---

---

1200

---

Turkey

12

439

10911

191

2385

13487

26000

Uganda

4

437

16141

84

3368

19593

---

United Kingdom of Great Britain and Northern Ireland

1

140

---

---

---

---

---

United States of America (the)

5

658

125

15

150

290

20000

Uzbekistan

1

24

---

---

---

---

---

Vanuatu

1

1

---

---

3000

3000

---

Venezuela (Bolivarian Republic of)

4

164

19000

318

2200

21518

800

Viet Nam

6

330

---

74

39000

39074

2300

Yemen

2

76

---

11

---

11

---

Zambia

1

9

---

---

150

150

---

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 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

due to landslides (with respect to number of occurrences and total economic damage), while people in Brazil most suffered from the landslides (based on total affected people data). In the economical point of view, these events may even affect the economies of the countries. For example, Hutchinson (1992) stated that natural catastrophes account for 1–2% of the gross national product (Hutchinson, 1992). In many cases, these effects contribute to economic stagnation and lack of development (Aleotti and Chowdhury, 1999). In addition, Varnes (1984) pointed out that individual slope failures are generally not so spectacular or so costly as earthquakes, floods, hurricanes, or some other natural catastrophes. Yet they are more widespread, and over the years may cause more property loss than any other geological hazard. Moreover, much of the damage and sometimes a considerable proportion of the loss of life occurring with earthquakes and intense storms is due to landslides (Varnes, 1984). In addition to direct effects, landslides also affect forests, agricultural lands, rivers and may cause some social problems. Many times, these indirect effects cannot be considered in the damage calculations. Therefore, the total cost for a region and/or country may be more than that is estimated. As can be seen from these examples, landslides and their direct and/or indirect effects have a significant importance for many countries all over the world. The most promising issue is that there have been perpetually increasing interest on this subject among the researchers, governments, national agencies, and scientists all over the world concerning landslides. Aleotti and Chowdhury (1999) stated that the reasons for the increasing international interest in landslides are twofold: firstly an increasing awareness of the socio-economic significance of landslides and secondly, the increased pressure of development and urbanization on the environment. As development increases on sloping urban areas, a higher incidence of slope instability and landsliding is also reported. In addition, landslide database constructions and recording these events may also cause the increase number of landslides as well as the interest. Despite considerable improvements in our understanding of instability mechanisms and the availability of a wide range of mitigation techniques, landslides still cause a significant death toll and significant economic losses all over the world (Corominas et al., 2014). Given the above-mentioned situations, in this chapter, it was aimed at discussing landslide mapping and assessment methods considering the chronological development of these methods. In addition, this section also summarizes the landslide inventory, susceptibility, hazard and risk concepts, considering the scientific landslide literature, and gives new perspectives and trends about the landslide mapping and assessment techniques.

2. BACKGROUND Although the term landslide was defined in different forms by different researchers, its generally accepted definition is that the movement of a mass of rock, debris or earth down a slope (Cruden, 1991) among the landslide researchers. In addition, Working Party on the World Landslide Inventory (WP/ WLI) (1993) published excellent guides to standardize the terminologies related to landslide. Since the detailed terminological discussions are out of this chapter, the details can be found in these references in addition to WP/WLI (1990, 1991, 1993), Cruden and Varnes (1996), Guzzetti et al. (1999), Fell et al. (2008 a and b), Corominas et al. (2014). The materials subjected to a landslide may move by falling, toppling, sliding, spreading, flowing or a combination of these based on Varnes (1978) landslide classification. Landslide occurrence depends upon different parameters such as geological and/or geomorphological processes, changes in vegeta355

 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

tion cover and land use, and can be triggered by heavy precipitation, earthquakes and human activity (Alkevli and Ercanoglu, 2011). Any landslide hazard or risk assessment begins with the collection of information on where landslides are located, and this is the goal of any landslide mapping (Guzzetti, 2004). The simplest form of landslide mapping is landslide inventory, which records the location, and where known, the date of occurrence and types of landslides that have left discernable traces in an area (Hansen, 1984; Wieczorek, 1984; after Guzzetti, 2004). A detailed landslide inventory mapping is one of the most important issues in order to minimize the possible damages and losses caused by the landslides. In addition, a landslide inventory map also a key element for the spatial and temporal analysis of landslides as well as the evolution process of the landforms (Soeters and Van Westen, 1996; Galli et al., 2008; Booth et al., 2009). There are many studies in the literature related to landslide assessments and mapping. However, it is not possible to mention about a conventionally accepted standards when preparing landslide inventory, susceptibility, hazard and risk maps. Furthermore, there are many guidelines and published articles related to the landslide assessments and mapping techniques, but, they properly represent the idealized conditions such as very rich landslide data, correct and available trigger data etc. for these assessment stages. Unfortunately, available and reliable landslide data, in most part of the world, do not exist. Many works have been performed, particularly in the last decades, in order to minimize the damages sourced from the landslides throughout the world. In near future, the quality and relability of the landslide data will be increased because of these efforts with the aid of utilization of technological developments. Recently, it could be concluded that it is indispensable to use of GIS (Geographical Information Systems) and RS (Remote Sensing) techniques when performing any landslide study mentioned above. Thus, in line with the technological developments, landslide related studies will continue to increase. In the next sections, the readers will find some important issues related to landslide assessments based on the past and recent literature. Also, it is possible to follow the historical developments and recent trends in landslide inventory, susceptibility, hazard and risk mapping studies.

3. LANDSLIDE INVENTORY With respect to the practical landslide applications, landslide inventory maps (and related maps will be produced, based on the landslide inventory maps) may provide very useful information for the decisionmakers, planners, governmental services and local administrations for their future plans, regional developments etc. For this reason, many researchers and institutions produce landslide inventory maps with different scales, ranging from national to a detailed specific one, all over the world. However, almost 25% of these maps can be used in practice (Aleotti and Chowdhury, 1999). Of course, this statement may be valid at that time, but, today, the utilization ratio should be more than that of the 20 year’s before. Although the increase is indispensable, it is expected that the utilization of landslide inventory maps in practice seems to be a problematic issue throughout the world, particularly for in the developing or under developed countries. Landslide inventory maps are prepared for different purposes, including: (i) showing the location and type of landslides in a region, (ii) showing the effects of single landslide triggering events, such as an earthquake, an intense rainfall event or a rapid snowmelt event, (iii) showing the abundance of mass movements, (iv) determining the frequency-area statistics of slope failures, (v) providing relevant information to construct landslide susceptibility, hazard or risk models (Galli et al., 2008). All these compo356

 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

nents provide valuable information for engineers, decision makers, planners, researchers and scientists. Thus, this stage (i. e. preparation of landslide database and landslide inventory maps) is one of the most important stages of any landslide study since it provides very important information on past landslide, mechanisms, related parameters on landslide occurrences, geometry and size of the landslides etc. As a matter of fact, there is no standardized process of producing landslide inventory maps although the WP/WLI and the experts published many papers, reports and documentations. Perhaps, the main reason behind this situation may be sourced from the complex mechanisms of landslide types. In addition, the occurrence conditions of landslides may be different and the causatives and triggers may change even in the same region. In other words, one parameter (let’s say slope aspect) may be important for any region, which may not be important for any other region. Thus, the researchers may not consider this parameter in their landslide database, which is essential to the landslide inventory map. Particularly, the last 25-30 years have been witnessed the increase in landslide related studies (Aleotti and Chowdhury, 1999; Guzzetti et al., 2000; Van Westen et al., 2008). When it is thought that the landslide inventory maps and databases are the essential element to every landslide assessment, their accuracy and reliability may become more important. When the scientific landslide literature is examined, it is clear that the number of direct landslide inventory mapping and database construction studies is less than that of the landslide susceptibility, hazard or risk related studies. The main reason could be for this situation is that these studies even include landslide database and inventory stages. There are many methods and approaches to prepare landslide inventory maps as follows based on the landslide literature (Soeters and Van Westen, 1996; Guzzetti et al., 2000; Metternicht et al., 2005; Lee and Lee, 2006; Nichol et al., 2006; Weirich and Blesius, 2007; Galli et al., 2008; Van Westen et al., 2008; Booth et al., 2009; Marcelino et al., 2009) as follows: 1. 2. 3. 4. 5. 6.

Topographical maps and Digital Elevation Model (DEM) analyses Air photo interpretations Field work and geomorphologic analyses Printed or digital map archives or reports Light Detection and Ranging (LIDAR) applications Utilization of satellite images (high or medium resolution)

The first four methods can be considered as the “classical” or “traditional” ones, while the others can be named “new” or “actual” methods (Nichol et al., 2006). Recently, the utilization of unmanned aircraft systems has been anticipated to increase in different applications related to landslide assessments. For example, preparation of high resolution under meter DEMs and images taken from the airborne sensing by unmanned aircraft vehicles (UAV) may become more important in near future. They provide valuable information with respect to the actual conditions on the landscape both for elevation and visible features. They may also be useful for any natural hazard assessment before and after conditions occurred by the considered event. The above mentioned methods have pros and cons each other. Some researchers support the utilization of the traditional methods (e. g.: Mantovani et al., 1996; Hervas et al., 2003), while the others (e. g.: Malamud et al., 2004 a and b; Roering et al., 2005; Farina et al., 2006; Nichol et al., 2006; Booth et al., 2009) defend to use of high or medium resolution satellite images. In addition, Guzzetti et al. (2000) state that at least two or three methods should be used when preparing landslide inventory maps. Furthermore, Lee and Lee (2006) emphasize that the most appropriate and reliable method to prepare 357

 General Trends and New Perspectives on Landslide Mapping and Assessment Methods

the landslide inventory map is the field work. They also point out that the utilization of remote sensing imagery provides significant information and data related to landslide features and characteristics in the mountainous regions. All these assessments yield a result that the classical methods such as air photo interpretation and field works still constitute the essential part of the landslide inventory mapping and database construction. For example, none of the methods can be considered as reliable without a field work and field check. However, developments in the satellite and computer technologies should not be ignored for the inventory studies since their effective utilization provides easy and rapid assessment on the landslide characteristics. LIDAR or UAV may also be very helpful during the mapping and database construction stages. Nonetheless, a topographical map representing the conditions of 60-70 years before (when there is no any remote sensing product except for air photography) or a report, which was worn, but, a readable one, may be very helpful for the analyses and assessments to be done with respect to landslides and their characteristics. Contrary, some researchers such as Malamud et al. (2004a), Roering et al. (2005), Weirich and Blesius (2007) emphasize that the landslide inventory maps prepared by the traditional methods have a subjective aspect and do not completely represent the area studied. For example, utilization of topographical maps is not sufficient to determine small size landslides. In addition, air photo interpretations, one of the most widely used traditional methods, are considered as a time consuming approach. In order to conduct a landslide inventory study in a 1000 km2, approximately 400 stereoscopic air photo of 1/10000 scale are needed in addition to a reliable and accurate Digital Elevation Model. However, in order to obtain a reliable and accurate Digital Elevation Model, the necessary data is not always available. Even if it was reached, orthorectification process of a photo takes approximately 5 hours (Nichol et al., 2006). In addition, certain weather conditions are necessary to obtain a clear and helpful air photograph on which the scarp should be visible and could be differentiated from its surroundings by its color, tone and contrast. On the other hand, air photo interpretations are the most commonly used remote sensing (RS) methodology even at the end of the last century (Metternicht et al., 2005). Utilization of the satellite images, one of the other commonly used remote sensing methodologies, is extremely important in landslide inventory studies, and always shows an increasing trend in such studies (Tralli et al., 2005), particularly after the beginning of 1990s. Contrary to this increase, some researchers such as Mantovani et al. (1996), Hervas et al. (2003), Metternicht et al. (2005) state that the satellite images with medium resolution such as LANDSAT, SPOT and ASTER have not capability of capturing small size landslides. However, some researchers such as Martha et al. (2010) and Aksoy and Ercanoglu (2012) successfully used medium resolution satellite images to determine the landslide locations using Object Based Image Analysis (OBIA). For the large size landslides, optical LANDSAT, SPOT, ASTER, IRS 1-D satellite images and RADARSAT, ERS 1-2, JERS and ENVISAT radar images have been successfully used both in determining the landslide locations and visual interpretations (Singhroy, 2005). Van Westen et al. (2008), in a different point of view, considered the high resolution satellite images such as IKONOS, QuickBird, CARTOSAT 1-2 the most appropriate remote sensing products in landslide inventory studies. Similarly, Nichol et al. (2006) also stated that the Digital Elevation Models created from IKONOS stereo images appear to be much more accurate and sensitive to micro-scale terrain features than a DEM created from digital contour data with a 2 m contour interval. The authors emphasize that pan-sharpened stereo IKONOS images permit interpretation of recent landslides as small as 2–3 m in width as well as relict landslides older than 50 years. As can be seen from all these examples, satellite images, particularly the ones with high resolution, suggest more effective results with respect to landslide inventory studies. 358

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The scale of the landslide inventory is another important issue since it is completely related to the scope of the work and what will be presented in the area studied. The scale should be selected taking into account the objectives of the map. In practical terms, however, the scale of mapping may be controlled by the scale of the available topographic maps (Fell et al., 2008a). The scale of work constrains the type of approach to be followed to achieve the purposes of the zoning purposes. For instance, maps at national (45° and the depth is greater than Hcr infinite slope failure is not reasonable. So he concluded that this type of a slope is more often seen on coarse grained soils than fine grained soils. LEM may also base on the moment equilibrium. In the calculation steps for a trial point of rotation O above the slope, the actual sliding surface is replaced by several arc segments. If the shape of failure plane is a circular arc, Fs will be defined as the ratio of the moments with respect to center point of the circle caused by the resisting forces to the disturbing forces. When the slope in fully saturated normally consolidated clay is subjected to loading or rapid dropdown of water level, for a given circular sliding surface the critical short term stability check with total stress analysis requires Fs as Fs =

cu × La × r W ×d



(10)

where cu is the cohesion equals to undrained shear strength of the clay, La is arc length of the circular arc shaped sliding surface, r is radius of the arc, W is total weight of the soil mass above the sliding surface, d is the lateral distance between the center of arc at point O and the gravity center of the sliding soil mass as shown in Figure 4. If the soil profile is complicated, assuming the sliding mass composed of vertical slices with a predetermined width will be useful to solve the slope stability problem of soil statically. The width of the slices may be optimized with respect to non-uniformity in the soil conditions along the sliding surface and its length. Even though a better fit is gained between the curvature of the sliding surface and the slice base when the number of the slices increase, the enormous calculation steps and iterations require computerized solutions. However, if the soil profile is limited, it is useless to increase the number more than 100

394

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Figure 4. The ϕu= 0 analysis method

that has negligible effect on the solutions. Figure 5 presents an example of a sliding surface where the sliding mass subdivided into twelve vertical slices with the width of b. The body forces acting on a slice is also represented in Figure 5 where W is the weight of the slice T, N’ and U are the tangential force, normal effective force and uplift force due to pore water pressure acting on the slice base with a length of l respectively. In case of a non-circular sliding surface the normal forces N acting on this segment will cause a moment with respect to rotation center that has to be taken into account. E is the normal force and X is the shear force acting on the sides of the slice. If any external forces such as loading on the top of the slope or the water pressure in the tension crack or in front of the slope are subjected to the sliding mass, they have to be also taken into account in the relevant portion of the equations. Figure 5. Slice method and force on a slice

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The factor of safety can be derived by the limit of equilibrium equations of forces in two directions and moments. However, the problem is statically indeterminate until the distribution of normal or interslice forces are known. Several researchers suggested different assumptions while determining N value with or without interslice forces based on some or all of the equilibrium equations with respect to moments, vertical forces and horizontal forces. The traditional limit equilibrium methods available in slope stability programs are briefly explained in the following section. Fellenius (1927) proposed Ordinary method of slices based on the satisfaction of moment equilibrium equation for circular sliding surface, when the resultant of the interslice forces is assumed to be zero. This assumption causes an underestimation of factor of safety within the error range of 5-20%. (Craig, 2004). Fs will be Fs =

M resisting M disturbing

=

∑ c ′ × l × r + ∑ (W × cos α − u × l )× tan φ ′ × r ∑W × sin α × r

(11)

Bishop (1955) suggested to solve the indeterminate problem by using the equilibrium equation of forces in vertical domain. For a circular sliding surface, considering the moment equilibrium, he assumed Ei and Ei+1, Ui and Ui+1 are collinear and Ni act at the midpoint of the slice base. In terms of effective stresses, Fs will be       sec α 1 i  c ′ ×b + W − u ×b + (X − X ) × tan φ ′ ×  Fs = ∑ i i i +1 i   ′ W × sin α tan α × tan φ ∑ i  i i i  + 1   Fs  

(

(

)

)

(12)

Bishop’s Simplified method is a simplified version of Bishop’s method that also assumes that resultant of the interslice forces is horizontal which means that on the sliding mass for ith slice the shear forces acting on the sides Xi and Xi+1 are equal to each other in the opposite direction which results a limited error around 1% (Bishop, 1955). Thus, considering the vertical equilibrium on the slices, Fs based on moment equilibrium will be       sec α 1 i  c ′ ×b + (W − u ×b ) × tan φ ′ ×  Fs = ∑ i i  ′ W × × sin α tan α tan φ ∑ i  i i i  + 1   Fs  

(

)

(13)

Bishop and Morgenstern proposed the determination of Fs by using pore water ratio ru, defined as the ratio of pore water pressure and total pressure at the base of each slice and dimensionless stability coefficients m and n which are calculated by β, ϕ’, the depth factor D and c’/γH for slopes in homogenous soils as

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      1 sec α   ′ ′ Fs = c × b + W − r × × 1 tan φ ( ) ∑ u  ′ W × × α φ sin α tan tan ∑   + 1   F   s

(

)

(14)

Janbu (1954) investigate the factor of safety against sliding by assuming lateral interslice forces EiEi+1=0 and replacing interface shear forces by a correction factor ƒo for a noncircular sliding surface. If the equilibrium of horizontal forces is valid Fs will be       sec αi  c ′ ×b + W × (1 − r ) + (X − X ) × tan φ ′ ×  cos α ∑ i i i u i i +1 i ′ tan αi × tan φi   1+   Fs   Fs = ∑ Wi + (Xi − Xi +1 ) × tan αi

(

(

)

)

(

)

(15)

Janbu’s corrected method which satisfy all conditions of equilibrium uses a correction factor f0 to consider the geometry of the sliding mass and the shear strength parameters of the soil that it compensate Janbu’s simplified method (Janbu, 1968) which satisfies only force equilibrium, so Fs will be       sec α i  × cos α f0 × ∑  ci′ ×b + Wi × (1 − ru ) × tan φi′ × i  ′ α φ tan × tan  i i  1 +   Fs   Fs = ∑Wi × tan αi

(

(

)

)

(16)

Spencer (1967) assumes that the resultant force of the interslice forces E and X on both sides of the slice are parallel and the inclination angle θ with horizontal is constant for all slices for a circular or non-circular arc sliding surface. The moment and force equilibrium is satisfied by this method. The procedure requires several tries to find a value of θ, which gives the same factor of safety against sliding of a circular surface, with respect to moment and force equilibrium. Modified Swedish method satisfies force equilibrium. In the method it is possible to choose a constant value of θ for inclination angle of the resultant force of interslice shear force X and interslice normal force E for all slices, which is suggested to be the average of the slope of the ground surface (U.S. Army Corps of Engineers. 1970). Lowe and Karafiath’s (1960) Method satisfies force equilibrium and identical to Corps of Engineers Modified Swedish Method except the value of θ and the acting point on the sides. On this procedure the resultant force of interslice forces X and E is assumed to be at the mid height of a side and its inclination angle is average of slope surface and slip surface and changing from side to side of each slice of a sliding mass.

397

 Slope Stability of Soils

Morgenstern and Price (1965) suggested an analysis method that satisfies all boundary and equilibrium conditions for any type of a sliding surface. It resolves the forces parallel and normal to the base of the slice. The ratio of interslice forces X/E is equaled to the product of a specified function related to the angle of α of a slice and a scaling factor λ. In solution process interslice force inclinations are calculated that satisfy the all conditions of equilibrium. It can be same or different from slice to slice. Sarma’s method (Sarma, 1973) following a prescribed pattern for the vertical interslice forces searches a value for pre-factoring strengths which results in zero horizontal acceleration for barely stable equilibrium. It is applicable to all shapes of slip surface and satisfies all conditions of equilibrium. The result can be converted to the value of conventional factor of safety. Fredlund and Krahn (1977) proposed a common formulation that encompasses the Simplified Bishop, Spencer’s, Janbu’s simplified, Janbu’s rigorous, and the Morgenstern-Price methods for computing the FS for a failure surface. It is known as General Limit Equilibrium method (GLE). In slope stability problems the effect of earthquake is taken into account by pseudo-static, Newmark’s displacement and accelerogram selection for Newmark’s method in LEM. However, due to its simplicity pseudo-static is used for practical purposes. In the method, seismic force is produced by multiplication of seismic coefficient k expressed in terms of gravitational acceleration g and potential sliding mass acting on the gravity center of a trial sliding mass (Duncan, 1996; Janbu, 1973; Nash, 1987). So earthquake disturbing force, W .k will be added as a parameter by it vertical and horizontal components W.kh and W.kv. In the analysis typically assuming kv =0 will result an inertial force, W .kh acting horizontally. For practical purposes Corps of Engineers suggested to use kh value as 0.1 for major earthquakes and 0.15 for great earthquakes while Fs=1.0. Hynes-Griffin and Franklin (1984) assumed seismic coefficient is equal to half of the peak ground acceleration when Fs=1.0. In Japan acceptable seismic coefficient is changing between 0.15 and 0.25 when Fs=1.0. Moreover, critical seismic coefficient kc may be used as a threshold value for the limit equilibrium analysis where a value of k>kc represents instability of the slope under seismic conditions. In this respect kc may be useful as an alternative to Fs where kc 1.3 is sufficient for short term stability problems, generally

398

 Slope Stability of Soils

for long-term stability problems Fs >1.5 may be required. In slope stability problems at urbanized or residential areas Fs>2 may be requested. When the shape of a failed slope is known the average shear strength parameters of the soil layers along the sliding surface can be determined by equalizing Fs=1. This method is called as back analysis. Generally, the shear strength found by back analysis and the laboratory tests converge. However, the uncertainty in simulating a complex sliding surface with the result of a test specimen from an infinitesimally small region of the surface may cause misleading that back-analyzed shear strength parameters will be useful in soil slope remediation projects. The complexity in the soil stratigraphy and stresses due to environmental infra and super structures requires more complicated modeling which means more time and effort for engineers to solve the problem statically. There are many software packages specialized for slope stability analysis such as GEO-SLOPE International (2008) & TALREN using limit equilibrium approaches. The stability check of the slope can be performed on several methods with different approaches for a user defined soil profile and shear strength parameters. Besides the input of an average value for the soil parameters in a layer, some of the programs allow to set an increment value for cohesion in order to simulate the variation per depth. While the stress distribution on a trial sliding surface is automatically calculated by the program, nowadays, some of the software programs allow to import the soil stresses or pore pressure meshes calculated in a Finite Element analysis program such as TALREN & PLAXIS, GEOSLOPE & SIGMA/W & SEEP/W. Krahn (2001) illustrated that importing the stresses determined by FEM was improving the accuracy of limit equilibrium solutions. Generally, in a slope stability program the sliding surface may be defined manually or automatically with “grid and radius” or “enter and exit” options. In “grid and radius” mode user is defining the center points for the circular sliding surfaces on a predesignated grid area and tangential boundaries is requested for determining the radius. If the location and the depth of tension cracks or the location of the toe are visible on the site, the “entrance and exit” mode will be useful to define the boundaries of the sliding surface on the ground surface in the model. Then, automatic search option may enable to determine the center point and radius for the most critical sliding surface under given circumstances. Some of the programs also enable to define the tension cracks with depth and inclination for the entrance point. On the contrary user can model the tension crack by placing a trench on the ground surface not to take any shear strength along the tension crack and limit the entrance boundary to this trench. Furthermore, programs allow the user to limit the sliding surface to pass inside or outside of any soil layer. Most of the programs are available to present the critical sliding surfaces in the soil slope within the range of selected boundary values for Fs. The hot spots for center points are also visible on the searching grid as in contours or color coded regions with respect to the calculated minimum values of Fs at an inspection point. Starting from wider grid areas, the user may repeat the analysis with the grids localized on the hot spot areas to find the critical sliding surface properly with the minimum Fs. The digital advances in software programs using limit equilibrium methods allow the reasonable evaluation of remediation projects of slope stability problems for practical purposes. The soil reinforcements systems such as lime or fiber additives used in a man-made slope project may be modelled by increasing the shear strength parameters for the improved soil material. Moreover, the inclusion elements modelled along the sliding surface such as piles, anchors and geotextiles can be checked for the length of fixity below the searched sliding surface in the mode of tension, compression, shear or bending. Moreover, some of the programs such as TALREN enables the modelling of the mobilized forces between the reinforcements and the soil based on the principle of maximum plastic work by defining a combination of different failure criteria.

399

 Slope Stability of Soils

3D stability analyses in limit equilibrium methods are generally based on the equilibrium of columns produced by extension of slices in 2D limit equilibrium methods such as Bishop’s (1955) simplified, Morgenstern and Price’s (1965) or Spencer’s (1967). The factor of safety is determined by using overall stability equations of the sliding mass composed of a number of columns with vertical interfaces (Baligh & Azzouz, 1975; Chang, 2002; Gens, Hutchinson & Cavounidis 1988; Hovland, 1977; Hungr, Salgado & Byrne, 1989; Lam & Fredlund, 1993; Leshchinsky, Baker & Silver, 1985; Ugai, 1985; Ugai & Hosobori, 1988). In these analysis mainly the assumptions made to consider the intercolumn forces are changing for force equilibrium of a column.

9. STRESS DEFORMATION ANALYSIS Limit equilibrium analysis methods based on the overall equilibrium does not satisfy the compatibility. In some cases, the necessity for accurate evaluation of stresses, strains and deformations within natural and man-made slopes lead to develop advanced numerical methods to improve the findings of limit equilibrium methods. Besides the short-term undrained analysis and long-term drained analysis of slopes in saturated clays, more sophisticated analyze methods are available for modelling time-dependent behavior of slopes due to the developments in the hardware and software programing. Finite Element Method (FEM) (Huo & Zhai, 2012; Zhang, 1999, Zheng, Liu & Li 2005), Explicit Finite Difference Method (EFDM), Discrete Element Method (DEM), Boundary Element Method (BEM) (Donald & Chen, 1997), Discontinuous Deformation Analysis (DDA) are one of the best known methods and applicable to soil mechanics in slope stability problems. The difference in the assumptions used in these methods may lead differences within an acceptable range or limit the usability for some of specific cases. Finite Element Method (FEM), is based on the numerical solutions of slope stability problems in continuum mechanics. In the scope of the analysis, soil is composed of triangular, rectangular or quadrilateral finite elements generally involving nodal points on the boundaries. Method approximates the distributed and boundary stresses as forces concentrated on nodes. Unknown nodal point displacements are determined for calculating element strains and element stresses achieving the overall equilibrium. The continuity of displacement between elements may require finer meshing of increasing nodal points on the elements. Soil material parameters for stress analysis requires the determination of modulus of elasticity E, Poisson’s ratio, ν, shear modulus G at in-situ loading and drainage conditions. This stress deformation analyses method developed initially based on the assumption of linear elastic behavior was improved by constitutive models (Carter, Desai, Potts, Schweiger & Sloan 2000; Dounias, Potts & Vaughan, 1996; Duncan, 1992; Duncan & Chang, 1970; Potts, 2003; Potts, Kovacevic & Vaughan. 1990). So more sophisticated stress-strain relations using the assumptions such as linear elastic, nonlinear elastic, hyperbolic elastic, plastic and strain-softening soil behavior are also available in FEM analysis. Duncan (1992) stated a reasonable agreement between the results of FEM and measured deformations. Discrete Element Method (DEM) and Discontinuous Deformation Analysis (DDA) are designed especially for modeling the discontinuity in the soil or rock masses as an alternative to FEM. Slip or separation behavior in the media are modelled with forced based method on DEM by dual nodes defined without special joint elements to allow inelastic displacements along the discontinuity according to prescribed criteria (Ko, 1972; Wang & Voight, 1969). DDA is a displacement based method properly used for several slide problems such as Vaiont slide (Sitar, MacLaughlin & Doolin, 2005). Boundary Element Method (BEM), is based on the continuum mechanics and assumes perfectly rigid plasticity. 400

 Slope Stability of Soils

While the theory of Lower Bound limit analysis satisfy equilibrium without allowing compressibility, Upper Bound limit analysis satisfy compressibility without equilibrium. There are many numerical analysis software packages available for geoengineering based on finite element approach ABAQUS, PLAXIS, finite difference approach FLAC, discrete element approach UDEC and 3-DEC. In slope engineering stress reduction methods are carried out successively to define sliding surface and the generation of deformations up to overall collapse. This elastoplastic analysis is based on decreasing the magnitude of shear strength parameters in small incremental steps. After the critical equilibrium is stated, the solution will no longer converges. Although the closed form solutions based on the elastic or elastoplastic assumptions may be improper for modelling the stress-strain behavior of soil slopes, the justifications by advanced numerical methods are silver lining for constitutive modelling of elastoplastic and elasto-viscoplastic behavior of soil while satisfying equilibrium, compressibility and boundary conditions. The slope stability problems may be modelled in 2-D or 3-D. The difference between them may be negligible when modelling a planar sliding surface. However, if a dish shaped sliding surface is the case, considering the problem in two dimensions by the assumption of plane strain condition, analysis gives conservative results when compared with the three dimensional solution (Craig, 2004). When there are non-uniformly distributed super or infra-structures on the site, three-dimensional modelling may be preferred to analyze the effect of the instability of the slopes on the surrounding structures. Although several 3-D finite element software programs are ready to use, theoretical studies on 3-D slope stability analysis performed on certain shapes of sliding surfaces (Azzouz & Baligh, 1978; Cavounidis 1987) illustrates that 3-D will give a slightly higher factor of safety than 2-D.

10. EMPIRICAL OR STATISTICAL REGIONAL APPROACHES Evaluating the site specific approach findings based on conventional LEM within the context of empirical or statistical regional approaches is always useful. These approaches based on analysis of observational data of geoscientists. There are many publications (Chowdhury et al., 1987; Christian & Baecher 2001; 2004; Christian, 2004) dealing with the reliability related to progressive failure modes and system reliability. First Order Second Moment (FOSM), Point Estimate Method (PEM) and Monte Carlo Simulation Method (MSM) are one of the most commonly used numerical methods for evaluating statistical moments of factor of safety or simulating its probability distribution and they work well for slope stability problems. While FOSM and PEM use statistical moments of Fs to obtain reliability index and probability of failure, MSM simulates probability function of Fs directly. Chowdhury et al. (2010) stated that PEM is better than the other two and requires much less computing effort. Some of the researchers (Cassidy, Uzielli & Lacasse, 2008; Hong & Roh, 2008, Shou & Wang, 2003) performed probability analysis on the case studies to estimate the risk of the slope under high water level in static and seismic loading conditions. If observational data involving the uncertainties regarding the regional and site specific factors i.e. seismicity, their influence on annual probability of failure pf will be different and have to be considered (Silva, Lambe & Marr, 2008). Silva et al. (2008) categorized the slope stability problems with respect to the uncertainties in the scope of the investigation, reliability of the shear strength data and the quality of engineering to determine pf for a given value of Fs from the proposed non-linear functions. Formal analysis is necessary to consider the probability of multiple failure modes, including progressive failures. However, if there is no previous experience available about 401

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a new problem, estimating a value of pf for individual slopes by probabilistic approaches may mislead. However, these efforts are useful to understand the important phenomena related to slope stability. There are also other numerical approaches such as neural networks and multiple regressions approaches which are also popular for the prediction of the critical factor of safety of homogeneous finite slopes (S.K. Das, Biswal, Sivakugan & B. Das, 2011; Erzin & Cetin, 2013). After Taylor (1937), in the literature there are many available slope stability charts proposed by researchers based on limit equilibrium analyses or finite element analyses or lower and upper bound analyses in 2D (Baker, Shukha, Operstein & Frydman 2006; Gens et al. 1988; Jiang & Yamagami 2006; Kostic, Vasovic & Jevremovic 2016; Kostic, Vasovic & Sunaric 2015; Loukidis, Bandini & Salgado 2003; Yu et al. 1998; Kim, Salgado & Yu 1999; Zhu, 2001) or 3D (Li, Merifield & Lyamin 2009, 2010) for rapid analysis of slope stability and evaluation of design alternatives. The approximations in slope geometry (height of the slope, inclination angle, depth of bedrock etc.), and selected soil properties (γ, c, ϕ, homogeneity), ground water conditions (submergence, seepage), existence of tension crack, type of the sliding surface (infinite, circular etc.) are some of the parameters that may limit the usability of such charts. There are several publications that examine slope stability problems at different initial conditions by using charts proposed by several researchers (Duncan, Buchignani & De Wet, 1987; Hunter & Schuster 1968; Janbu, 1968) for soils with ϕ=0, ϕ >0, ϕ =0 and shear strength increasing with depth (Duncan, Wright & Brandon 2014).

11. REMEDIATION TECHNIQUES IN SLOPE STABILITY Landslide remediation is aiming to restore the stability of a failed slope permanently against individual or expected future conditions that may cause instability. Landslides occur if the shear strength of the failure plane is not sufficient to support the shear forces acting on the shear surface where the factor of safety Fs ≤ 1. Increasing the shear resisting forces and/or reducing the destabilizing forces may increase Fs and regain the stability. Although the selection of remediation techniques is related to the amount of the budget and the site conditions, from geotechnical engineer’s point of view, several factors play important role on selecting the factor of safety in the analysis generally within the limits of 1.15 to 1.50. If minimal level of study is performed or landslide is small or type of landslide movement is very fast or in case of future instability involves very significant potential risks of life and property loses or experience of geotechnical consultant or contractor is limited, factor of safety should be relatively higher than the reverse cases. In some cases, such as high cost, ownership boundaries, environmental difficulties, large size landslides do not allow the full remediation of the slope. When the movement is continuing at a constant or decreasing rate, leaving the slope to be stabilized in time may be a solution. Maintenance option may be enough for roads located on such a cut slopes, unless the movements regress and cause danger to upslope structures or facilities or infrastructures. Sometimes lower values of factor of safety than desired is adopted for minimizing the stabilization requirement aiming to slow down or stop the movements of a landslide unless the risk to personal safety or the usage of facilities on land. In both selection; no action and maintenance, for a slow moving landslide, the monitoring landslides by inclinometer readings and surface survey points are necessary in differential frequencies ranging between the weeks and years based on the rate of the movement. However, when an accelerating rate of movement is observed in stiff clays, this attitude will cause disastrous delayed failure. Shifting the route of a road affected landslides 402

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to a stable area, relocating the facilities, bridging over the landslide or tunneling through the stable side will be an economical way of avoiding the landslide. Another alternative to full remediation of a landslide is the selective stabilization of a specific portion of the landslide where a facility is located and down slope is left untreated. In such a case, the stability of upper portion has to be checked in case of instability of untreated portion. In the scope of remediation of a landslide, earthworks may increase the resisting forces at the down slope by regrading or buttresses or shear key installation. Regrading of a slope aims to shift weight from the upper wedge to the lower wedge of the slope. It may result in decreasing the inclination angle of the slope or making platforms. If the infinite slope analysis of a cohesionless soil is performed it is seen that keeping the groundwater level constant, removing a weight from middle or lower wedge of a landslide will reduce Fs. Circular arc or translational type landslides are effectively remediated by buttresses. External buttress consist of weight of a hard, angular, free draining rock fill, is placed below the landslide on a stable ground for foundation. A filter fabric coating or filter layer is laid on the surface of the land before the rock fill to prevent occurring cavities in the soil due to the seepage forces. If the rock fill on the shore of sea or river the riprap protection is applied to the slopes of the rockfill against flood risk. When erosional failure occurs, in case of emergency ground loss was replaced with rockfill or well graded sandy gravel without a fabric or filter layer. However, it may cause the fine grained soil pass into the rock fill site in long-term. It is better to construct the infill buttress in a dry season. In any case before the construction soft saturated landslides must be excavated and removed completely or cement and/or lime treated native soil can be left in a limited volume. If there is no place for external buttress, replacement buttress can be preferred. The construction made in two vertical cuts first stable above the groundwater, more critical second excavation phase till bottom of the landslide requires a closely sequenced construction procedure to minimize the landslide movements. While removing the old debris for a limited section which may be defined by the applicability width of the filter fabric drainage liner coating at the toe of the landslide, constructing the free drained rock fill right after the filter fabric replacement is proceed from one side to another. During the constructions vibratory cylinders are not preferred which may initiate sliding. Although the main purpose of the buttresses is reestablishing the stability by weight on the resisting side, the path of the sliding surface in rock fill will be significantly higher value when compared with the residual shear strength of clayey soils. Shear keys are also corresponding this purpose where the excavated trenches deeper than the depth of sliding surface backfilled by rockfill strata to increase the shear stress on the sliding surface. It also benefits the drainage to relieve porewater pressure on the sliding surface. Considering the relative construction costs, the mentioned earthworks are slope regrading, infill buttress, external buttress, shear key in ascending order. Erosion is one of the major factors that provoke landslides by undermining and steepening the slopes. It is possible to prevent a failure of an eroded slopes. Filter systems composed of single to graded filter blankets with or without geotextiles are used to prevent transportation of fine grained soils placed on the surface of the slope and in buttress at the interface of rockfill and natural soil. Riprap is a surficial erosion protection system consist of heavy angular rock layer placed on the surface of the slope under the effect of waves and river currents to resist displacement and uplift forces by its own weight. If the currents are not so strong, gabion mattresses may be a solution for shore protection. In a predesigned geometry with respect to the bank soil type and the maximum velocity of the currents, rectangular wire mesh baskets are placed at the toe and on the surface of the slope. After filling free drainage small rock material in the basket the lid is wired. The limited flexibility, corrosion susceptibility, high construction and repair costs of gabion mattresses are disadvantages. Shotcrete is a way of protection applicable to stiff 403

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and dense soils to weak rock surfaces against weathering and rainfall infiltration type erosion of slopes. the slope surface is coated by spraying a mixture of sand, fine, gravel, cement, water, accelerators and air entrainment for a thickness of wall construction. The placement of steel mesh or the additive of short fiber reinforcements made of steel, polypropylene or synthetics may increase the shear strength of the coating. It is not suitable when there is free water running over the surface and it can work well at minor seepage conditions due to the weepholes left inside the wall. Bioremediation is an alternative protection way for surface erosion from run off and flowing streams and shallow landslides. The living plants will resist to erosion or sliding by the tensile strength of their roots. The long time requirement for planting is tried to recover by adding erosion meshes and nets to the protection system made of geosynthetic or degradeable organic fibers. Interlocking angular, unconnected hand placed and articulated mattresses are all concrete block systems useful for surficial erosion. While the construction cost of filter systems is relatively lower than the other methods for prevention of surficial erosion in slopes, the costs will rise in the order of shotcrete, bioremediation, gabion mattress, riprap slope armor and concrete blocks. Decreasing the pore water pressure acting on the sliding mass will increase the effective stress that increase stability of the slope. There are various types of drain and well systems are commercially available to reduce the ground water level or relief the artesian water pressure or capture the surface runoff water. Dewatering projects may include a mix of these systems. The uncertainty in the ground conditions may results insufficient lowering of water. It is recommended to perform packer and pumping tests during the site investigations. A limited lowering in the elevation of water table especially when the groundwater table is so close to the sliding surface or the consolidation of surrounding soil cause a risk for the safety of adjacent structures, may not effectively increase the stability of landslide. Horizontal drains are one of dewatering systems composed of a number of drain PVC pipes placed through the horizontally drilled boreholes in the slope and connected to a collector system to expel water out of the slope. There are many other techniques summarized shortly in this chapter. Trench drains are the narrow trenches prepared by geotextile filter fabric coated free draining rock fill and a perforated plastic pipe at the trench base to lower the groundwater level generally or to reduce the sudden peaks of the groundwater level during storm or snowmelt. If the aim of the trench is also to capture surface runoff, free draining soil is placed to the top part of the trench, contrary compacted impervious seal is preferred. Trench drains are generally constructed not deeper than 6 m and parallel or transverse to the direction of the slope. Another surface water control system to prevent surficial erosion in sand and silt soils is French drains. They laid in a scheme of chevron and main called narrow trench drains connected together down the cut slope. Drainage blanket is one or two layers of fine to coarse filter materials laid on the slope surface to absorb the seepage forces to prevent erosion while seepage water is coming to the slope surface. Deep wells can provide temporary or permanent stability to landslides by lowering the groundwater table of unconfined or confined aquifers using a pumping system. Depending on the ground conditions the internal distances of the wells are arranged considering the interaction with the adjacent wells. Using the wellpoint systems at several elevations of the excavation, shallow temporary cut slopes in waterbearing sands and gravels can be achieved safely. Up to 40 m depth of economical lift for ejector systems can be preferred for deep drainage of fine grained soils at close spacing (Forester, 2001). In ejector system consist of ejector body where the water is pumped down the hole and return as a mixture of pumped water and groundwater with single or two pipes in the same unit. However, 40% of operating efficiency recorded in wellpoint systems is only around 10% in ejector systems (Powers, 1992). Artesian condition must be checked in slope stability problems. Existence of high porewater pressure under a layer of impermeable soil may cause piping, sand boils and erosion and lower effective stresses 404

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when compared to hydrostatic conditions. Installation of vertical or inclined relief wells on the slope will lower the high water pressures of an artesian. Furthermore, relief wells may be a solution in reducing the high seepage forces at the toe of a dam on the downstream side or sleeves occurred during floods. In perched groundwater conditions dewatering can be achieved by using vertical gravity drains where the perched water is transferred by its own weight to a deeper stratum where available. If the sliding surface is deep-seated, deep drainage may be provided by tunnels, drainage adits and horizontal drains arrayed with the vertical shafts or a combination of them. When the embankment will be constructed on a soft clay, the shear strength of the soil may be increased by proper soil improvement techniques like preloading plus prefabricated vertical drains. Strip drains are one of the best preferred technique due to the time saving, economy and easy installation procedures. It also reduces the potential of liquefaction in saturated fine sand and silt layers that may affect the stability of the slope. Some researches on 400 landslides illustrates that control of the surface water and water carrying pipes may be prevented at 60% (Cornforth, 2005). Consequently, regarding the relative construction costs, dewatering systems used in slope stability problems of soils are drains, drainage blanket, surface water control, prefabricated vertical drains, relief wells, vertical gravity wells, vertical drains, horizontal drains, wellpoint ejectors, trench drains, deep wells, tunnels, adits, vertical shaft and drain array in the ascending order. Instability of the slopes due to the seepage forces may be prevented or reduced by seepage barriers. These may consist of vertical walls like slurry trench cutoff walls to increase the drainage path constructed on the opposite of the slope direction. One of the soil bentonite, cement-bentonite, plastic concrete or diaphragm wall types of the slurry trench cutoff can be selected proper to the site permeability conditions in the order of cheap to expensive. Liners are also used on the slopes of dams levees or pods as preventative for seepage based problems. If the landslide debris has pockets and lenses of water bearing gravels and sands can be improved against seepage by cement or chemical grouting or jet grouting work as grout curtains. Deep mixing is a soil mix wall that can be used as a cutoff wall for reducing seepage or preventing liquefaction induced problems in loose to medium dense silts, sands and gravels. While for speepage protection the construction cost of slope liners is relatively lower than grout curtains, slurry trench cutoff walls will be the highest of them all. Buried or full depth concrete shear piles, at a length of fixity under and above the discrete zone that the sliding surface pass through are placed in a line opposite to the slope direction to increase the strength of the sliding surface. The reinforcement used in the design of a pile is realized based on the moment equilibrium conditions. It is not suitable to construct the pile if the movement of the landslide continues. The shear forces acting on the pile prior to the hardening the concrete will cause cracks. The pile spacing from center to center, S will be calculated based on the passive resistance of a laterally loaded pile with a diameter B. It makes no problem if the S/B>4, contrary interference of passive zones of individual piles will require a reduction factor to take into account the group effect where the bearing capacity of single pile will be reduced 50% for contiguous pile in a wall. Stone column construction method laid in a scheme of an array format for the clay landslides is another way of increasing residual shear strength of the sliding surface. The angular crushed rock with ϕ=40°-43° is filled and compacted in a drilled borehole at a depth of deeper than the depth of sliding surface for socketing the column to the stable strata. Trench drains and jet grout columns and deep soil mixing techniques can also use from the same perspective that they are replacing the weak material in the discrete zone with a stronger material. All these techniques can be improved by future developments but has to be tested before put into practice. For the mentioned soil improvement techniques applicable in landslides, relative construction costs from low to high is excavation and replacement, stone column, deep soil mixing. 405

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Stabilizing the landslides may be achieved by retaining structures. Masonry walls, bin walls, gabion walls, concrete mass walls are some of the gravity wall types constructed at the toe of landslides to resist sliding and overturning. The foundation soil of these walls must have sufficient bearing capacity and located below the weak zone where the sliding surface is in. In this method of bottom to top construction, excavation of some landslide debris may disturbed the stability in wet weather conditions so temporary stability of the cut slope must be checked. Cantilever concrete retaining walls i.e. shear pile walls and diaphragm walls and sheetpiles are vertical or inclined top to down constructed walls that are placed along the landslide till to the stable strata below the sliding surface for a length of fixity to satisfy the limit equilibrium conditions of moments in a factor of safety. After construction of the wall, the landslide debris at the down slope face is excavated. If the deflection criteria are not satisfied the face of the cantilever walls can be restrained by ground anchors. In these tied back walls the bonding length of the anchors must stay in the stable strata below the sliding surface after leaving an appropriate margin. Regarding the relative construction costs, the retaining structures constructed with bottom to top method are gabion walls, reinforced soil slope, concrete crib, bin wall, masonry or concrete gravity wall, while the retaining structures constructed with top to bottom method are tied back pile wall, concrete cantilever wall, tied back sheet pile walls, concrete shear pile wall, tied back slurry trench wall in ascending order. Soil nailing, reticulated micropiles and mechanically stabilized earth walls are earth reinforcement systems where the soil with the inserted reinforcement i.e. steel rods, metal strips, geogrids, geosynthetics behaves as a block in the manner of a gravity retaining structure that they can be used at down slope of a landslide to resist sliding and overturning. In the preliminary design phase, LEM analysis of reinforced soil can be used to check the slope stability against several types of sliding failures. However, FEM is one of the popular analysis methods where the actual behavior of the wall can be determined by defining the material parameters and soil failure criteria. On the contrary, empirical analysis methods are still in use due to its simplicity such as Terzaghi and Peck’s, (1948) method for the strutted excavations. The earth reinforcement systems are reticulated micropiles, soil nailing, mechanically stabilized earth wall which are listed according to ascending order of relative construction costs.

12. FUTURE RESEARCH DIRECTIONS Modelling the site conditions in a slope stability problem properly requires developments in explorations, in-situ and laboratory testing and advance numerical analysis. A numerical analysis performed with well-defined soil parameters in an improper approach cannot be realistic. The recent studies on slope stability analysis are using FEM or comparing FEM results with LEM static or pseudo static approaches. After FEM software programs had started to accept the input of the parameters for more sophisticated soil failure criteria rather than Mohr-Coulomb, it started to be used by engineers. However, the necessity for determination of more parameters to define the stress-strain behavior properly requires new developments in testing and numerical analyzing. Numerical analysis based on regional properties for modelling the stress-strain behavior of soil slopes, are still developing topic. Constitutive modelling of elastoplastic and elasto-viscoplastic behavior of soil while satisfying equilibrium, compressibility and boundary conditions in a slope stability problem requires so much effort. More detailed and case-based stability charts may be developed regarding the FEM for practical purposes.

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13. CONCLUSION Slope stability analysis is performed to predict the behavior of natural and man-made slopes in long and short-term conditions, Therefore, the potential hazard areas, the failure mechanisms with slope sensitivities, triggering mechanisms can be evaluated to design the optimum measures for safety, reliability and economics. Stability of the slope in soils requires well defined stratigraphy and engineering properties of soils prepared by a teamwork of engineering geologists and geotechnical engineers. The soil profile lack of the ground water condition, the thickness of the discrete zone or ancient activity of the site will cause totally wrong input for soil profile. The site investigation without in-situ tests and sampling and soil laboratory testing may result wrongly predicted shear strength parameters. The mode of the shear must be well determined. If the soil on the sliding surface is an overconsolidated clay, peak, fully softened or residual shear strength parameters may be required and even shear viscosity will be helpful to understand flow type slides and creep behavior of soils. The remediation project for a landslide based on a wrong profile or sublayer material properties may cause a disaster or be too expensive. This chapter mainly focus on slope stability of soils. In this scope a review of types and causes of soil slope failures are given. The scope of field investigations and laboratory shear tests of soils were summarized. Slope stability analysis methods with respect to LEM is mentioned. Well known LEM available in most of the slope stability problems such as Bishop’s, Bishop’s Simplified, Janbu’s, Janbu’s Simplified, Spencer’s, Corps of Engineers Modified Swedish Method, Lowe and Karafiath’s, Morgerstern and Price Methods are discussed. Today, software programs using the slice methods for circular or noncircular sliding surfaces are still popular because of the simplicity while modelling. Computer programmers are developing skills to take into account of the uniform change in shear strength parameters with depth in a layer, defining the shear mode of a soil inclusive (tension, compression, shear, bending) etc. However, slope stability problems also require stress deformation analysis especially in urban areas. LEM analysis is lack of the information about stress deformation behavior. So more advanced numerical methods such as FEM, DEM, BEM are being developed. Several software programs specialized for geotechnical engineers are applicable for slope stability analysis. In the analysis the elastoplastic behavior of the soil and the discontinuity in the strata and the soil-structure interaction can be modelled. They are becoming popular due to the element base material property definition allow to model the soil structure interaction of infrastructures i.e. tunnels, pipelines etc. in a slope or superstructures i.e. houses, facilities etc. on a slope. Although 2-D analysis are commercially favorable, in some cases 3-D analysis are crucial especially when there is an important facility in the affected zone of the landslide. In this chapter new remediation and preventative techniques used in slope stability problems were also summarized and some of the research directions were mentioned. Author give a relative construction costs of the methods in ascending order separately. However, the construction cost for remediation or prevention methods of landslide are also changing rapidly related to environmental and site conditions; the topography, accessibility, seasonal conditions, contractor; level of experience, the risks of the project, time of workability, transportation of materials, available equipment park, the specification of the project and etc. In an undesirable situation during the construction may cause unrecoverable losses, which may extremely increase the costs. So the budget for site investigation and laboratory testing will lead a better engineering and a proper site monitoring project covering the stages of before during and after the construction may prevent such events to occur. All these investigations will decrease the total cost of the project compared to a failed project.

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Iyisan, R., Cevikbilen, G., & Hatipoglu, M. (2011). Rapid Estimation of Residual Shear Strength by Ring Shear Test. XV European Conference on Soil Mechanics and Geotechnical Engineering, Athens, Greece. Iyisan, R., Cevikbilen, G., Koltuk, S., & Yilmaz, E. (2006). Measurement of residual shear strength by ring shear test. 7th International Congress on Advances in Civil Engineering, İstanbul, Türkiye. Jahanandish, M., & Keshavarz, A. (2005). Seismic bearing capacity of foundations on reinforced soil slopes. Geotextiles and Geomembranes, 23(1), 1–25. doi:10.1016/j.geotexmem.2004.09.001 Janbu, N. (1954). Stability analysis of slopes with dimensionless parameters. Harvard Soil Mechanics Series, 46, 811. Janbu, N. (1968). Slope stability computations. Soil mechanics and foundation engineering report. Trondheim, Norway: The Technical University of Norway. Janbu, N. (1973). Slope Stability Computations. Embankment Dam Engineering. New York, NY: John Wiley and Sons. Jayawardena, A. W., & Bhuiyan, R. R. (1999). Evaluation of an interrill soil erosion model using laboratory catchment data. Hydrological Processes, 13(1), 89–100. doi:10.1002/(SICI)10991085(199901)13:13.0.CO;2-T Jetten, V. (2002). LISEM user manual, version 2.x. Draft version January 2002. In Utrecht Centre for Environment and Landscape Dynamics (p. 64). Utrecht University. Jiang, J. C., & Yamagami, T. (2006). Charts for estimating strength parameters from slips in homogeneous slopes. Computers and Geotechnics, 33(6–7), 294–304. doi:10.1016/j.compgeo.2006.07.005 Johari, A., & Khodaparast, A. R. (2015). Analytical stochastic analysis of seismic stability of infinite slope. Soil Dynamics and Earthquake Engineering, 79, 17–21. doi:10.1016/j.soildyn.2015.08.012 Kim, J., Salgado, R., & Yu, H. S. (1999). Limit analysis of soil slopes subjected to pore-water pressures. Journal of Geotechnical and Geoenvironmental Engineering, 125(1), 49–58. doi:10.1061/(ASCE)10900241(1999)125:1(49) Krahn, J. (2001). The limits of limit equilibrium analysis. An Earth Odyssey, Calgary, Canada. Ko, K. C. (1972). Discrete element technique for pit slope analysis, Stability of Rock Slopes. 13th Symp. Rock Mechanics, 183–199. Ko, F. W. Y., & Lo, F. L. C. (2016). Rainfall-based landslide susceptibility analysis for natural terrain in Hong Kong - A direct stock-taking approach. Engineering Geology, 215, 95–107. doi:10.1016/j.enggeo.2016.11.001 Kostic, S., Vasovic, N., & Sunaric, D. (2015). Slope stability analysis based on experimental design. International Journal of Geomechanics. doi:10.1061/(ASCE)GM.1943-5622.0000551 Kostic, S., Vasovic, N., & Jevremovic, D. (2016). Stability of earth slopes under the effect of main environmental properties of weathered clay-marl deposits in Belgrade (Serbia). Environmental Earth Sciences, 75(6), 492. doi:10.1007/s12665-016-5339-5

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Kumar, J. (2000). Slope stability calculations using limit analysis. Slope Stab., 239–249. La Rochelle, P., Roy, M., & Tavenas, F. (1973). Field Measurements of cohesion in Champain Clays. Proceedings Eighth International Conference on soil mechanics and foundation engineering, 229-236. Lam, L., & Fredlund, D. G. (1993). A general limit equilibrium model for three-dimensional slope stability analysis. Canadian Geotechnical Journal, 30(6), 905–919. doi:10.1139/t93-089 Leshchinsky, D., Baker, R., & Silver, M. L. (1985). Three dimensional analysis of slope stability. International Journal for Numerical and Analytical Methods in Geomechanics, 9(3), 199–223. doi:10.1002/ nag.1610090302 Li, D.-Q., Qi, X.-H., Cao, Z.-J., Tang, X.-S., Phoon, K.-K., & Zhou, C.-B. (2016). Evaluating slope stability uncertainty using coupled Markov chain. Computers and Geotechnics, 73, 72–82. doi:10.1016/j. compgeo.2015.11.021 Li, X. (2007). Finite element analysis of slope stability using a nonlinear failure criterion. Computers and Geotechnics, 34(3), 127–136. doi:10.1016/j.compgeo.2006.11.005 Li, A. J., Merifield, R. S., & Lyamin, A. V. (2009). Limit analysis solutions for three dimensional undrained slopes. Computers and Geotechnics, 36(8), 1330–1351. doi:10.1016/j.compgeo.2009.06.002 Li, A. J., Merifield, R. S., & Lyamin, A. V. (2010). Three-dimensional stability charts for slopes based on limit analysis methods. Canadian Geotechnical Journal, 47(12), 1316–1334. doi:10.1139/T10-030 Liu, S. Y., Shao, L. T., & Li, H. J. (2015). Slope stability analysis using the limit equilibrium method and two finite element methods. Computers and Geotechnics, 63, 291–298. doi:10.1016/j.compgeo.2014.10.008 Long, E. J., Hargrave, G., Cooper, J. R., Kitchener, B., Parsons, A. J., & Wainwright, J. (2011). Experimental investigation of particle detachment by raindrop impact: three-dimensional measurements of particle trajectory and velocity. AGU Fall Meeting Abstracts, 1, 698. Loukidis, D., Bandini, P., & Salgado, R. (2003). Stability of seismically loaded slopes using limit analysis. Geotechnique, 53(5), 463–479. doi:10.1680/geot.2003.53.5.463 Lowe, J., & Karafiath, L. (1960). Stability of Earth Dams upon Drawdown. Proceedings of the First Pan American Conference on Soil Mechanics and Foundation Engineering, 537-552. Lu, L., Wang, Z. J., Song, M. L., & Arai, K. (2015). Stability analysis of slopes with ground water during earthquakes. Engineering Geology, 193, 288–296. doi:10.1016/j.enggeo.2015.05.001 Lumb, P. (1975). Slope Failures in Hong Kong. Quarterly Journal of Engineering Geology, 8(1), 31–65. doi:10.1144/GSL.QJEG.1975.008.01.02 Luo, N., Bathurst, R. J., & Javankhoshdel, S. (2016). Probabilistic stability analysis of simple reinforced slopes by finite element method. Computers and Geotechnics, 77, 45–55. doi:10.1016/j.compgeo.2016.04.001 Ma, T., Zhou, C., Zhu, T., & Cai, Q. (2008). Modelling raindrop impact and splash erosion processes within a spatial cell: A stochastic approach. Earth Surface Processes and Landforms, 33(5), 712–723. doi:10.1002/esp.1570

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Morgenstern, N. R., & Price, V. E. (1965). The analysis of the stability of generalized slip surfaces. Geotechnique, 15(1), 79–93. doi:10.1680/geot.1965.15.1.79 Nash, D. (1987). Comparative review of limit equilibrium methods of stability analysis. Geotechnical Engineering and Geomorphology, 11-75. Potts, D. M., Kovacevic, N., & Vaughan, P. R. (1990). Finite element analysis of progressive failure of Carsington embankment. Geotechnique, 40(1), 79–101. doi:10.1680/geot.1990.40.1.79 Potts, D.M.. (2003). 42nd Rankine Lecture: Numerical analysis: a virtual dream or practical reality?. Geotechnique, 53(6), 533–574. Powers, J. P. (1992). Construction dewatering a guide to theory and practice. New York, NY: John Wiley & Sons. Reale, C., Gavin, K., Prendergast, L. J., & Xue, J. (2016). Multi-modal reliability analysis of slope stability. Transportation Research Procedia, 14, 2468–2476. doi:10.1016/j.trpro.2016.05.304 Sarma, S. K. (1973). Stability analysis of embankments and slopes. Geotechnique, 23(3), 423–433. doi:10.1680/geot.1973.23.3.423 Sharma, P. P., & Gupta, S. C. (1989). Sand detachment by single raindrops of varying kinetic energy and momentum. Soil Science Society of America Journal, 53(4), 1005–1010. doi:10.2136/sssaj1989.0 3615995005300040003x Shinoda, M. (2015). Seismic stability and displacement analyses of earth slopes using non-circular slip surface. Soil and Foundation, 55(2), 227–241. doi:10.1016/j.sandf.2015.02.001 Shou, K. J., & Wang, C. F. (2003). Analysis of the Chiufengershan landslide triggered by the 1999 ChiChi earthquake in Taiwan. Engineering Geology, 68(3-4), 237–250. doi:10.1016/S0013-7952(02)00230-2 Silva, F., Lambe, T. W., & Marr, W. A. (2008). Probability and risk of slope failure. Journal of Geotechnical and Geoenvironmental Engineering, 134(12), 1691–1699. doi:10.1061/(ASCE)10900241(2008)134:12(1691) Sitar, N., MacLaughlin, M. M., & Doolin, D. M. (2005). Influence of kinematics on landslide mobility and failure mode. Journal of Geotechnical and Geoenvironmental Engineering, 131(6), 716–728. doi:10.1061/(ASCE)1090-0241(2005)131:6(716) Spencer, E. (1967). A method for analysis of the stability of embankments assuming parallel interslice forces. Geotechnique, 17(1), 11–26. doi:10.1680/geot.1967.17.1.11 Sun, G., Cheng, S., Jiang, W., & Zheng, H. (2016). A global procedure for stability analysis of slopes based on the Morgenstern-Price assumption and its applications. Computers and Geotechnics, 80, 97–106. doi:10.1016/j.compgeo.2016.06.014 Taylor, D. W. (1937). Stability of earth slopes. J. Boston Soc. Civil Eng., 24, 197–246. Terzaghi, K., & Peck, R. B. (1948). Soil Mechanics in Engineering Practice. New York, NY: John Wiley and Sons.

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Tschuchnigg, F., Schweiger, H. F., & Sloan, S. W. (2015). Slope stability analysis by means of finite element limit analysis and finite element strength reduction techniques. Part I: Numerical studies considering non-associated plasticity. Computers and Geotechnics, 70, 169–177. doi:10.1016/j.compgeo.2015.06.018 Ugai, K. (1985). Three-dimensional stability analysis of vertical cohesive slopes. Soil and Foundation, 25(3), 41–48. doi:10.3208/sandf1972.25.3_41 Ugai, K., & Hosobori, K. (1988). Extension of simplified Bishop method, simplified Janbu method and Spencer method to three dimensions. Proceedings of the Japanese Society of Civil Engineers, 394(3-9), 21–26. (in Japanese with English abstract) U.S. Army Corps of Engineers. (1970). Stability of Earth and Rock-Fill Dams. EM 1110-2-1902. Vicksburg, MS: U.S. Army Engineer Waterways Experiment Station. Varnes, D. J. (1978). Slope movement types and processes. In Special Report 176: Landslides: Analysis and Control. Transportation and Road Research Board, National Academy of Science. Wang, Y. J., & Voight, B. (1969). A discrete element stress analysis model for discontinuous materials. Proc. Int. Symp. on large permanent underground openings. Wicks, J. M., & Bathurst, J. C. (1996). SHESED: A physically based, distributed erosion and sediment yield component for the SHE hydrological modeling system. Journal of Hydrology (Amsterdam), 175(14), 213–238. doi:10.1016/S0022-1694(96)80012-6 Wu, Y., Lan, H., Gao, X., Li, L., & Yan, Z. (2015). A simplified physically based coupled rainfall threshold model for triggering landslides. Engineering Geology, 195, 63–69. doi:10.1016/j.enggeo.2015.05.022 Yu, H. S., Salgado, R., Sloan, S. W., & Kim, J. M. (1998). Limit analysis versus limit equilibrium for slope stability. Journal of Geotechnical and Geoenvironmental Engineering, 124(1), 1–11. doi:10.1061/ (ASCE)1090-0241(1998)124:1(1) Zao, L. H., Cheng, X., Zhang, Y., Li, L., & Li, D. J. (2016). Stability analysis of seismic slopes with cracks. Computers and Geotechnics, 77, 77–90. doi:10.1016/j.compgeo.2016.04.007 Zhang, X. (1999). Slope stability analysis based on the rigid finite element method. Geotechnique, 49(5), 585–593. doi:10.1680/geot.1999.49.5.585 Zheng, H., Liu, D. F., & Li, C. G. (2005). Slope stability analysis based on elasto-plastic finite element method. International Journal for Numerical Methods in Engineering, 64(14), 1871–1888. doi:10.1002/ nme.1406 Zheng, H., Tham, L. G., & Liu, D. (2006). On two definitions of the factor of safety commonly used in the finite element slope stability analysis. Computers and Geotechnics, 33(3), 188–195. doi:10.1016/j. compgeo.2006.03.007 Zhu, D. Y. (2001). A method for locating critical slip surfaces in slope stability analysis. Canadian Geotechnical Journal, 38(2), 328–337. doi:10.1139/t00-118

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ADDITIONAL READING Batali, L., & Andreea, C. (2016). Slope Stability Analysis Using the Unsaturated Stress Analysis. Case Study Procedia Engineering Advances in Transportation Geotechnics, 143, 284–291. doi:10.1016/j. proeng.2016.06.036 Head, K. H. (1988). Manual of soil laboratory testing: Vol. 2. Shear Strength Tests. London: Pentech Press.

KEY TERMS AND DEFINITIONS Drain: Artificially produced drainage paths in the soil for surficial or ground water to pass through. FEM: Finite Element Method is a numerical technique for finding approximate solutions to boundary value problems for partial differential equations. Fully Softened Shear Strength: The peak drained shear strength of a clay in a normally consolidated state. LEM: Limit Equilibrium Method is numerical analysis of forces and/or moment acting on a body in equilibrium condition. Pseudo-Static Analysis: In earthquake engineering to analyze the seismic response of soil embankments and slopes simply adding a permanent body force representing the earthquake shaking to a static limit-equilibrium analysis. Residual Shear Strength: The minimum and constant shear strength attained at large displacements after the peak shear strength. Slope Angle: Is the angle between ground surface and horizontal.

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Chapter 12

Determination of the Cyclic Properties of Silty Sands Eyyüb Karakan Kilis 7 Aralik University, Turkey Selim Altun Ege University, Turkey

ABSTRACT Liquefaction may be triggered by cyclic loading on saturated silty sands, which is responsible of severe geotechnical problems. Development of excess pore water pressure in soil results in a liquid-like behavior and may be the reason of unavoidable superstructural damage. In this study, in order to investigate the behavior of saturated silty sands exposed to cyclic loading under undrained conditions, a systematic testing program of stress-controlled cyclic triaxial tests was performed on specimens of different silt contents, under different loading conditions and environment. The effect of parameters such as silt content on the liquefaction behavior of specimens was studied. Pore water pressure and shear strain curves were obtained for the silty sands. Furthermore, the boundaries existing in the literature on sands are compared with the results current research, on silty sands. Conclusively, the outcomes of this study were useful to develop insight into the behavior of clean and silty sands under seismic loading conditions.

1. INTRODUCTION While the cyclic behavior of clean sands has been investigated in-depth over the fifty years, this phenomenon in silty sand containing varying amounts of fines, stimulated a real interest in recent twenty years. The number of researches on this subject are quite limited and the studies claim that this type of soil is more susceptible to liquefaction, in comparison with clean sand. However, the reported results are still contradictory due to effect of fines content on the shear strength of silty sands. The effect of fines content on the cyclic liquefaction potential of sands has been investigated extensively in geotechnical literature. Several investigations in the field shows that the presence of fines increases liquefaction resistance (Seed and Lee 1966; Seed et al., 1985) while laboratory tests results show different trends, for the fine content less than 30% (Koester et al., 1994; Troncoso, 1990). Koester (1990) DOI: 10.4018/978-1-5225-2709-1.ch012

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 Determination of the Cyclic Properties of Silty Sands

claimed that fine content is more important than the plasticity index (PI), contrary to Ishihara (Ishihara, 1993) and Prakash and Guo (1999), claiming that high plasticity fines might change the liquefaction behavior. Finn et al. (1993) indicated that many of the past studies used different criteria for comparison of the effect of fines on liquefaction resistance, resulting different conclusions. The effect of fines content on liquefaction resistance are based on mechanisms of deformation in the particle size level. Laboratory test results suggest that fines in small percentages (F.C 125

(Joint water press)/ (Major principal σ)

0

< 0.1

0.1, - 0.2

0.2 - 0.5

> 0.5

General conditions

Completely dry

Damp

Wet

Dripping

Flowing

15

10

7

4

0

Groundwater

Rating

B. RATING ADJUSTMENT FOR DISCONTINUITY ORIENTATIONS (See F) Very favourable

Favourable

Fair

Unfavourable

Very Unfavourable

Tunnels & mines

0

-2

-5

-10

-12

Foundations

0

-2

-7

-15

-25

Slopes

0

-5

-25

-50

Strike and dip orientations

Ratings

C. ROCK MASS CLASSES DETERMINED FROM TOTAL RATINGS Rating

100 - 81

80 - 61

60 - 41

40 - 21

< 21

Class number

I

II

III

IV

V

Description

Very good rock

Good rock

Fair rock

Poor rock

Very poor rock

Class number

I

II

III

IV

V

Average stand-up time

20 yrs for 15 m span

1 year for 10 m span

1 week for 5 m span

10 hrs for 2.5 m span

30 min for 1 m span

D. MEANING OF ROCK CLASSES

continued on following page 509

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Table 2. Continued Cohesion of rock mass (kPa)

> 400

300 - 400

200 - 300

100 - 200

< 100

Friction angle of rock mass (deg)

> 45

35 - 45

25 - 35

15 - 25

< 15

E. GUIDELINES FOR CLASSIFICATION OF DISCONTINUITY CONDITIONS Discontinuity length (persistence)

20 m

Rating

6

4

2

1

0

Separation (aperture)

None

< 0.1 mm

0.1 - 1.0 mm

1 - 5 mm

> 5 mm

Rating

6

5

4

1

0

Roughness

Very rough

Rough

Slightly rough

Smooth

Slickensided

Rating

6

5

3

1

0

Infilling (gouge)

None

Hard filling < 5 mm

Hard filling > 5 mm

Soft filling < 5 mm

Soft filling > 5 mm

Rating

6

4

2

2

0

Weathering

Unweathered

Slightly weathered

Moderately weathered

Highly weathered

Decomposed

Ratings

6

5

3

1

0

F. EFFECT OF DISCONTINUITY STRIKE AND DIP ORIENTATION IN TUNNELLING** Strike perpendicular to tunnel axis

Strike parallel to tunnel axis

Drive with dip Dip 45 - 90°

Drive with dip - Dip 20 - 450

Dip 45 - 900

Dip 20 - 450

Very favourable

Favourable

Very unfavourable

Fair

Drive against dip Dip 45-90°

Drive against dip - Dip 20-450

Dip 0-20 - Irrespective of strike0

Fair

Unfavourable

Fair

Q = RQDJn × JrJa × JwSRF where RQD is the Rock Quality Designation Jn is the joint set number Jr is the joint roughness number Ja is the joint alteration number Jw is the joint water reduction factor SRF is the stress reduction factor The three pairs of ratios represent the relative block size (RQD/Jn), the minimum interblock shear strength (Jr/Ja) and active stress (Jw/SRF). The possible Q-values range from approximately 0.001 to 1000, and support recommendations are given when the value is combined with the dimensions of the tunnel or cavern in a Q-support chart (Figure 20).

7.5. Geological Strength Index (GSI) The GSI rock classification system proposed by Hoek (1998) is more subjective than the other classifications and is totally based on observatory evaluations (Hoek ve Brown, 1997). Sönmez and Ulusay 510

 Geological and Geotechnical Investigations in Tunneling

Figure 20. The support selection chart for the Q-system (Barton and Bieniawski, 2008)

(2002) tried to quantify this classification system. The system is practical and user friendly especially regarding the grading of tunnel face. The geological strength index (GSI) provides a system for assessing the decrease in rock mass strength for different rock mass conditions (Hoek et al., 1998). The rock conditions are defined considering blockiness and surface conditions of discontinuities indicated by alteration degree nad roughness by field studies. The GSI value is determined from the above mentioned parameters according to Figure 21. The use of these parameters together describes rock structures ranging from firmly interlocked strong rock fragments to heavily crushed rock masses. After obtaining GSI value, the compressive strength of the rock mass (σcm) and its deformation modulus (Em) can beestimated by a set of empirically developed equaitions. After determinin the the GSI values, the uniaxial compressive strength σci and the material constant mi are needed to be determined. These strength parameters can be obtained from laboratory testing or estimated from published tables (Hoek et al., 1998).

7.6. ÖNORM B 2203 ÖNORM B 2203 Due to the overwhelming success of the New Austrian Tunnelling Method (NATM) there has been a trend towards evaluation of the rock mass quality according to the criterions of Austrian Standard ÖNORM B 2203. The ground is grouped into several classes each class being given a specific type and amount of temporary support, in addition to specific excavation steps. Rock Class Description Austrian Standard ÖNORM B 2203 A1 Stable1 Stable A2 Slightly overbreaking2 Afterbreaking B1 Friable3 Slightly friable B2 Heavily friable4 Friable or slightly pressure exerting C1 Pressure exerting5 Heavily friable or pressure exerting C2 Heavily pressure exerting6 Heavily pressure exerting L1 Loose ground, highly cohesive L2 Loose ground, low cohesive

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 Geological and Geotechnical Investigations in Tunneling

Figure 21. GSI classification

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 Geological and Geotechnical Investigations in Tunneling

7.7. Rock Mass Quality Rating (RMQR) This new rock mass rating system is used to estimate the geomechanical properties of rock masses. The most recent rock classification system which is known as RMQR is proposed by Aydan et al (2014). In this classification the degree of weathering, number of discontinuity sets, aperture or RQD, conditions of discontinuity, groundwater flow and infiltration conditions of groundwater are tried to be quantified.

7.8. The Evaluation of Classification Systems There are quite a number of publications that discuss the advantages and limitations of empirical rock mass classification systems, (e.g. Riedmüller and Schubert (1999), Palmström and Broch (2006), Barton and Bieniawski, (2008) and Pells (2008)). The interaction between geological features such as fracture orientation, degree of fracturing, fracture shear strength and stress conditions are not sufficiently considered (Riedmüller and Schubert (1999)).

8. NUMERICAL ANALYSIS IN TUNNELS Tunnel structures are difficult to be projected due to some complexities such as their geometry and pressures they receive and properties of geologic environment and coating material and their interferences. Tunnel excavations are required to be projected with respect to a low-cost excavation design and the most suitable ground support to provide safety. Meanwhile, in some cases construction of surface structures on and/or in the vicinity of tunnels may be an indispensable need. Therefore, design of surface structures in areas of existing tunnels necessitates investigation not only classical soil-structure interference but also structure, soil and tunnel interferences as well. Either tunnel projecting in areas of surface structures or projecting surface structures or another underground structure in the tunnel area requires studying the interference of structures with detailed geotechnical surveys. For determination of stability of underground excavations several empirical equations have been proposed (Singh et al., 1992; Aydan et al., 1993; Barla, 1995; Goel et al., 1995; Hoek et al., 1995; Bhasin and Grimstad, 1996; Carranza-Torres and Fairhust, 1999; Carranza Torres and Fairhust, 2000) and as a result of rapid development in the computer technology new numerical analysis methods have been developed such as finite element (FEM), discrete element (DEM), finite differences (FDM) and boundary element (BEM). However, when structure-tunnel interference is required to be evaluated, the use of numerical methods with the aid of computer programs becomes inevitable. In numerical analysis performed with computer programs, all materials under consideration including geologic units should be transferred to analysis medium with a lumped model. Accurate description of materials comprising the whole system is directly related to reliability of analysis results. These material models significantly vary with respect to geologic units and the selection of model for the units directly affects the results. The computer program used for the selection of material models and its remarks regarding these models play an important role for the selection of engineering parameters and thus accuracy of analyses results. For description of lumped models, in addition to selection of correct lumped model, selection of accurate drainage conditions also affect the analyses results. For example,

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 Geological and Geotechnical Investigations in Tunneling

in a model which examines dynamic effect, it should be remembered that clay lithologies will behave in a non-cohesive manner and void water pressure will increase as a result of dynamic effect, and in material lumped model parameters essential for undrained conditions should be considered and necessary arrangements should be made in the computer program used. In analysis carried out for static conditions, permeability of geologic units needs to be accurately described and during tunnel excavations the amount of groundwater fluxes and its effects should be monitored. With the aid of computer programs which are significantly developed in the recent years, it is possible to 2-D and/or 3-D simulate the construction stages of both surface and underground structures to be built any type of geologic environment. Although geologic units with soil character show some changes with respect to numerical analysis method and computer program used in the study, parameters of volume weight, cohesion, friction angle, Poisson ratio, elasticity module and odometer module comprise the main parameters. Well known consolidation properties particularly of fine-grained environments are necessary for the formation of lumped model for the unit. When geologic environment is composed of rock type lithologies, it is necessary to determine rock mass parameters and classify the rock mass with detailed geotechnical studies and engineering characteristics of discontinuity and bedding are needed to be specified in detail (Table 3). Considering the above mentioned issues, Figure 22 shows an example study for deformation characteristics obtained from the evaluation of tunnel-structure interference prior to construction in a tunnel medium. As a result such study, deformations occurring at the surface and in the tunnel and stress conditions in tunnel ferroconcrete elements are summarized in Figure 23. Table 3. The input parameters, which are required by numerical models, their obtaining methods and effects to numerical models. (Satıcı ve Topal, 2015) Parameters Identify lithologic and stratigraphic

Method of obtaining

Effect

Geological - Geotechnical field work, Engineering Geological Model

Direct

Weathering, strength, roughness

Geological - Geotechnical field work

Indirect

Discontinuities, set number, length, opening, fill

Geological - Geotechnical field work, Engineering Geological Model

Direct, Indirect

RQD

Geological - Geotechnical field work

Indirect

Groundwater conditions

Geological - Geotechnical field work, Engineering Geological Model

Direct

Rock mass classification score (RMR, Q, GSI)

Geological - Geotechnical field work, Engineering Geological Model

Direct, Indirect

Strength test (UCS, TCS, PLT)

Laboratory experiments

Direct, Indirect

Elasticity modulus of the rock (Ei)

Laboratory experiments

Direct

Deformation modulus of the rock mass

Empirical methods

Direct

Rock - c, Φ, γ, ʋ, UCS

Laboratory experiments

Direct

Rock mass - c, Φ, γ, ʋ, UCS

Empirical methods

Direct

Discontinuities - c, Φ

Empirical methods

Direct

σv,σh

Empirical methods or In-Situ Tests

Direct

514

 Geological and Geotechnical Investigations in Tunneling

Figure 22. Distribution of deformations after structure load

Figure 23. Bending moment, axial force and total displacements at tunnel surface

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9. INVESTIGATION OF EXCAVATION METHOD FOR TUNNELS Geologic and geotechnical properties are the most important parameters for the tunnel excavation. Discontinuity characteristics in the rock medium and some parameters such as grain size, cohesion and friction angle directly affect the efficiency of machine and thus excavation progress. For this purpose, geologic profile given in detailed project is examined. Type of geologic units progressed along the tunnel axis, geotechnical classifications for these units, their pressure strengths, discontinuity characteristics and water contents are investigated. Based on these parameters and considering the integrity of project, excavation method should be selected. Realistic selection of equipment and excavation method to be applied for rock masses on which tunnel or engineering structures are constructed minimizes the excavation cost (Kaya et al., 2011). Therefore, several empirical excavability and detachability classifications were proposed for which rock mass and material properties are used as the input parameters (Franklin et al., 1971; Atkinson, 1971; Bailey, 1975; Weaver, 1975; Kirsten, 1982). Among them, parameters used by 4 of these classifications are described below. In excavability classification system suggested by Franklin et al. (1971), joint spacing and uniaxial pressure strength or point load index value are used as the input parameters. In the excavability classification system proposed by Kirsten (1982), uniaxial pressure strength, relative structure number, RQD, number of joint sets, joint roughness number and joint surface weathering index are used to determine excavation class. In excavability classification system suggested by Pettifer and Fookes (1994), joint spacing index and point load index value are used as the input parameters. In the excavability classification system proposed by Tsiambaos and Saroglou (2009), geologic strength index (GSI) and point load strength index value are used as the input parameters and two different GSI charts were used to evaluate the rock masses with respect to point load strength index by means of excavability. As shown from these classifications, excavability of rocks in the tunnel is affected chiefly by strength of rock material and discontinuity characteristics. In spite of recent developments in TBM technology and increasing tunneling experience with TBM under hard conditions, there has been no full-featured TBM machine to be operated under any type geologic condition. In other words, TBM’s produced can partly challenge with geological variations. Consequently, geologic surveys in tunnel projects which use TBM should be carried out carefully and in detail and machinery production should be made based on data from such studies (Yüksel andBilgin, 2014). Initial studies on excavation performance of TBM were related to some mechanical properties such as uniaxial compression strength of massive rock, elasticity module, hardness and abrasiveness and excavation parameters including penetration, compressive force and torque. Most of these works are theoretical and empirical studies. Most studies above mentioned are based on mechanical properties of massive rock and do not comprise all geomechanical properties of rock medium. Some sub-properties such as joints, block size, joint or bedding orientation, surface roughness of discontinuities and weathering degree affect the insitu behavior of rock and thus excavation performance. Following the rock mass classifications proposed in 1965s, the relation between rock mass characteristics and TBM excavation performance was started to be examined. In the Q classification system proposed by Barton et al. (1974) developed a QTBM criterion adding uniaxial pressure strength and quartz content, horizontal field load, disk load and disk lifetime index to their Q classification system and gave empirical equations between this parameter and progress rate (Barton, 2000). Bienawski et al. (2006) suggested a criterion for “Rock Mass Excavability” (RME) which is similar to that of Barton et al. (1974) 516

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and using the observations in Spanish and Ethiopian tunnels proposed relations for progress rate, torque and disk load. Sapigni et al. (2002) for tunnels in Spain, Hassanpour et al. (2009), Farrokh et al. (2012) and Oraee et al. (2010) for tunnels in Iran and Bilgin et al. (1999) for tunnels in Turkey developed excavation performance estimation models based on geomechanical characteristics of 18 rock types. With these models penetration, compressive force, torque and progress rate may be estimated. Considering processes necessary for a certain excavation period, there might be some delays for production process such as excavation and support, for TBM such as maintenance, breakdown and for non-TBM factors such as geology, material supply, measurement, ventilation and lack of coordination. Some of delays in the total excavation period are compensated to a large extent by increasing experience of workers during the training at the worksite and also minimizing the technical problems. Although there are different TBM performance estimation models, the method used in this study is applied in areas of complex geology and intense housing and is based on uniaxial compressive strength, RQD and specific energy. Following obtaining these parameters performance estimation of machine may be made (Tüysüz, 2012).

10. INVESTIGATION OF FAILURE MODELS FOR TUNNELS Martin et al. (2003) indicated two common instability types around underground openings in hard rock structurally controlled gravity-driven initabilities leading to wedge type falls-of-ground and stress-induced failure or yielding. In the case of low confinement zones wedge type failures or sliding from the roofs and sidewalls of tunnels are commonly observedespecially at shallow depths or at tunnel intersections. Stresses and block sizes are the critical parameters for brittle failures. Stress induced failures occur at stress magnitude nearly equal to the rock mass strength, and the resulting yielding may generate large convergence displacements (Kvartsberg, 2013). Palmström and Stille (2007) added a third category; the groundwater initiated failures, and declared a summary of behavior types, given in Table 4 and Figure 24. Rock blasting problems are often encountered in underground openings that form in firm rocks (Aydan and Geniş, 2010). Rock blasting occurs during excavation as rapidly bounding of rock fragments. Rock blasting is mostly related with brittle failure of compact rocks such as gneiss, quartzite, volcanic rocks and siliceous sandstone. MontBlanc in France, Gotthard in Switzerland, Dai-Shimizu and Kanetsu tunnels in Japan are well known examples in tunneling for rock blasting. Rocks blasted are characterized by behaviors of high strength, high deformation module and brittle failure (Figure 25). Rock compress problems are observed in weak rocks. Aydan et al. (1993; 1996) proposed a method for estimation of rock compression potential and deformation in the tunnel. Rock compression occurs in rocks with uniaxial compression strength less than 20-25 MPa such as phyllite, mudstone, siltstone, gypsum, and/or sheared metamorphic and magmatic rocks. Compressing rocks show low strength, low deformation module values and ductile post-failure behavior (Figure 26). Due to large deformations on rocks around the tunnel rock compression is thought to be a decrease in the tunnel cross section. The first scientific description for compressed rocks was made by Terzaghi (1946). Considering the experience of Terzaghi and other researchers (Barton, 1974; Hoek, 1985; Bieniawsky, 1989; Aydan, et. al. 1993; Barla, 1995), this process is found to occur in weak rock at shallow depths and in rock medium intensely deformed as a result tectonism. Claystone, sandstone, marl, schist/graphite schist and altered and/or altered and/or fragmented metamorphic and volcanic rocks are some of examples. Rock compression is mechanical elasto-plastic behavior of rock under strain (Aydan et al., 1993). In other words, compression occurs when rock has failed as a result of redistribution of stresses after the tunnel excavation. 517

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Table 4. Summary of behaviour types in underground excavations, (Palmström and Stille, 2007) FAILURE MODE GROUP

Gravity driven

Stress induced

Water influenced

BEHAVIOUR TYPE a.

Stable

b.

Block fall(s)

-

of single blocks

-

of several blocks

c.

Cave-in

d.

Running ground

e.

Buckling

f.

Rupturing from stresses

g.

Slabbing

h.

Rock burst

i.

Plastic behaviour (initial)

j.

Squeezing

k.

Ravelling from slaking

l.

Swelling

-

of certain rocks

-

of certain clay seams or fillings

m.

Flowing ground

n.

Water ingress

Brittle behaviour

Plastic behaviour Hydratization Swelling minerals

Flowing water

Figure 24. Summary of behavior types for underground excavations (Palmström and Stille, 2007)

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Figure 25. Typical stress-unit deformation responses of rocks which show rock blasting and compression effects (Aydan ve Geniş, 2010)

Figure 26. Deformation in tunnels due to insufficient support in compressing medium

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11. INVESTIGATION OF INSITU GEOTECHNICAL MEASUREMENTS FOR TUNNELS In tunnel engineering monitoring during construction is essential to overcome natural uncertainties. Monitoring of displacements provides important information. This information gives rise to determination of tunnel stability, better establishment of unsuitable events, estimation of quality of rock mass on the front of face, assessment of failure mechanism for the rock mass, arrangement of suitable excavation support and performing the most suitable tunnel construction in regard to safety and cost (Figure 27).

11.1. Rod Extensometers They are placed into the borehole to monitor vertical movement of soil. It is composed chiefly of three stages. It is lowered to the borehole as the lowest stage to be maximum 2 m above the tunnel ceiling. The locations of other stages are determined with respect to geologic structure. The number of stages might be increased or decreased when necessary. The measurements are taken from extensometers established on the surface. Figure 27. Principles of measurement of underground and surface movements in tunnels (Arıoğlu, et. al. 2002)

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11.2. Convergence and Opto-Trigonometric Measurements In Tunnels Convergence measurements are made with 0.01 mm-sensitive band extensometers to monitor relative movements of tunnel ceiling and walls whilst opto-trigonometric measurements are made to measure absolute movement of walls in vertical and horizontal directions. Both measurements are carried out using a total of five collection bolts as one on the ceiling and two on side walls. In tunnels measurement sections are made for about every 25 m.

11.3. Surface and Building Settlement Measurements Settlements are measured with bolts nailed on the surface and buildings. For this, measurement sections are made at the surface for about every 25 m upright to the tunnel axes. Like for the rod extensometers, the measurement frequency is determined with respect to measurement section of tunnel face. However, settlements at the stations are taken every day or every few days depending on the critical condition.

11.4. Anchorage Load Cells They are oil-filled cylindrical devices that are placed on the anchorage heads and connected to a manometer to monitor the changes of stress forces on the anchorages. Before the placement, each cell is tested and calibrated at the laboratory. After the placement readings are made daily.

11.5. Rock Bolt Load Cells They are used to monitor stress changes on rock bolts. The main principle is similar to load cells in anchorages. However since pre-stress force applied to rock bolts is lower, the size of load cells is smaller. Like in the convergence and opto-trigonometric measurements, the measurement frequency is arranged with respect to distance between measurement site and tunnel face.

11.6. Hydraulic Pressure Cells They are placed to monitor radial and tangential stresses applied onto shotcrete. Measurements are arranged with respect to distance between measurement site and tunnel face.

12. FUTURE RESEARCH DIRECTIONS In spite of the tunneling studies that started with the Babylor in about B.C. 2160, today the tunnel research techniques should be developed. In the future, it will be necessary to reduce the excavation-support systems based on human power, to increase the mechanical systems, and at the same time to develop the supporting systems and measurement systems. Greater involvement of research activities in the projects will be the pioneer to ensure all these requirements. In this way, it may be possible to produce more economical projects and reduce the risk of accidents to zero. In numerical analyzes, which has been

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increasingly used in recent years and which is done with the help of computer software, the material model representing the geotechnical environment as required must be defined correctly. For this reason, directing the tunnel investigations together with the expert who will carry out the numerical analysis, obtaining necessary parameters for the material model not empirically but by the field and laboratory datas will provide the necessary foresight in the tunneling work.

13. CONCLUSION In order to establish a stable structure in tunnels geologic and geotechnical properties should be investigated in detail. When tunnel is opened geologic, geotechnical and hydrogeologic investigations should be conducted and length and depth of route, stress conditions in the environment and intensity of settlements have to be specified. Following the geologic mapping, geologic model and tunnel cross section to be proposed should be verified with geophysical and drilling data. In order to determine problematic sites along the tunnel route, necessary tests should be conducted within the borehole and on disturbed and undisturbed samples. In photogeologic and remote sensing studies lithologic and structural elements, particularly linear elements, are easily observed. For this purpose, photogeologic and remote sensing studies with proper scales provide important contribution to tunneling works. Total drilling length for tunnel must be around percent 0.2 times of the tunnel length. Boreholes opened before the tunnel construction should be drilled on the surface at both sides of axis in a distance nearly two-fold of diameter. The spacing and frequency of boreholes should be specified with respect to type of formations and geologic conditions. Borehole depth in the route of correlation tunnel must be extended to the tunnel base as much as half of tunnel diameter depending on bedding dips and if problematic rocks are progressed it should be extended at least two-fold of tunnel diameter. Since tunnels are underground structures they are significantly affected by geologic and structural factors such as bedding, faulting, active faults and alteration zones. Distribution of loads on tunnels is closely related to geometry of beddings. Positions of these beddings and faults cause residual stress and stresses to be greater than expected. In addition, prior to engineering activities to be conducted in the tunnel below the water level, groundwater should be drained and water proofing provided. Rock blasting, heat and gas problems and collapses comprise some of problems affecting the effective use of tunnel during and after the construction of tunnel. In general, for tunnels opened in the city, interaction with structural medium in NATM and TBM excavations, weak and insufficient buildings that do not agree with technical specifications, registered historical buildings, difficulties encountered during evacuation of buildings, negative conditions arising from heterogeneous geologic settings, determination o water wells in the studied area, lowering of groundwater level and consolidation settlements, material fatigue and breakdown due to long-term stopping of TBM and difficulties in soil improvement due to archeological findings should be considered. Some geotechnical measurements are needed to monitor and record changes in deformation and load on revetment elements and adjacent rocks in tunnels. The location and distance between geotechnical measurement sections must be selected based on geologic conditions, frequency of changes, mechanical behavior of rock, tunnel length, main stress conditions and tunnel size. Convergency nails or bolts are

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installed on front tunnel coating. In order to determine relative displacements in front tunnel coating and tunnel space, measurements are made with band extensometers or optical electronic methods. Well extensometers are used for measurement of displacements at varying depth of soil surrounding the tunnel. Extensometer readings provide information on absolute displacement amount around the tunnel and depth and pattern of deformations on tunnel rock. They are also used for specifying and examination of length of rock bolts.

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Rowan, L. C., Hook, S. J., Abrams, M. J., & Mars, J. C. (2003). Mapping hydrothermally altered rocks at cuprite, nevada, using the advanced spaceborne thermal emission and reflection radiometer (aster), a new satellite-imaging system. Economic Geology and the Bulletin of the Society of Economic Geologists, 98(5), 1019–1027. doi:10.2113/gsecongeo.98.5.1019 Sabins, F. F. (1997). Remote Sensing-Principles and Interpretation. New York: W.H. Freeman and Company. Sapigni, M., Bert, M., Bethaz, E., Busillo, A., & Cardone, G. (2002). TBM performance estimation using rock mass classifications. Rock Mechanics and Mining Sciences, 39(6), 771–788. doi:10.1016/ S1365-1609(02)00069-2 Satıcı, Ö., & Topal, T. (2015). Tünel Açma Yöntemlerinin Mühendislik Jeolojisi ve Kaya Sınıflama Sistemleri ile Değerlendirilmesi. Jeoloji Mühendisliği Dergisi, 39(1), 45-57. Schmertmann, J. H. (1975). Measurement of In-Situ Shear Strength. Proceedings of the 7th PSC (Vol. 2, pp. 57 -138). Singh, B., Jethwa, J. L., Dube, A. K., & Singh, B. (1992). Correlation between observed support pressure and rock mass quality. Tunnelling and Underground Space Technology, 7(1), 59–74. doi:10.1016/08867798(92)90114-W Solomon, S., & Ghebreab, W. (2006). Lineament characterization and their tectonic significance using Landsat TM data and field studies in the central highlands of Eritrea. Journal of African Earth Sciences, 46(4), 371–378. doi:10.1016/j.jafrearsci.2006.06.007 Stroud, M. A. (1974). The Standard Penetration Test in Insensitive Clays and Softrock. Proceedings of the 1st European Symposium on Penetration Testing, Stockholm, Sweden (pp. 367 – 375). Stroud, M. A. (1988). The Standart Penetration Test - Its Implication and Interpretation. London: Thomas Telford. Szechy, K. (1970). The Art of Tunnelling. Budapest: Akademiai Kıado. Tangestani, M. H., Jaffari, L., Vincent, R. K., & Maruthi Sridhar, B. B. (2011). Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran. Remote Sensing of Environment, 115(9), 2243–2254. doi:10.1016/j.rse.2011.04.023 Terzaghi, K. (1946). Rock defects and loads in tunnel supports, Rock tunneling with steel supports (pp. 17–99). Youngstown, Ohio: The Commercial Shearing and Stamping Co. von Terzaghi, K. (1967). Soil Mechanics in Engineering Practice. John Wiley & Sons, New York. Tsiambaos, G., & Saroglou, H. (2009). Excavatabilityassessment of rock masses using the Geological Strength Index (GSI). Bulletin of Engineering Geology and the Environment, 69(1), 13–27. Tüysüz, L. (2012). İstanbul’da açılacak metro tünellerinde TBM (tünel açma makinesi) performansını tahmin etmek için yeni bir yaklaşım. İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Maden Mühendisliği Anabilim Dalı.

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Ulusay, R., & Sönmez, H. (2002). Kaya kütlelerinin mühendislik özellikleri, TMMOB, jeoloji Mühendisleri odası yayınları. Vardar, M. (2013). Dünyada ve Türkiye’de tünelcilik ve yeralti geçişleri. Türkiye tünelcilik semineri. Weaver, J. M. (1975). Geological factors significant inthe assessment of rippability. The Civil Engineering in South Africa, 17(12), 313–3. Webb D.L., Mival K.N., & Allinson A.J. (1982). A comparison of methods determining settlements in estuarine sands from Dutch cone penetration tests. Wu, T.D. & Lee, M.T. (2007). Geological lineament and shoreline detection in SAR. Yüksel, A., & Bilgin, N. (2014). Kayanın Jeomekanik Özelliklerinin Metro Tünellerinde Kullanılan Tünel Açma Makinelerinin Performansına Etkisi. Yerbilimleri, 35(1), 17–36. doi:10.17824/huyuamd.78237 Zhang, X., & Pazner, M. (2007, May). Comparison of Lithologic Mapping with ASTER, Hyperion, and ETM Data in the Southeastern Chocolate Mountains, USA. Photogrammetric Engineering and Remote Sensing, 73(5), 555–561. doi:10.14358/PERS.73.5.555

KEY TERMS AND DEFINITIONS Geotechnical Test: Experiments carried out in the field and in the laboratory to determine the physical and mechanical properties of the soil and rock. Groundwater: It is the water present beneath Earth’s surface in soil pore spaces and in the fractures of rock formations. In-Situ Geotechnical Measurements: In-situ geotechnical measurements are measurement studies carried out to remove uncertainties foreseen in geotechnical studies. Rock Mass Classification: Rock mass classification is a classification made for obtaining engineering parameters and forming geotechnical approaches in rock mass where many different geotechnical parameters are active. Tunnel: It is an underground or underwater passageway, dug through the surrounding soil/rock and enclosed except for entrance and exit, commonly at each end. Tunnel Investigation: All of the geological and geotechnical studies carried out during the construction and application of the tunnel projects.

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Chapter 15

Multi Criteria Decision Making Techniques in Urban Planning and Geology Kadriye Burcu Yavuz Kumlu Gazi University, Turkey Şule Tüdeş Gazi University, Turkey

ABSTRACT In this paper, Multi Criteria Decision Making (MCDM) processes will be clarified in the context of the disciplines related with the spatial information, as urban planning and its geographical perspective. For this purpose, first Spatial MCDM will be introduced, then the relation between the geographical data and GIS is established. Therefore, following sections include the detailed explanation of three widely used Spatial MCDM techniques, as Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). These techniques will be clarified by giving examples related with urban planning and geological science.

INTRODUCTION The rapid urbanization movement has been continuing in the whole world to reach the development goals. Today, there is lack of precautions in the long term, considering the consequences might appear in the future due to the rapid urbanization movement. Yet, the environmental, social and economic systems comprising the world have been deteriorating. In this regard, disciplines related with the geographical significance has been continuing to increase their significances. Urban planning and geological sciences, geological engineering as a related discipline, constitute the important part of the related disciplines, which have significance effects on the shape of the settlements, where the humankind lives. These disciplines have a vital role in controlling and directing the urbanization movements, by considering the public welfare (Chandio et al., 2012). These controlling and directing process in the rapid urbanization context require the conclusion of a great number of decision making process in its different stages. DOI: 10.4018/978-1-5225-2709-1.ch015

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

In the context of the disciplines related with the geographical context, for instance as urban planning, geology, geography, architecture etc., there are a great number of decision makers (actors or stakeholders) and alternatives exist in any decision making process. Examples related with the decision making processes in relation with the geographically related data could be the site selection of a new settlement or site selection of the hazardous waste area considering its geological suitability etc. (Rahman et al., 2012). Generally, these processes require the consideration of many evaluation criteria (attributes). Actually, rationally, almost all of the decisions are based on one or more criteria. The criteria could be defined as measurable attributes of the alternatives, which are evaluated to reach the final decision in the context of a decision rule. In some cases, decision making process might begin based on a single criterion; however, most of the decision making process relies upon more than one criterion (Eastman, 1999). Within the context of the evaluation process of these criteria, which are considered as inputs; value judgements of the decision makers are significant in the sense of the determination of the relative importance of each criterion. Assuming a certain criterion has different relative importance assigned by each decision maker; on the other hand, some certain criteria have the same importance (e.g. naturally protected areas). After the determination of the relative importance of each criterion, alternatives related with these criteria are developed. In the previous process, criteria were ranked, depending on the relative importance assigned by the decision makers. Hereby, alternatives are also ranked to a reach a specific, defined goal. As in the previous stage, namely the ranking of the criteria, the ranking of the alternatives depends on the subjective value judgements of the decision makers. Geographical Information System (GIS)-based SMCDM (spatial multi criteria decision making) enables decision makers to evaluate criteria, namely attributes having certain kinds of geographical characteristics, and conduct spatial analyses by using the related MCDM (multi criteria decision making) techniques and decide which geographically featured alternative is the best to reach the predefined goal. In the context of SMCDM processes, conventional techniques might cause the loss of data and this circumstance may mislead the decision makers to take appropriate decisions. Computer based GIS prevents the formation of this case and carry out the MCDM analysis by ignoring the loss of data. In addition to these, GIS-based MCDM is an essential approach for the land suitability analyses conducted with regards to geographically related sciences. With this regard, vector data, including polygons, lines and points or raster cells are introduced as geographical units. Additionally, these geographical units compose the map layers, which are also called as the evaluation criteria in the sense of SMCDM. The core of the decision problem is consisted of the integration of the criterion maps and the relative importance of the attributes, namely criterion maps, assigned by the decision makers because relative importance of the attributes constitute the weights of those criteria. This subjective process, that is the weighting of the criteria, constitutes the distinctive characteristic of SMCDM. It could be argued that the ranking procedure related with SMCDM might differ depending on the different conditions because it constitute the subjective judgements of the decision makers and these subjective judgements generate the criteria weights, which are important in the sense of directing the final decision. The circumstances might differ in the case that the decision makers are consisted of a group of people who have different ideas that it is generally not possible to agree upon a single set of weights, but the range of weights that lead to the final decision (Chen et al., 2009). So, problems related with MCDM are interested in the integration of the predefined criteria to generate a simple evaluation index. The usage of GIS-based MCDM is efficient in the case that there is wide range of multi-spatial, multi-temporal and multi-scale criteria. The usage of GIS enables decision makers

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to conduct a time efficient and cost effective MCDM analysis. Within this context, there is a growing interest in GIS-based SMCDM analyses (Chen et al., 2010). To sum up, GIS-based MCDM could be considered as the integration of the spatial and non-spatial data into a final decision. The decision rule, constructed by the decision makers, in the context of a specific MCDM technique, constitutes a significant relation between the input and the output maps, namely criteria and the best alternative, respectively. The procedures in the sense of related MCDM technique include combination of the geographical data relative importance of the criteria assigned by the decision makers, the data manipulation and the implementation of the specified decision rule. Multi attribute techniques within this context (here, simple additive weighting-SAW, analytic hierarchy process-AHP and The Technique for Order of Preference by Similarity to Ideal Solution-TOPSIS), which are called as the discrete techniques since they argue that the number of alternatives is explicitly given, will be explained in this paper (Jankowski, 1995). In the light of this information, similar to urban planning, other disciplines as geology, related with the “space” requires decision making, involving a great number of alternatives based on some certain criteria and decision makers, take advantage of MCDM. MCDM, in the simplest term, makes easier the ranking of alternatives based on relative importance developed from value judgements of decision makers. Hereby, the most important part of MCDM could be stated as ranking alternatives, since ranking alternatives plays a crucial role in determining which of them are relatively important in the decision making process. Thus, the order of importance among alternatives formalizes the final decision.

BACKGROUND In this part, background information related with the geographical data, its relationship with GIS and map algebra will be explained in detail in order to provide necessary layout to Spatial Multi criteria Decision Making (SMCMD) analyses.

Geographical Data Geographical data, also known as spatial data, includes positions, attributes and relationships of features in space (Morrison, 1995). A data is called as “geographical” in the case the data is related with a location or a place and it can be organized as two tabular forms, called as geographical data matrix and spatial behavior data matrix. The rows of the geographical data matrix indicate geographical entities or units or observations. On the other hand, attributes indicate certain kind of property, distinguishing a geographical entity from another (Malczewski, 1999). The logic behind the geographical data matrix was defined by Berry (1964). Berry states that a single characteristic belonging to a single place could be called as “geographic fact” and it is generally one of a set of observations, either could be the same characteristic at a series of places, or a series of characteristics in the same spatial unit (1964). The geographical data matrix could be illustrated as follows: The values in the rows indicate certain characteristic of a geographical entity, defined by units. For instance, the value of Entity1 regarding to Attribute1, representing as x 11 asserts the certain characteristic of geographical entity, called as Entity1. These values, indicated in the rows, change for the each of entities. Therefore, it could be stated that each values in the rows might be different (or the same) for

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 1. The geographical data matrix (Malczewski, 1999) Attribute1

Attribute2

Entity1

x

11

x

12

Entity2

x

21

x

22





x

Entitym



x

m1



Attributen



x

1n



x

2n



m2





x

mn

each of the geographical entities, since these entities show different geographical attributes. A hypothetic real world example might be as follows for three neighborhoods in Ankara. In the Table 2, a real world example of a geographical data matrix for the three neighborhoods of Ankara, namely Bahçelievler, Emek and Anıttepe, is generated for each neighborhoods’ land value, household income, population density and liquefaction values for the purpose of earthquake resistance neighborhood design. The second type of tabular form for the geographical entities is called as spatial behavior data matrix. This matrix shows the flow of various attributes from one geographical entity to another and illustrated as follows. A real world example for the spatial behavior data matrix could be given as a part of the matrix developed by Guo (2009) as follows. In the Table 4, A-B-C-D-E-F refers to certain geographical units, defined by coordinates. In other words, they represent certain places (might be certain neighborhoods, districts, cities etc.) and the table indicates various flows from one place to another. For example, it is stated that 5 people from 0-5 age group, 28 people from 6-10 age group, 55 people having less than 10k income and 68 people having between 10-20k income get through from A to B. Briefly stated, a “geographical” data refers to a certain place which has definite coordinates and could be transformed to two kinds of tabular data matrixes, called as geographical data matrix and spatial behavior data matrix. So that, attributes related with the space could be attached to the related Table 2. A real world example of a geographical data matrix for the three neighborhoods of Ankara Attribute1 Land value ($)

Attribute2

Attribute3

Attribute4

Household income ($)

Population density (persons per hectar)

Liquefaction values

Entity1

Bahçelievler

x

11

x

12

x

13

x

14

Entity2

Emek

x

21

x

22

x

23

x

24

Entity3

Anıttepe

x

31

x

32

x

33

x

34

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 3. The spatial behavior data matrix (Malczewski, 1999) Entity1 Entity1

x

11

x

12

Entity2

x

21

x

22





x

Entitym



Entity2



x

m1

Entitym



x

1n



x

2n







m2

x

mn

Table 4. A real world example of a spatial behavior data matrix (Guo, 2009) From

To

Age (0-5)

Age (6-10)

Income30

1

>2000

4

1000-2000

3

500-1000

2

0-500

1

0-500

3

500-1000

2

>1000

1

>3000

4

2000-3000

3

1000-2000

2

0-1000

1

>1000

3

100-1000

2

0-100

1

Table 8. Main criteria and their assigned standardized weights (Khanlari et al., 2012) Main criteria

Weights

Geomorphology

8

Geology

8

Slope

7

Stream

6

Distance from road

4

Distance from settlement

5

Distance from fault

2

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

the related alternatives. Since the core of the decision making process includes the relative importance assigned to the attributes by the decision makers, attributes have a vital role in determining the output (here is the best alternative). The attributes here constitute each criterion map in the GIS environment. Criteria maps could be conceived as input maps, which have direct effect on the selection of the best alternative. After deciding the attributes, which constitute criteria maps, these maps are standardized in order to make them comparable. The following step enables decision makers to array attributes, namely criteria maps, depending on their relative importance. After arraying the attributes by their significance level, weights are assigned, based on the related significance level, by the decision makers. Accordingly, each criterion map is multiplied by the related weight value and as a result, weighted standardized criterion maps are generated. Then, overlay function in the context of GIS is used to obtain the final scores for each alternative. After the determination of the final scores for each alternative, the alternatives are ranked and the alternative which has the highest value is selected as the best alternative.

Analytic Hierarchy Process (AHP) AHP was developed by Saaty (1980). It is stated that in the decision making problems (in the context of taking a decision, as deciding where is the most geologically suitable place for a new settlement to locate on), decision makers should generate some organization to take the most appropriate decision and this is possible in the sense of a hierarchical representation. The relative importance of decision makers which constitute their judgements and the measurements in the decision making process should be integrated and AHP is the proper way to provide these requirements. The basis of the mathematical thinking beyond AHP is based on linear algebra and this technique differs from other techniques in the sense of scientific measurement, namely pairwise comparison (Saaty, 1988). Saaty (1988) summarizes the AHP in the context of eight uses. In this sense, AHP enables decision makers to: 1. 2. 3. 4. 5. 6. 7. 8.

Generate a model for a complex decision making problem Determine priorities and select among the defined alternatives Measure consistency Predict Generate a cost / benefit analysis Constitute a design, which includes forward and backward planning Test conflict resolution Determine for the resource allocation in the context of the cost / benefit analysis

In another study, Saaty (2008) decomposes the decision making process and the whole process of AHP as: 1. Problem definition and the determination of the type of knowledge 2. Creation of the decision making hierarchy with the desired goal agreed upon by the all decision makers, followed by the objectives, which are required to reach the desired goal. Accordingly, designation of the criteria which are linked with the related alternatives.

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

3. Construction of the pairwise comparison matrices. Comparison of each attribute with the other attributes, based on the judgements of the decision makers, which constitute relative importance of the attributes. 4. Weighting each of the attributes, depending on their relative importance assigned by the decision makers. Accordingly, the determination of the overall weightings, which enables decision makers to find the best alternative. In the light of this information, the most significant characteristic of AHP could be stated as the pairwise comparison. For the purpose of making pairwise comparison, a scale of 1-9 was defined. A number between 1 and 9 is assigned to each attribute by the decision makers. The assignment process depends on the judgements of the decision makers, which generates the relative importance of the attributes. The fundamental scale for the pairwise comparison could be illustrated as in the Table 9 (Saaty, 1990). In this context, the mathematical explanation of AHP includes first the construction of the pairwise comparison matrix A . Matrix A is generated, depending on the values indicated in the fundamental scale for the pairwise comparison (see Table 10). The matrix is composed of m × m elements, which actually are the criteria. Therefore, m reflects the number of criteria, which are compared. The values, assuming any value in any cell is represented as a ij , in the cells of the matrix indicate the significance of the i th criterion over j the j

th

th

criterion. If aij > 1 , then it means that i

criterion. In the same way, if aij < 1 , then it means that

th

i

th

th

criterion is more significant than

criterion is less significant than the

j criterion. In the case aij = 1, it means that the two criteria has the equal significance. Additionally, the two sides of the diagonal line of the matrix A have values which their multiplication is equal to 1. An example of a pairwise comparison matrix is illustrated in the Table 8. In this example, there are four Table 9. The fundamental scale for the pairwise comparison (Saaty, 1990) Intensity of importance on an absolute scale

Definition

Explanation

1

Equal importance

Two attributes contribute equally to the objective

3

Moderate importance of one over another

Experience and judgment strongly favor one activity over another

5

Essential or strong importance

Experience and judgement strongly favor one activity over another

7

Very strong importance

An activity is strongly favored and its dominance demonstrated in practice

9

Extreme importance

The evidence favoring one activity over another is of tile highest possible order of affirmation

2, 4, 6, 8

Intermediate values between the two adjacent judgements

When compromise is needed

Reciprocals Rationals

550

If activity

i

has one of the above numbers assigned to it when compared with activity

has the reciprocal value when compared with Ratios arising from the scale

i

j , then j

If consistency were to be forced by obtaining n numerical values to span the matrix

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 10. An example of a pairwise comparison matrix Attributes

A

A1

a

1

A

2

11

=1

a

21

A2

22

A4

1 a21

a13 =

1 a 31

a 14 =

=1

a 23 =

1 a 32

a 24 =

=1

1 a 43

a12 =

a

A3

A

3

a

31

a

32

A

4

a

41

a

42

a

33

a

43

a

44

1 a 41 1 a 42

=1

attributes, namely criteria. Each attribute is compared with themselves and other attributes, depending on the values indicating in the fundamental scale for the pairwise comparison (see Table 9). As seen in the Table 10, when comparing the attributes with themselves (for the cells ofa11 , a22 , a 33 and a 44 ), the cells get the value of 1. On the other hand, the multiplication of the values exist on the two sides of the diagonal line is equal to 1. For example, a21 × a12 × a 31 × a13 and a 41 × a14 is equal to 1. After the construction of matrix A , the values in this matrix should be normalized to enable decision makers to compare them. Therefore, the normalized pairwise matrix B is generated. This matrix is generated by making the sum of the values exist on each column equal to 1. Assuming the values in the cells of the matrix is bij , therefore, the matrix B is generated as:

b

=

ij

a ∑a



ij

m

i =1

(5)

ij

After calculating the matrix B , namely normalized pairwise matrix, the matrix which is called as criteria weight vector W is calculated. This matrix is calculated by taking averages of the each row of the B matrix and also known as the priority matrix. This matrix is consisted of a single column, as seen in the equation (7). Therefore, the calculation of the matrix W is illustrated as: m

w

i

=

∑b i =1

m

il



(6)

Criteria weight vector, matrix W is generated as:

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

  w 1    W =  w 2       w m 

(7)

For the following step, relative importance by percent (%) of the alternatives linked with the related criteria is determined. Namely, the relative importance of the alternatives is determined as percent units in this stage. The newly developed matrix D is composed of a single column and the number of the rows is equal to the number of alternatives (n ) , including the percent relative importance values for each alternative in the context of a single specific criteria / attribute. The matrix D could be illustrated as:

D

i

  d 11    = d 21       d n 1

(8)

Therefore, the final relative importance matrix E for the all alternatives linked with the related all criteria could be illustrated as below:  d 11  E = d 21    d n 1

d d

12 22



d

n2

   2m      d nm     

d d

1m

(9)

The following step includes the multiplication of the matrix E and the criteria weight vector, also known as priority matrix B . Therefore, the final result, which includes the percent distribution of the relative importance of the alternatives is calculated. Therefore, the final decision matrix F is generated as:    f 11    f  F =  21          f n 1  

(10)

However, the whole AHP procedure is not finish, yet. The check of consistency stage should be implemented to verify the pairwise comparison process. Accordingly, after computing the criteria weight vector B , the AHP requires the consistency check of these weights in order to determine whether the

552

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

comparison between the criteria is consistent or not. Accordingly, consistency ratio (CR) is used to determine the consistency. In this sense, the basis of the CR is consisted of the relation between the criteria number and reciprocal value (λ). In order to compute λ, the pairwise matrix A and criteria weight vector W are multiplied. The result matrix, here, is called as the matrix C . After computing the values in the matrix C , the reciprocal value is calculated as:

∑c w λ= m

i

i =1

i

m



(11)

After the calculation of λ, it is easy to determine the consistency index(CI ) , which is required for the calculation of theCR . CI is calculated as: CI =

λ −n n −1

(12)

The last step to reach the value of CR includes CI and random index (RI ) . The values of RI differ with the criteria number. RI values, developed by Saaty (1980), depending on the number of criteria are illustrated below: Depending on the RI values indicating in the Table 9, the consistency ratio (CR) is calculated as: CR =

CI RI

(13)

If CR > 0.1 it means that the pairwise comparison is inconsistent. On the other hand, ifCR < 0.1 , it means that the pairwise comparison is consistent (or slightly inconsistent) and if C = 1 , it means that the pairwise comparison is perfectly consistent (Saaty, 1980). As in the case of SAW, the AHP procedure could be simply implemented in the GIS environment. First, the hierarchical structure of MCDM for AHP procedure should be constructed. That is, the common goal specified by the decision makers should be defined. Then the objectives related with the defined goal should be determined. Then in the following step, attributes, in other word criteria, should be identified in order to reach the predefined objectives. In the context of AHP, the defined attributes by the decision makers constitute each criterion map. It is significant that the criteria maps should be transformed into a single unit to compare with themselves. Therefore, these maps are standardized to enable decision makers to compare them. At the same time, pairwise comparison matrix, which composes the Table 11. The values of RI with the related criteria number (Saaty, 1980) m

2

3

4

5

6

7

8

9

10

RI

0

0.58

0.90

1.12

1.24

1.32

1.41

1.45

1.51

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

basis of AHP, should be formed to compare each criterion with the other criteria and with themselves. Based on the ranking procedure (1 to 9 scale), weights, which indicate the relative importance of each criterion, are assigned to each criterion by the decision makers. Then, the assigned weights are multiplied by each criterion map. Therefore, weighted standardized criterion maps are generated. In the following stage, the weighted standardized criterion maps are combined in the GIS environment, with the help of overlay operations. The result constitutes the output map. The final output map should be standardized to distinguish the ranking of alternatives in a normalized scale of 1, 100 etc. Within the AHP or after obtaining the output map, the consistency check procedure should be followed to control whether the comparison process in the context of pairwise comparison is implemented in a consistent way or not. In this stage, the value of CR , an index used for to check consistency in the AHP, is checked to determine the consistency. If the related CR value is in the range of consistency, namely if it is less than 0.1, then it could be stated that the pairwise comparison was implemented in a consistent way. Here, there is an example of an AHP procedure, based on the study of Siddiqui et al. (1996), related with the subject of landfill siting. In this study, Siddiqui et al. (1996) defines the AHP hierarchy as illustrated in the Figure 10, below: As seen in the Figure 11, the top of the AHP hierarchy is consisted of the goal. Therefore, the goal in this example is the landfill suitability. Then, the main criteria (attributes) are defined, as hydrogeology / geology, land use and proximity. On the other hand, the sub criteria are defined as depth to water table, depth to bedrock, permeability, slope and texture for the hydrogeology / geology criterion; agricultural land and forest land for the land use criterion and Moore proximity, Norman proximity and Noble proximity for the proximity criterion. Accordingly, sub-sub criteria are defined, for instance, as 0-10 km zone, 10-20 km zone and >20 km zone for Moore, Norman and Noble proximity sub criteria. For the following step, Siddiqui et al. (1996) generates pairwise comparison matrixes for each element including in the AHP hierarchy (from top to bottom), including criteria, sub criteria and sub-sub criteria. The pairwise comparison matrix for sub criteria and sub-sub criteria, as well as their related Eigen values and weights are illustrated in the Table 12 (Siddiqui et al. (1996). As seen in the Table 12, in the context of population centers (constituting sub criteria), Norman proximity was assigned to the highest weight, depending on the relative importance assigned by the decision

Figure 11. AHP hierarchy defined for landfill suitability by Siddiqui et al. (1996)

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 12. The pairwise comparison matrix for sub criteria and sub-sub criteria (Siddiqui et al., 1996) Pairwise comparison Criterion 1 Criterions

Criterion 2

Criterion 3

Population centers (sub criteria)

Norman Moore Noble

Eigen values

Weights

9

3.00

0.676

1

5

1.18

0.265

1/5

1

0.28

0.062

Norman

Moore

Noble

1

3

1/3 1/9

City proximity (sub-sub criteria) 0-10 km

0-10 km

10-20 km

>20 km

3.55

0.735

1

5

9

1.00

0.207

10-20 km

1/5

1

5

0.28

0.058

>20 km

1/9

1/5

1

3.55

0.735

Land use type (sub criteria) Agricultural land Agricultural land Forest land

Forest land

1

5

2.23

0.833

1/5

1

0.44

0.166

Soil limitation (sub-sub criteria) Without limitation Without limitation With limitation

With limitation

1

7

2.65

0.87

1/7

1

0.38

0.13

makers. Norman proximity is followed by Moore and Noble proximity, respectively. The same logic is valid for the other criteria. Similarly, the pairwise comparison matrix for the sub criteria related with the soil attributes related with hydrogeology / geology is illustrated in the Table 13 (Siddiqui et al. (1996). As seen in the Table 13, slope has the highest weight assigned by the decision matrix in the context of hydrogeology / geology sub criteria. Slope is followed by the texture, permeability, depth to bedrock and depth to water table, respectively. Table 13. The pairwise comparison matrix for the sub criteria related with the soil attributes in the context of hydrogeology / geology sub criteria (Siddiqui et al., 1996) Pairwise comparison Slope

Texture

Permeability

Depth to bedrock

Depth to water table

Eigen values

Weights

1

3

5

7

7

2.38

0.48

Texture

1/3

1

5

7

7

1.59

0.31

Permeability

1/5

1/5

1

5

5

0.72

0.13

Depth to bedrock

1/7

1/7

1/5

1

1

0.28

0.04

Depth to water table

1/7

1/7

1/5

1

1

0.28

0.04

Criterions Slope

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

For the following parts, Siddiqui et al. (1996) has composed a framework, depending on three scenarios. The first scenario assumes the case of soil > proximity > land use, the second one assumes proximity > soil > land use and the last assumes the case of land use > proximity > soil. Depending on these assumptions, Siddiqui et al. (1996) created the pairwise comparison matrix for the main criteria (attributes), including soil, land use and proximity in the Table 14. Therefore, weights, based on the relative importance of each criterion, sub criterion and sub-sub criterion, are assigned by the decision makers and this process was implemented by constructing the related pairwise comparison matrixes (see Table 12-14). On an individual basis, the last pairwise comparison matrix was constructed by considering three scenarios, in which main criteria (attributes) as hydrogeology / geology (here it is stated as soil), land use and proximity have different significance over another. The following stage includes overlay operations in the GIS environment. In this sense, overlay operations include the multiplication of determined weights with the related criterion maps. After the multiplication of the weights with the related criterion maps, three weighted (and standardized previously) criterion maps for hydrogeology / geology, land use and proximity were generated. The final step includes the combination of these three criterion maps to generate a single output map. Accordingly, generated output map is also standardized to recognize the places, ranging from most suitable to least suitable for landfill suitability. In this example, all of the pixels which constitute the output map composes the alternatives and these alternatives differs from most suitable to least suitable. For the similar practices as in this example, next attempts after the generation of the output map, including alternatives, might contain different selection processes for the alternatives, depending on the local characteristics, urban and public policies or the value judgements of the related decision makers. In this stage, the usage of AHP is important in the sense that it enables decision makers to help to take consistent and appropriate decisions. The following hypothetic example clarifies this situation: Table 14. The pairwise comparison matrix for the main three criteria, in the context of three scenarios (Siddiqui et al., 1996) Pairwise comparison Criterions

Soil

Land use

Proximity

Eigen values

Weights

Scenario 1: soil > proximity > land use Soil

1

5

7

3.27

0.71

Land use

1/5

1

5

1.00

0.22

Proximity

1/7

1/5

1

0.31

0.07

Scenario 2: proximity > soil > land use Soil

1

5

1/5

1.00

0.22

Land use

1/5

1

1/7

0.31

0.07

Proximity

5

7

1

3.27

0.71

Scenario 3: land use > proximity > soil Soil

1

1/7

1/5

0.31

0.07

Land use

7

1

5

3.27

0.71

Proximity

5

1/5

1

1.00

0.22

556

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

A Hypothetic AHP Example Assuming, decision makers are analyzing the most suitable land in the sense of earthquake resistant settlements. There are three main criteria, as liquefaction, soil type and slope. Depending on the value judgements of the decision makers; liquefaction is more significant than the soil type and the soil type is more significant than the slope in the context of earthquake resilience. Therefore, decision makers construct a pairwise comparison matrix A as (Table 15): Then, the summation of the each values in the related columns are calculated and the each cell values are divided to the related summation value for the normalization process, as stated in the Table 16 below: The next step includes the following calculations in the Table 17: In the following step, each values in the rows are added and the calculated values are divided to the criteria number (in this example, 3). The result calculation indicates the final decision. In this context, the highest value demonstrates the most significant criterion, which has the highest impact on the result (liquefaction (having value of 0.68), in this example).     2.03 0.68 1    W = ×  0.71 = 0.24 3     0.25 0.08

(14)

In the next stage, consistency index and ratio are calculated in order to check whether the decision makers assign consistent values to the each criterion. For this purpose, first of all, λ max is calculated. Table 15. Pairwise comparison matrix A ATTRIBUTES

Liquefaction

Soil Type

Slope

Liquefaction

1

3

8

Soil Type

1 3

1

3

Slope

1 8

1 3

1

Table 16. Normalized pairwise matrix B ATTRIBUTES

Liquefaction

Soil Type

Slope

1

3

8

Soil Type

0.33

1

3

Slope

0.13

0.33

1

SUM

35 ∼ 1.46 24

13 ∼ 4.33 3

12

Liquefaction

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 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 17. Following calculations ATTRIBUTES

Liquefaction

Soil Type

Slope

Liquefaction

1 ∼ 0.68 1.46

3 ∼ 0.69 4.33

8 ~ 0.66 12

Soil Type

0.33 ∼ 0.23 1.46

1 ∼ 0.23 4.33

3 ∼ 0.25 12

Slope

0.13 ∼ 0.09 1.46

0.33 ∼ 0.08 4.33

1 ∼ 0.08 12

λ

= (1.46 × 0.68) + (4.33 × 0.24) + (12 × 0.08) =∼ 3

max

After CI =

λ

max

(15)

is calculated, CI and CR values are calculated as:

3−3 = 0 3 −1

(16)

Therefore CR = 0 (see equation 13) Since CR < 0.1 , it could be stated that the pairwise comparison made by decision makers is consistent.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) TOPSIS was developed by Hwang and Yoon (1981). The main logic of TOPSIS depends on the fact that the best alternative chosen should be located in the shortest distance from the ideal solution, as well as it should be located in the farthest distance from the negative ideal solution. Assuming, each criterion, namely attribute, has ability of increasing or decreasing in a monotone. Then, it would be easy to locate the ideal solution, which has all of the best attribute values, as well as, negative ideal solution, which has the worst attribute values. The attitude here is taking the alternative with the weighted minimum Euclidean distance to the ideal solution by means of a geometrical approach. In this sense, it is stated that this alternative should be located in the farthest direction from the negative ideal solution. Occasionally, the alternative with the minimum Euclidean distance from the ideal solution might be located on the shorter distances to the negative ideal solution, comparing it with the other alternatives. Therefore, the TOPSIS approach takes into account of the both kinds of distances from the ideal and the negative ideal solutions by considering the relative closeness to the ideal solution (Hwang and Yoon, 1981). The logic of the TOPSIS is explained in a list by Olson (2004), as: 1. TOPSIS is implemented for the data, including n alternatives over k criteria. The raw data is generally standardized into a single unit to conduct analysis, as from x ij to the standardized measure of sij . 558

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

2. In the decision making process, a set of importance weights wk are defined for the each criterion. The basis of the importance weights might rely on anything, which the decision makers decide. 3. One of the most significant part of the TOPSIS is consisted of the identification of the ideal alternative, s + . 4. In addition to the ideal alternative, the negative ideal alternative should be identified, s − . 5. After the determination of both ideal and negative ideal alternatives, a distance measure should be identified from over each criterion to the ideal D + and negative ideal D − . 6. The following step includes the determination of a ratio R , which has the equal distance to the negative ideal divided by the summation of the distance to the negative ideal and the positive ideal. 7. The last step contains the rank order of the alternatives by maximizing the ratio, explained in the 6th step. Therefore, it could be argued that TOPSIS tries to maximize the distance to the negative ideal, while minimize the distance to the positive ideal (Olson, 2004) and due to its simplicity, computational efficiency and capability of measuring the alternatives’ relative performances in a simple algebraic manner, TOPSIS is widely used in the decision making processes in the context of MCDM (Yeh, 2002). In this context, the mathematical process in TOPSIS begins with the construction of the decision matrix, which has m alternatives related with n attributes, namely criteria (Hwang and Yoon, 1981).  A  x A  x 1

11

2

21

    D= Ai  x i1     Am x m1

x x

12 22

… …





x

i2



x

m2



… …

x x

1j 2j



x

ij

x

mj



   2n      x in     x mn 

x x

1n

(17)

Where Ai is the i th alternative and x ij is the numerical outcome of the i th alternative related with the j th criterion. After the generation of the decision matrix, the next step involves the construction of the normalized decision matrix. The construction of the normalized decision matrix is required in order to transform the units of different attributes which have different measures. The normalized decision matrix is calculated as (Hwang and Yoon, 1981):

r

ij

=

x ∑x ij

m

i =1

2



(18)

ij

After the construction of the normalized decision matrix, the weighted normalized decision matrix is generated. This matrix is generated by multiplying the each column of the matrix R with their as-

559

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

sociated weights. Therefore, the weighted normalized decision matrix V is calculated as (Hwang and Yoon, 1981):   w 1r 11 w 1r 12      V =  w 1r i 1 w 2r i 2      w 1r m 1 w 2r m 2

… w j r ij … w nr 1n  …     … w j r ij  w r r in   …      … w j r mj … w nr mn  

(19)

The next step involves the most significant part of the TOPSIS. In this sense, this step is called as the determination of the ideal and negative ideal solutions. Here, the ideal solution is illustrated as −

on the other hand, the negative ideal solution is illustrated as

A

+

A

,

. Therefore, the calculations related

with the ideal and negative ideal solutions are explained as below (Hwang and Yoon, 1981): +

A

*

A

 =  max v ij j ∈ J  i

(

 =  min v ij j ∈ J  i

(

), min v i

), max v i

ij

ij



j∈

j  i = 1, 2, ..., m  = {v ,v ,...,v ,...,v

j∈

j  i = 1, 2,..., m  = {v ,v 

'





'





}

(20)

}

(21)

*

*

*

*

1

2

j

n









1

,..., v j ,..., v n 2

}

{

where J = j = 1, 2,..., n j associated with benefit criteria

J

'

}

{

= J = 1, 2,..., n j associated with the cost criteria +



Here, A and A indicates the most desired and least desired alternatives, namely ideal and negative ideal solutions, respectively. Then, the next step includes the determination of the separation measure. The separation measure between each alternative could be calculated by using Euclidean distance. The separation measure between the alternatives is calculated as (Hwang and Yoon, 1981):

si

+

560

=

n

∑ j =1

(

)

2

* v ij −v j , i = 1, 2,..., m

(22)

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

si



=

n

∑ j =1

s

+ i

(

)

2

− v ij −v j , i = 1, 2,..., m

(23)

shows the separation of each alternative from the ideal solution, on the other hand, si− shows

the separation of each alternative from the negative ideal solution. The following step is the calculation of the relative closeness to the ideal solution. Thus, the relative closeness of Ai regarding to A+ is calculated as (Hwang and Yoon, 1981):

ci

+

=

(s

si i

*



+ s i −)

, 0 < c * < 1, i = 1, 2,..., m i

(24)

It could be stated that an alternative is closer to A+ in the case of ci+ approaches to 1. The following and the last step is consisted of the ranking of the alternatives. Now the alternatives could be ranked depending on the decreasing sequence of ci+ (Hwang and Yoon, 1981). Most of the GIS-related with the special interest on space and place, studies related with the usage of TOPSIS is implemented under the title of as AHP-TOPSIS technique. These studies use AHP as an input which constitutes the basis of TOPSIS procedure. To make it clear, AHP is used to determine the weights of the criteria. One of these studies was implemented by Pazand and Hezarkhani (2014). As Pazand and Hezarkhani (2014) stated, the AHP is used in order to determine the weights based on the relative significances of the criteria. After the determination of the weights of the criteria in the context of the AHP procedure, these weights provide input for TOPSIS procedure, which is used for the ranking and selection process. In their study, Pazand and Hezarkhani (2014) seeks for the potential area for Porphyry Cu potential and in order to reach the goal, Pazand and Hezarkhani (2014) generated a schema, which shows the general direction of the study. Based on the related schema, it is stated that the weights of the criteria were assigned via AHP. After the determination of the criteria weights, TOPSIS procedure was started. In the context of the TOPSIS procedure, Pazand and Hezarkhani (2014) evaluated the alternatives, as well as determined the final ranking and obtained areas for Porphyry Cu potential. As stated, TOPSIS could also be easily applied in the context of GIS environment. As is the case with SAW and AHP, the core of any MCDM process begins with the construction of MCDM hierarchy. It is also valid in TOPSIS. First, a common goal is defined by the decision makers at the top of the hierarchy. Then decision makers compose objectives related with the common goal. The objectives could be considered as intermediary steps to reach the goal. After the determination of the objectives, attributes related with the objectives are generated. The setting of the attributes is especially important in the sense that they have the highest impact on deciding the best alternative. The next step in the GIS-based TOPSIS process includes the construction of the decision matrix, depending on the attributes defined in the hierarchy making process explained above and then this decision matrix is normalized in order to enable the attributes having the same measurement units. After that, each column in the normalized decision matrix is multiplied with their related weights assigned by the decision makers to obtain the weighted normalized decision matrix. Following step includes the calculations related with to find the ideal and

561

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

negative ideal solutions. TOPSIS depends on the idea that the best alternative should be located in the shortest distance from the ideal solution and farthest distance from the negative ideal solution. Therefore, the following stage includes the calculation of the separation measures. There are two types of separation measure. The first type constitutes the separation of each alternative to the ideal solution, on the other hand, the second type constitutes the separation of each alternative to the negative ideal solution. Depending on the distance of each alternative to the ideal and negative ideal solutions, alternatives are ranked according to the value of ci+ .

A Hypothetic TOPSIS Example Assume that there are three candidate areas to construct new project. Decision makers want to evaluate those three areas depending on four criteria, as cost suitability, slope suitability, geological suitability and closeness to the center. The weights of those criteria are assigned as 20%, 15%, 40% and 25% relatively. Decision makers, first, assign values to those areas, considering the related criteria is illustrated in the Table 18. Then, square of each value exists on the decision matrix is calculated and those calculated values in the each column are added, as shown in the Table 19. The following step includes the calculation of r values (see equation 15), illustrated in the Table 20: After the calculation of the r values, weighted normalized decision matrix V is calculated as (Table 21). The maximum and the minimum values of the each column in the weighted normalized decision matrix are calculated as (Table 22). After finding the maximum and the minimum values, separation measures are calculated (Table 23 and Table 24). Table 18. Decision matrix CRITERIA 1st Area

Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

10

6

8

4

2 Area

8

4

2

8

3rd Area

6

2

10

6

20%

15%

40%

25%

Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

1st Area

102

62

82

42

2nd Area

82

42

22

82

3rd Area

62

22

102

62

200

56

168

116

nd

WEIGHTS

Table 19. Second step of TOPSIS procedure CRITERIA

SUM

562

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 20. Calculation of the r values Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

1st Area

0.71

0.8

0.62

0.37

2 Area

0.57

0.53

0.15

0.74

3 Area

0.42

0.27

0.77

0.56

Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

1st Area

0.71 × 0.2 = 0.1414

0.8 × 0.15 = 0.1203

0.62 × 0.4 = 0.2469

0.37 × 0.25 = 0.0928

2nd Area

0.57 × 0.2 = 0.1131

0.53 × 0.15 = 0.0802

0.15 × 0.4 = 0.0617

0.74 × 0.25 = 0.1857

CRITERIA

nd rd

Table 21. Weighted normalized decision matrix CRITERIA

3rd Area

Table 22. Finding minimum and maximum values Maximum values

0.1414

0.1203

0.3086

0.1857

Minimum values

0.0849

0.0401

0.0617

0.0928

Table 23. Calculation of separation measures (for Smin) Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

SUM

Smin

1st Area

0.0032

0.0064

0.0343

0

0.04391

0.209557

2 Area

0.0008

0.0016

0

0.0086

0.01103

0.105013

3rd Area

0

0

0.061

0.0022

0.06311

0.251212

SUM

Smax

CRITERIA

nd

Table 24. Calculation of separation measures (for Smax) CRITERIA

Cost Suitability

Slope Suitability

Geological Suitability

Closeness to The Center

1st Area

0

0

0.0038

0.0086

0.01243

0.111491

2nd Area

0.0008

0.0016

0.061

0

0.06336

0.251713

3rd Area

0.0032

0.0064

0

0.0022

0.01178

0.108553

After calculation of the separation values, the ideal solution is calculated by using equation (21), illustrated in the Table 25. As stated in the Table 25, the ideal solution is determined as the 3rd area. In other words, 3rd area is the best choice to construct new project on it. 563

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

Table 25. Determination of the ideal solution CRITERIA 1 Area st

RESULT 0.652729

2 Area

0.294381

3rd Area

0.698267

nd

All of these procedures apart from the construction of the MCDM hierarchy could be implemented by algebraic operations in the GIS environment. As stated previously in the context of SAW and AHP, the TOPSIS procedure also is ended with the visualization process in the GIS environment. Visualization capability of GIS is significant in the sense that spatial MCDM processes should be notified as visual because the related alternatives are in the form of pixels. All of the pixels in the output map constitute the alternatives for decision making process. Therefore, decision makers consider the areas formed by the related pixels as alternatives and take decisions in the spatial context in the context of spatial MCDM.

FUTURE RESEARCH DIRECTIONS Following research directions in the future depend on the improvements achieved in the common spatial database and remote sensing technology. Currently, studies related with the remote sensing technology is efficient, especially in the sense that it enables decision makers to evaluate the spatial change in the course of time. It is significant, since it provides the information about how the future should not be. Besides, formation of the common spatial database could not still be achieved in the worldwide. This situation causes the loss of time, since the spatial layout should be created before each of the spatial decision making process. Therefore, formation of a common spatial database in the worldwide is significant in the sense that it enables the decision makers to take decisions related with the space, as well as it enables the related researches to conduct spatial analyses.

CONCLUSION To sum up, GIS enables decision makers to take proper decisions related with the space, providing its ability to manage the spatial data. Especially, spatial analyses, which are conducted in the GIS environment, highly ease the role of decision makers. In this context, MCDM procedures also could be carried out in the GIS context. It is particularly significant to emphasize that performing MCDM activities related with the space in the GIS environment is efficient in the sense that GIS prevents the data loss, which might affect the decision making process negatively. In this paper, most of the used MCDM techniques, namely Simple Additive Weighting (SAW), Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are clarified. Explained two hypothetical examples could be conducted in any GIS software, by using its functions, as map algebra, spatial analyses and spatial statistics.

564

 Multi Criteria Decision Making Techniques in Urban Planning and Geology

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Guo, D. (2009). Flow mapping and multivariate visualization of large spatial interaction data. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1041–1048. doi:10.1109/TVCG.2009.143 PMID:19834170 Hwang, C., & Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications, A State of the Art Survey. New York, NY: Springer. doi:10.1007/978-3-642-48318-9 Hwang, C.-L., & Masud, A. S. (1979). Multiple Objective Decision Making - Methods and Applications A State of the Art Survey (Vol. 1). Jankowski, P. (1995). Integrating geographical information systems and multiple criteria decisionmaking methods. International Journal of Geographical Information Systems, 9(3), 251–273. doi:10.1080/02693799508902036 Jankowski, P., Andrienko, N., & Andrienko, G. (2001). Map-centred exploratory approach to multiple criteria spatial decision making. International Journal of Geographical Information Science, 15(2), 101–127. doi:10.1080/13658810010005525 Khanlari, G., Abdilor, Y., Babazadeh, R., & Mohebi, Y. (2012). Land Fill Site Selection for Municipal Solid Waste Management Using GSI Method. Advances in Environmental Biology, 6(2), 886–894. Maguire, D. (1991). An overview and definition of GIS. Geographical Information Systems: Principles and Applications. Retrieved from http://lidecc.cs.uns.edu.ar/~nbb/ccm/downloads/Literatura/OVERVIEW AND DEFINITION OF GIS.pdf Malczewski, J. (1999). GIS and Multicriteria Decision Analysis. New York: Wiley. Morrison, J. L. (1995). Spatial data quality. In S.C. Guptill & J.L. Morrison (Eds.), Elements of spatial data quality. Oxford: Elsevier. doi:10.1016/B978-0-08-042432-3.50008-2 Noghin, V. D. (2001, July 10-14). What is the Relative Importance of Criteria and how to Use it in MCDM. In M. Köksalan & S. Zionts (Eds.), Multiple Criteria Decision Making in the New Millennium: Proceedings of the Fifteenth International Conference on Multiple Criteria Decision Making (MCDM), Ankara, Turkey (pp. 59–68). Springer. doi:10.1007/978-3-642-56680-6_5 Nyerges, T. L., & Jankowski, P. (2010). Urban and Regional GIS. New York: The Guilford Press. Olson, D. L. (2004). Comparison of weights in TOPSIS models. Mathematical and Computer Modelling, 40(7-8), 721–727. doi:10.1016/j.mcm.2004.10.003 Pazand, K., & Hezarkhani, A. (2014). Porphyry Cu potential area selection using the combine AHP TOPSIS methods: a case study in Siahrud area (NW, Iran). Proceedings of Earth Science Informatics (Opricovic 1998). doi:10.1007/s12145-014-0153-7 Pereira, J. M. C., & Duckstein, L. (2007). A multiple criteria decision-making approach to GIS- based land suitability evaluation. International Journal of Geographical Information Systems, 7(5), 37–41. doi:10.1080/02693799308901971

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Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning - A review. Renewable & Sustainable Energy Reviews, 8(4), 365–381. doi:10.1016/j. rser.2003.12.007 Rahman, M. A., Rusteberg, B., Gogu, R. C., Lobo Ferreira, J. P., & Sauter, M. (2012). A new spatial multi-criteria decision support tool for site selection for implementation of managed aquifer recharge. Journal of Environmental Management, 99, 61–75. doi:10.1016/j.jenvman.2012.01.003 PMID:22322128 Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw-Hill. Saaty, T. L. (1988). What is the Analytic Hierarchy Process? In G. Mitra (Ed.), Greenberg HJ, Lootsma FA, Rijckaert MJ, Zimmermann HJ (co-eds) Mathematical Models for Decision Support. Springer. doi:10.1007/978-3-642-83555-1_5 Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. doi:10.1016/0377-2217(90)90057-I Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83. doi:10.1504/IJSSCI.2008.017590 Sen, P., & Yang, J.-B. (1998). MCDM and the Nature of Decision Making in Design. In Multiple Criteria Decision Support in Engineering Design (pp. 13–20). London: Springer London. http://doi.org/ doi:10.1007/978-1-4471-3020-8_2 Siddiqui, M., Everett, J., & Vieux, B. (1996). Landfill Siting Using Geographic Information Systems: A Demonstration. Journal of Environmental Engineering, 122(6), 515–523. doi:10.1061/(ASCE)07339372(1996)122:6(515) Triantaphyllou, E. (2000). Multi-Criteria Decision Making Methods: A Comparative Study. http://doi. org/10.1007/978-1-4757-3157-6 Yeh, C. H. (2002). A Problem-based Selection of Multi-attribute Decision-making Methods. International Transactions in Operational Research, 9(2), 169–181. doi:10.1111/1475-3995.00348

ADDITIONAL READING Malczewski, J. (1996). A GIS-based approach to multiple criteria group decision-making. International Journal of Geographical Information Systems, 10(8), 955-971. Malczewski, J. (2004). GIS-based land-use suitability analysis: A critical overview. Progress in Planning, 62(1), 3–65. doi:10.1016/j.progress.2003.09.002 Massam, B. H. (1988). Multi-Criteria Decision Making (MCDM) techniques in planning. Progress in Planning, 30, 1–84. doi:10.1016/0305-9006(88)90012-8 Saaty, R. W. (1987). The analytic hierarchy process—what it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. doi:10.1016/0270-0255(87)90473-8

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Ying, X., Zeng, G. M., Chen, G. Q., Tang, L., Wang, K. L., & Huang, D. Y. (2007). Combining AHP with GIS in synthetic evaluation of eco-environment quality-A case study of Hunan Province, China. Ecological Modelling, 209(2–4), 97–109. doi:10.1016/j.ecolmodel.2007.06.007

KEY TERMS AND DEFINITIONS Analytic Hierarchy Process: One of the Multi Criteria Decision Making (MCDM) techniques, which was developed by Saaty (1980). Geographical Information Systems (GIS): Information systems, which are used to manage geographically (spatially) attributed data. Multi Criteria Decision Making (MCDM): A decision-making process, which includes a set of alternatives, as well as criteria, which are used for to evaluate alternatives to choose the best among them. Spatial Multi Criteria Decision Making (SMCDM): A MCDM process, which differs from a classical MCDM process in the sense that it includes a set of spatial alternatives. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS): One of the MCDM techniques, which was developed by Hwang and Yoon (1981).

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Chapter 16

Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin (Turkey) Naciye Nur Özyurt Hacettepe University, Turkey Pınar Avcı Hacettepe University, Turkey Celal Serdar Bayarı Hacettepe University, Turkey

ABSTRACT Land subsidence which is defined as gradual settling or sudden collapse of Earth’s surface, is a geohazard phenomenon that occurs worldwide. Land subsidence occurs in time mainly due to excessive groundwater abstraction. This problem occurs usually in semi-arid regions where the groundwater is the sole source of water. Eliminating the adverse effects of land subsidence requires careful observations on the temporal change of elevation coupled with groundwater flow modeling. In this study, numerical groundwater flow modeling technique is applied to a confined aquifer system in the Konya Subbasin of Konya Closed Basin (KCB), central Anatolia, Turkey. Groundwater head in the KCB has been declining with a rate of about 1m/year since early 1980s. Recent GPS observations reveal subsidence rates of 22 mm/year over the southern part of KCB. MODFLOW numerical groundwater flow model coupled with subsidence (SUB) package is used to simulate the effect of long term groundwater abstraction on the spatial variation of subsidence rates.

DOI: 10.4018/978-1-5225-2709-1.ch016

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

INTRODUCTION The term land subsidence includes both of the processes of slow settlement and sudden collapse of a ground surface. However, in many of the cases, the subsidence is a subtle phenomenon. Both the vertical and horizontal components of the displacement can lead remarkable damages. Generally, it is the groundwater pumping that cause land subsidence in compressible aquifer systems. Such systems typically comprise of basin-fill and unconsolidated alluvial aquifer systems that include both aquifers and aquitards (Galloway and Burbey, 2011). The aquifer-system compaction and the resultant land subsidence is associated with the compaction of the aquitards which may be thick confining units within the aquifer system or may be fine-grained deposits interbedded within an aquifer. According to an assessment by Galloway and Burbey (2011), measured subsidence rates in different locations in the world range from 6 mm/year in Kolkata, India between 1992 -1998 (Chatterjee et al., 2006) and 300 mm/year in Mexico City, Mexico between 2004-2006 (Osmanoglu et al., 2011) and among the 18 sites distributed globally the mean and median subsidence rate are 100 mm/year (+/- 1 sigma standard deviation is 99 mm/year) and 55 mm/year. Either slow or sudden, motion of the ground due to subsidence is a life and property threatening process. Present and potential future hazards have been assessed by computer models which are based on basic relations between groundwater’s head, ground stress, compressibility of groundwater and aquifer skeleton, and the groundwater flow. These models use two different approaches: the first is based on groundwater flow theory (Jacob 1940, 1950) and secondly, the theory of linear poroelasticity (Biot 1941). The groundwater flow theory is a special case of the poroelasticity theory. As Galloway and Burbey (2011) states, both approaches are based on the Principle of Effective Stress (Terzaghi 1923, 1925) and the principal difference between these approaches is the way how the deformation of the skeletal matrix is treated. Conventional groundwater flow theory accounts only for the vertical deformation, whereas poroelasticity theory accounts for the 3-dimensional deformation. Therefore, the poroelasticity theory represents a better relationship between fluid flow and deformation, and is physically more realistic (Galloway and Burbey, 2011). However, the approach based on conventional groundwater flow theory is preferred in studies aiming the regional deformation because it requires much less data as compared to the approach based on poroelasticity theory. On the other hand, the poroelasticity approach is more suitable to address the local-scale deformations like ground ruptures and damaged engineering structures. Numerical models to simulate and predict aquifer-system compaction at regional scale are based on the aquitard-drainage model which describes the relations between fluid pressure, intergranular stress and fluid flow by combining the conventional groundwater flow theory, with the principle of effective stress and the theory of hydrodynamic lag (Terzaghi 1923, 1925). These models have been developed since 1970s as the computers allowed solving large set of partial differential equations. Some of the pioneering models are described in Helm (1972, 1975, 1976), Witherspoon and Freeze (1972), Gambolati and Freeze (1973), Narasimhan and Witherspoon (1977), and Neuman et al. (1982). A comprehensive assessment of the literature using such models are given by Galloway and Burbey (2011) and Galloway and Sneed (2013). Development of 3D groundwater flow model MODFLOW (McDonald and Harbaugh 1984, 1988) by USGS in late 1980s is a milestone in using the modeling techniques in hydrogeological problems. Soon after its initial release modules have been developed to simulate the areal compaction due to drainage of interbeds within aquifers. The Interbed Storage Package, version 1 (IBS1) developed by Leake and Prudic (1991) assumes that heads in interbeds equilibrate with aquifer head changes the 570

 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

same model time step. The module has been widely used (e.g. Hanson and Benedict 1994; Galloway et al. 1998; Nishikawa et al. 2001; Larson et al. 2001; Kasmarek and Strom 2002; Hanson et al. 2003a, b; Leighton and Phillips 2003; Don et al. 2006). Leake (1990) developed the Interbed Storage Package, version 2 (IBS2) to account for the time-dependent (i.e. delayed) aquitard drainage. This module has been used in several land subsidence studies (e.g. Leake 1990, 1991; Wilson and Gorelick 1996). Later on, Hoffmann et al.(2003b) combined the IBS1 and IBS2 modules under the SUB Package developed for MODFLOW-2000 (Harbaugh et al. 2000) and MODFLOW-2005 (Harbaugh 2005). Leake (1991) developed also the IBS3 Package to account for the effect of geostatic head changes on the land subsidence in unconfined and in underlying confines aquifers. This process also applied to MODFLOW-2000 and 2005 via the SUB-WT Package (Leake and Galloway 2007). In the following, MODFLOW SUB Package is used to simulate the aquitard compaction in the Konya subbasin of the Konya Closed Basin where both subtle and sudden land subsidences are common. A simplified scheme of the hydrogeological system includes a confined Neogene aquifer and the low-permeability Plio-Quaternary lake sediments on top. The Neogene aquifer includes lacustrine carbonates in which extreme secondary porosity and permeability development due to karstification and low-permeability interbeds rich in clay and silt. Groundwater obtained from the Neogene aquifer is the principal source of water in the Konya subbasin where extensive agricultural production has been carried our since 1970s. Because annual abstraction is almost about two times of the natural recharge, regional groundwater heads decline with a rate of about 1 meter per year since 1980s. The land subsidence accompanying groundwater over-exploitation is mostly subtle but sudden collapses do also occur with an increasing annual frequency during the last decade/s. The meter/decameter-sized collapse structures (locally called “obruks”) started to become a life and property threatening hazard. Therefore, a comprehensive assessment of regional land subsidence accompanying groundwater abstraction in the Konya subbasin is essential for management of the groundwater and for mitigating the associated hazards. Simulating vertical aquifer-system deformation (compaction) by means of aquitard drainage model appears to be a proven assessment tool at regional scale. Once the compaction prone areas are determined at regional scale, detailed modeling based on 3D linear poroelasticity models can be used to address the local scale hazards like ground ruptures and damaged engineering structures that arise from tensional stresses and strains accompanying groundwater abstraction.

1. STUDY AREA Fundamental properties of the Konya Closed Basin (KCB) is summarized below based on the detailed information given in Bayarı et al. (2009a, b). The KCB is located in central Anatolia (Turkey) and occupies an area of about 50,000-sq. km (Figure 1). This endorheic basin comprises of Konya and Salt Lake subbasins located at the southern and northern parts, respectively (Figure 2). The subbasins from Paleozoic to Neogene formations constitute a roughly east-west trending plateau where numerous obruks are located. This morphologic rise is also called Obruk Plateau. Obruk is an internationally recognized Turkish term for the meter to decameter-sized karst collapse structures (UNESCO, 1972). Formations of obruks are associated with the dissolution of Neogene carbonates by geogenic carbon-dioxide released from the crust and mantle since Miocene. Subsidence due to groundwater head lowering has been assumed to be the primary trigger for the formation of obruks particularly during the last decades.

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

From a morphological point of view, the KCB is an orogenic plateau located to the north of Taurus Mountains Belt extending along the eastern Mediterranean Sea. The plains of the southern and northern subbasins are located roughly at 1100 and 950 m (all elevations are above sea level), respectively. The mean elevation of the terminal Salt Lake in the northern subbasin is 905 m. The elevation along the water divide declines from about 2200 m in the Taurus Mountains to about 1100 m at the most northern part. A semi-arid continental climate characterized with cold-wet winters and hot-dry summers dominate the basin. The northern flank of the Taurus Mountains stays at the rain shadow of the moisture coming over the Mediterranean Sea and receives more precipitation compared to the other parts of the KCB. Lowest precipitation in Turkey is observed as about 200 mm/year near Karapinar town located in the east-central part of the basin. Mean annual precipitation and potential evapotranspiration in the KCB is about 400 mm and 1300 mm, respectively. Accordingly, a steppe-type vegetation dominates the basin. However, groundwater is abundant at almost every part of the KCB so that irrigated agriculture is engaged commonly.

1.1 Geological and Hydrogeological Setup The geology of the KCB is made up of three lithospheric plates, known as Tauride-Anatolide Block (TAB), Sakarya Zone Block (SZB), and Kirsehir Massive Block (KMB) (Bayarı et al. 2009a). The TAB comprises of early Paleozoic to late Mesozoic rocks of clastic (i.e., conglomerate, sandstone, and shale), metamorphic, ophiolitic and marine carbonate origin (i.e., dolomite, dolomitic limestone and limestone) (Okay and Tüysüz 1999). The SZB starts with the Triassic and Jurassic aged clastic sequence of clastic rocks; Jurassic to Cretaceous carbonates, Middle-Late Cretaceous clastics and volcanics, and terminates with Paleogene carbonates and clastics. The KMB is characterized by metamorphics and granitic rocks of Cretaceous age. The TAB and SZB include a similar Paleogene series comprised mainly of clastics (conglomerate, shale, sandstone, and siltstone), marine carbonates, volcano-clastics and laterally discontinuous evaporitic rocks towards the top of sequence. Overlying Oligo-Miocene rocks are represented by continental clastics, tuff, limestone, evaporites, and gypsum-shale alternation. The Neogene in which the land subsidence is common includes late Miocene to late Pliocene aged lithologies. The sequence starts with a basal conglomerate and continues upward with lacustrine carbonates (i.e., limestone and dolomite) that alternate with marl particularly in the upper part. The Plio-Quaternary paleo lake sediments and alluvial fans cover vast areas in both subbasins. A considerable part of the KCB is occupied by volcanic rocks formed during the period between the late Miocene and late Holocene. Among all major geologic units in the KCB, the TAB, SZB and Neogene units represent main aquifers whereas the Paleogene and QPS constitute aquitard systems, respectively (see Figure 1). Well-developed karst is observed in the TAB and Neogene aquifers, respectively. According to the conceptual model of regional groundwater flow, the Taurus Mountains in the south and the Salt Lake in the north constitute the main recharge and discharge areas of the KCB, respectively (see Figure 1, and 2). The recharge from the Taurus Mountains is separated into shallow and deep flow components around the boundary between the mountain foot and the southern plain. The shallow groundwater flows through the Neogene aquifer toward the Salt Lake. The Neogene aquifer is confined in the southern and northern subbasins where it is covered by Plio-quaternary sediments whereas unconfined conditions prevail in the Obruk Plateau (see Figure 2) where it is exposed at the surface. This aquifer contains cool (between 15 and 18.5°C) and fresh (between 440 microS/cm, 25°C and 1,400 microS/cm, 25°C) Ca-HCO3 type groundwater. The Neogene aquifer is very productive so that the groundwater can easily be accessed almost anywhere usually by 572

 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

Figure 1. Location and geological map of the study area

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

Figure 2. Geological cross-section of the modeled aquifer system (Modeled part is shown by red colored rectangle)

means of 50 to 250-m-deep wells. Typical discharge rates range between 10 and 70 l/s. The radiocarbon age of the groundwater in Neogene aquifer ranges from recent at the foot of Taurus Mountains at the south to more than 40,000 years near the Salt Lake at north. On the other hand, the deep groundwater flows through the TAB and SZB aquifers and emerges finally at the Salt Lake. In the late 1960s, the aquifer was almost at pristine state due to the limited number of groundwater abstraction wells. In agreement with the conceptual hydrogeological model of the regional groundwater flow system, the hydraulic head distribution at that time suggests a topography-driven groundwater flow from the Taurus Mountains in the south, toward the Salt Lake in the north (see Figure 2). Hydraulic head decreases from a maximum value of about 1100 m at the mountain flank to about 920 m around the Salt Lake. A slight head increase observed along the Middle Plateau implies the presence of an intermediate recharge in this zone where Neogene and TAB carbonates are exposed. Since the late 1960s, extensive use of groundwater throughout the KCB has caused a head decline rate of 1 m/year. Observations suggest about 30 m drop of water level compared to late the 1980s (Bayarı et al. 2009a).

1.2 Land Subsidence Observations Üstün et al (2010, 2015) conducted a long-term monitoring of the land subsidence at several locations in the Konya subbasin of the KCB based on GPS (Global Positioning System) and DInSAR (Differential Synthetic Aperture Radar Interferometry) data obtained since 2006. Both techniques revealed an apparent land subsidence in the KCB during the study period though, DInSAR observations have a larger uncertainty. Their GPS data collected at several stations indicated a sound linear relationship between the groundwater abstraction rate (expressed by well density) and the vertical ground displacement (i.e. subsidence). Results show that the land subsidence rates observed vary between -5.8 mm/year and -52.2 mm/year depending on the well density within a radius of 5 km from GPS stations. A Pearson’s correlation coefficient value of -0.908 was obtained between the well density and annual subsidence rate data.

2. BACKGROUND The method employed in this study to infer the spatio-temporal land subsidence in the Konya subbasin of the KCB is based on groundwater flow modelling based on MODFLOW-SUB package. In the following, first we provide a concise summary of the associated theory based on the outstanding literature reviews of Galloway and Burbey (2011), Galloway and Sneed (2013). Then, fundamentals of the modular numerical groundwater flow model MODFLOW (McDonald and Harbaugh 1984) and MODFLOW-SUB package (Hoffman et al. 2003) are explained.

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2.1 Aquitard-Drainage Model An integrated assessment of the spatial distributions of compressible aquifer deposits, groundwater head, discharge, recharge and land-surface displacements is essential to establish a reliable conceptual model of groundwater flow and land subsidence. The aquitard drainage model has been widely used in regional land subsidence problems associated with the over-exploitation of groundwater. The approach combines the conventional groundwater flow theory with the two principles of consolidation to establish the relations between fluid pressure, intergranular stress and fluid flow. The two principles of consolidation are i) the principle of effective stress and ii) the theory of hydrodynamic lag (e.g. Terzaghi, 1923; 1925). The following summary is based on Galloway and Sneed (2013) where more information and the literature on the theory of aquitard-drainage model can be obtained.

2.1.1 Principle of effective stress The principle of effective stress which describes the 3D deformation of water-bearing geologic material, can be used to express vertical deformation as well (see Galloway and Burbey, 2011). The theory of hydrodynamic lag adds a time dimension into the deformation process. Principle of effective stress in 3D can be described by the following stress tensors if the solid grains are assumed incompressible (Terzaghi, 1923; 1925): σij ' = σij − δij p

(1)

where p is pore-fluid pressure, σij ' and σij are the effective stress and total stress tensors, respectively. The i and j (for I = 1, 2, 3 and j = 1, 2, 3), represent the Cartesian coordinates x, y and z, respectively. The Kronecker delta ( δ ) is 1 for i = j and is 0 if i ≠ j. Thus, for a Newtonian fluid like groundwater δij = 1 . As inferred from Equation 1, the changes in the total stress and pore-fluid pressure determines the changes in the effective stress (Figure 3). In many cases, the aquitard extends horizontally and their lateral extent is much greater than their thickness so that the changes in pore-fluid pressure gradients within the aquitards will be close to vertical. Therefore, one can further assume that the resulting strains also are close to vertical σzz ' . Then,

( )

the 1D form of equation 1 becomes σzz ' = σzz − p

(2)

where p is the pore-fluid pressure, the σzz ' and σzz are the vertical effective stress and total stress, respectively. The total stress is determined mainly by the load of the overlying saturated and unsaturated sediments. However, tectonic stresses and other factors like glacial loading/unloading, deposition and erosion of sediments etc. may also contribute to the total stress. Moreover, in many cases, the total stress remains constant during the time frame of interest. Therefore, the change in effective stress depends entirely on the change in pore-fluid pressure. That is

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

Figure 3. An idealized aquifer system showing the interface stresses between a confined aquifer and an aquitard (after Galloway and Sneed 2013).

for ∆σzz = 0 , ∆σzz ' − ∆p

(3)

The modeling exercise considered in this paper based on 1D vertical compaction as described in equation 3.

2.1.2 Standard 3D groundwater flow equation The 3D transient saturated groundwater flow in a confined aquifer (e.g. Galloway and Sneed, 2013) can be expressed by the following standard diffusion equation, ∇2h =

576

S ∂h ∂2h ∂2h ∂2h + 2 + 2 −W = s 2 K ∂t ∂z ∂y ∂x

(4)

 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

where h is hydraulic head, Ss is specific storage, K is hydraulic conductivity, W is volumetric flux of sources and (or) sinks of water, and t is time. As pointed out by Galloway and Sneed (2013) equation 2 can be generalized for aquifer systems comprising aquifers and aquitards (e.g. Figure 7) where the parameters Ss and K represent properties of the aquifers, aquitards or the aquifer system. Like the approach in equation 3, the specific storage defined by Jacob (1940) assume 1D vertical deformation only so that change of storage in the aquifer is proportional to the head change through the change in specific storage. Cooper (1966) defines the aquifer specific storage by the following: Ss = γw (α + n βw )

(5)

where α is the vertical skeletal compressibility, βw is the compressibility of water, n is the prosity, γw is the unit weight of water, Ssk is skeletal specific storage and Ssw is the water specific storage. The same equation is expressed by Riley (1969) as follows: Ss = Ssk + Ssw

(6)

where, Ssk = ρw gα and, Ssw = ρw gn βw

( )

The vertical compressibility is related to the unit-compaction and vertical effective stress σzz ' by the following equation:

α=

∆b bo ∆σzz '

where,



(7)

´

∆b = bo −b  is the change in the thickness of a control volume with initial thickness of bo . At this point, ∆σzz'

max

being the previous maximum effective stress (preconsolidation stress threshold),

skeletal compressibility can be split into two sub components called i) αe , for the elastic range of effec-

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

tive stress where ∆σzz' ≤ ∆σzz' ' zz

∆σ > ∆σ

' zzmax

max

and, ii) αv , for the inelastic (or virgin) range of effective stress where

.

The skeletal specific storage (Ssk ) given in equation 6 has the components of Ss and Ss that acke

kv

(

count for the elastic and inelastic range of effective stress, respectively. If the effective stress ∆σzz' exerted on the aquifer system is below the previous maximum effective stress (i.e. ∆σ

' zzmax

)

) the deforma-

tion (i.e. compaction and expansion) of aquifers and aquitards is elastic and is recovered fully when the stress reverts to the initial condition. On the other hand, if the effective stress ∆σzz' exceeds the previ-

(

ous maximum stress threshold (i.e. ∆σ

' zzmax

)

) the “virgin” compaction of aquitards is mainly inelastic and

cannot be recovered fully when the effective stress decreases. The virgin compaction of aquitards includes a minor elastic component. Unlike the aquitards, the compaction of aquifers is primarily elastic but may include a minor inelastic component (Galloway and Sneed, 2013).

2.1.3 Theory of Hydrodynamic Lag The aquitard-drainage model implicitly assumes that the water in aquitards drained into the aquifers by which are transmitted to the wells. The flow in aquitards is vertical because of the large hydraulic conductivity of aquifers. The hydraulic conductivity of aquifers is usually about two orders of magnitude greater than the aquitards. On the basis of these arguments, the groundwater flow in aquitards can be described by the a 1D form of equation 4: S ′sk ∂h ∂2h = ∂z 2 K z' ∂t

(8)

where K z' is the hydraulic conductivity of the aquitard, S ′sk is the skeletal specific storage (i.e. Eq. 6) considering that α  βw in the unconsolidated alluvial aquifer systems. Terzaghi (1923, 1925) showed that consolidation is essentially complete when the dimensionless time (TD ) approaches unity. Dimensionless time is the ratio of consolidation time (t ) to the character-

istic time (τ ′) or the time constant: TD =

t τ′

(9)

Terzaghi (1923, 1925) developed an analytical solution for an equivalent of Eq. 9 to simulate the hydrodynamic lag (consolidation) in a saturated clay sample. Later on, Riley (1969) extended the theory of hydrodynamic lag to the analysis of aquitard-drainage. Helm (1975, 1976) applied the theory to the simulation of aquitard-drainage. Helm (1975) describes the time constant τ ′ for a doubly draining aquitard as:

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

2

τ′ =

b ′  S    2  ' s

K z'



(10)

’ ’ + Ssw for the elastic range of effective stress where b ′ is the thickness of the aquitard, Ss’ = Sse’ = Sske ' ' in which Sske and Ssw are the elastic specific storage and elastic skeletal specific storage, respectively. ' ' For the inelastic range of effective stress, Ss' = Ssv' ≅ Sskv where Ssv' and Sskv being the inelastic virgin specific storage and inelastic virgin skeletal specific storage, respectively, and τ ′ is the characteristic time (in equations 9 and 10) required the clay to attain about 93 percent of the ultime consolidation. Galloway and Sneed (2013) and the references therein provide a detailed assessment of the hydrodynamic consolidation theory of aquitards.

2.2 MODFLOW and SUB Package MODFLOW is a 3D finite-difference groundwater model that was first published in 1984 by McDonald and Harbaugh (1984), following the pioneering work of Trescott (1975). MODFLOW-2000 (Harbaugh et al. 2000) and MODFLOW-2005 (Harbaugh, 2005) is the most current release of MODFLOW. Both steady and non-steady flow can be simulated in an irregularly shaped flow system which may include any combination of confined and unconfined aquifer layers. The code can simulate areal recharge, discharge, evapotranspiration, flow to/from drains, wells, river/lake beds etc. Model allows use of spatially variable hydraulic conductivity, transmissivity and storage coefficient. In a layer hydraulic conductivity may be anisotropic. Boundary types used by the model include specified head and specified flux boundaries as well as the head dependent flux across the model’s outer boundary. The code has a modular structure and the modules can be easily adapted for a particular application such as subsidence. The MODFLOW-SUB (The subsidence and aquifer-system compaction) package was developed by Hoffman et al (2003) to simulate the land subsidence associated with the aquifer-system compaction. Both elastic (compaction/expansion, recoverable) and inelastic (permanent, not recoverable) compaction of interbeds can be simulated for the cases where drainage from the interbed is immediate (no-delay) or delayed. The deformation of the interbeds is caused by head or pore-liquid pressure changes that determines the changes in effective stress within the interbeds. If the effective stress is less than the preconsolidation stress of the sediments, the deformation is elastic; if the effective stress is greater than the preconsolidation stress, the deformation is inelastic. The propagation of head changes within the interbeds is defined by a transient, 1D (vertical) diffusion equation. This equation accounts for delayed release of water from storage or uptake of water into storage in the interbeds. Properties that control the timing of the storage changes are the vertical hydraulic diffusivity and the interbed thickness. The SUB package supersedes the Interbed Storage Package (IBS1) for MODFLOW, which assumes that water is released from or taken into storage with changes in head in the aquifer system within a single model time step and, therefore, can be reasonably used to simulate only thin interbeds. The SUB Package relaxes this assumption and can be used to simulate time-dependent drainage and compaction of thick interbeds and confining units. If the time-dependent drainage option is turned off, the SUB Package gives results identical to those from IBS1.

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The GMS (Groundwater Modeling SystemTM) used in this study includes a comprehensive graphical interface to the groundwater model MODFLOW as a pre- and post-processor (www.aquaveo.com). The GMS generates input data for MODFLOW and save them into a set of files that are read by MODFLOW when it is launched from the GMS menu. The GMS imports also the output from MODFLOW and visualize them in the form of tabular, text or graphical form.

3. RESULTS AND DISCUSSION In the following, first we explain the conceptual model of the aquifer system and the input parameters required by the numerical model. Then, we show how to calibrate the groundwater flow and subsidence in the model domain. Finally, we assess the spatio-temporal variation of calculated hydraulic head and subsidence distribution based on the long-term field observations.

3.1 Groundwater Flow Model Setup The MODFLOW-2000 version in LPF (Layer Property Flow) mode is used in modeling studies. The model requires hydraulic conductivity (K), hydraulic conductivity anisotropy factor (Kv / Kz ) , specific storage and specific yield. Vertical conductivity of confining layer is assumed zero. The conceptual model which is based on the geological cross-section given in Figure 2 comprises of two layers separated by a confining layer (Figure 4). Parameter values applied to numerical flow model are also shown in Figure 4. The upper layer (layer 1) is an unconfined aquifer representing the alluvial fan deposits and presumably permeable part of the Plio-Quaternary deposits. The layer 1 and 2 is separated by a confining horizon which is assumed to represent the clay-rich lacustrine deposits of Plio-Quaternary age. Layer 2 represents the confined (but convertible) Neogene aquifer. Figure 4. Conceptual model of the aquifer system

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Bottom of the Neogene aquifer is set to 650 m elevation where as the top elevation is determined by the thickness of Plio-Quaternary sediments. The lithological logs of 24 wells distributed over the model domain were used to assess the depth of Plio-Quaternary sediments from the ground surface. The first depth where a lacustrine limestone horizon encountered was assumed to be the top of Neogene aquifer. Ground elevation data were produced from digitized topographical maps. The nearest neighborhood algorithm was used to determine the areal distribution of Plio-Quaternary aquitard. The Plio-Quaternary thickness is set to zero in areas where Neogene carbonates exposed at the surface (Figure 5-a). The Konya subbasin which is subject to groundwater flow and land subsidence modeling is 8390 km2 large. This model domain is divided into 250 m x 250 m grids so that the entire flow domain comprised of 134,272 cells. The flow model was run in transient mode for 30 years (1985 and 2016). During this period, the number of wells used for groundwater abstraction is known, but the amount of abstraction from each well is unknown. According to a recent survey by State Hydraulic Works of Turkey (DSİ), total amount of annual groundwater abstraction from the model domain is 1 billion m3. Then, it was assumed that the irrigation water requirement is uniform throughout the model domain and total number of groundwater abstraction in 2015 was divided by the number of wells operated in this year to determine the annual groundwater production from each well. While this assumption is questionable, there is no other way to precisely determine the real amount of abstraction from each well. However, both the size of irrigated farm plots and the plant pattern throughout the Konya subbasin has been somewhat unchanged during the last decades that cover the modeling period. Because the number of groundwater abstraction wells in the Konya subbasin is more than 100,000 and the areal distribution of wells are uneven, some of the flow cells has no wells whereas in some cells there are more than one wells. For this reason, groundwater abstraction in the model was simulated by negative areal recharge rather than using well module. Amount of negative areal recharge was determined from the number of wells in each cell. Besides that, annual negative recharge from each cell was also tuned for each time step depending on the number of wells in that year. In accordance with the conceptual model negative areal recharge was applied only to layer 2 (i.e. Neogene aquifer) (Figure 5-b). The southern and northern boundaries of the model domain were set as specified head boundary. Based on the assessment of long-term hydraulic head observations, the head along the southern boundary was determined to have declined from 1040 m to 1010 m in 1985 to 1010 m to 980 m in 2015. Therefore, the hydraulic head along the southern boundary was reduced 1 m for every time step of transient modeling. Similarly, the hydraulic head along the northern boundary was determined to have declined from 1000 m in 1985 to 970 m in 2015 so that a 1m/year decline rate was used along this boundary as well. Eastern and western model boundaries were kept as active cells and were not assigned a hydraulic head value. A limited number of pumping tests have been conducted in Konya subbasin during 1970s and 1980s by the DSİ. Because the unpublished pumping test data is sparse, it was impossible to conduct statistical techniques to obtain a reliable spatial distribution of aquifer parameters particularly for the Plio-Quaternary deposits. A similar situation is valid also for the Neogene aquifer while relatively more pumping test have been conducted in this aquifer. Eventually, it was decided that both model layers should be assumed homogenous and the mean of parameter values obtained from pumping tests should be used.

3.2 Groundwater Flow Model Calibration Long-term (i.e. 1985-2015) hydraulic head measurements conducted only in one of the five observation wells in Konya subbasin were used to calibrate the numerical flow model. The other were found unsuitable 581

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Figure 5. Spatial distribution of a) Plio-Quaternary sediment thickness (layer 1) and, b) Groundwater abstraction density as number of wells per model cell

in view of their location in the model domain. Figure 6 shows the temporal variation of calculated and observed hydraulic head in the cell where the observation well 10472 is located. In general, both time series are in good agreement whereas deviations up to 5 meters exist in some years. However, these deviations are regarded unimportant considering the simplicity applied to the input parameters of the model.

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Figure 6. Temporal variation of observed and calculated heads during the modeling period

3.3 Subsidence Model Calibration To simulate the subsidence in the Konya subbasin, interbeds are placed in layer 2 (i.e. Neogene aquifer) and both delay and no-delay interbeds are used. Delay interbed material zone properties were set as Kv = 0.03 m/year which was taken equal to Kh of Plio-Quater nar y sediments, Ss _elastic = (Sfe) = 5 × 10−6 , Ss _ inelastic = (Sfv ) = 1.2 × 10−3 (dimensionless). For each cell, the preconsolidation head of delay interbeds were set to initial head in 1985. Likewise, the no-delay interbed material properties were set to: Sfe = 1.5 × 10−4 , Sfv = 8 × 10−3 . Dz (equivalent thickness) = 5.5. These parameter values were taken from refined studies conducted in Antelope Valley, California (Leighton et al., 2003) because no observation exist on the aquifer system deformation (i.e. Sfe and Sfv) in the Konya subbasin and the aquifer systems in both sites are remarkably similar. The subsidence rate calculated by the model for the site observed by Üstün et al (2015) was used to calibrate the subsidence model. They observed a subsidence rate of 22 mm/year during the 2010-2013 period. The model provided a comparable subsidence rate of 27 mm/year for the same period when the Sfv of delay interbed was set to 1.2 × 10−3 . The initial value of this parameter prior to model calibration was 6 × 10−4 . Figure 7-a shows the temporal variation of calculated land subsidence near Çumra city of the Konya subbasin where substantial amount of groundwater has been abstracted. The figure suggest that the linear subsidence-time relationship changed in last 6 years when the temporal subsidence rate started to increase. This period corresponds to increased rate of groundwater decline (Figure 7-b). The rate of obruk formation increased also during the same period.

3.4 Spatio-Temporal Hydraulic Head and Land Subsidence Variations Figure 8-a and b shows the model-calculated spatial distribution of hydraulic head decline and land subsidence in the Konya subbasin for the year 2016. A quick glance at Figure 5-b which shows the groundwater abstraction density as number of wells per model cell indicate that both the head decline and the land subsidence are governed primarily by the excessive groundwater use. Moreover, continuing land subsidence has also increased the number of obruk formation in recent years so that the rate of recent obruk formations in the past was about once per several years increased to about ten obruks per year. It is also interesting that many of the recent obruks concentrate in a zone where the Neogene aquifer is confined but overlain by a thin Plio-Quaternary sediment cover (see ellipse in Figure 8-b).

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Figure 7. Temporal variation of a) land subsidence in layer 1 (upper graph) and, b) hydraulic head decline (lower graph) during the modeling period in layer2

4. FUTURE RESEARCH DIRECTIONS An important issue concerning the realistic modeling of subsidence by MODFLOW is the changing flow properties of interbed that is greatly altered upon compaction as the overall vertical hydraulic diffusivity of clays is reduced a result of the loss of water content. This process results in the changes in total geostatic stress and stress-dependent skeletal specific storage. The MODFLOW-SUB is not able to incorporate changing geostatic stress and stress-dependent skeletal specific storage as handled in MODFLOW-WT. The aquitard drainage model approach used in the MODFLOW-SUB uses only the 1D storage coefficient based on the assumption that the deformation of aquifer system occurs only in vertical direction. In other words, it is not capable of simulating the horizontal components of displacement and, therefore, cannot be used in areas where horizontal motions are important. Eventually, future developments in MODFLOW should incorporate the 3D displacement and time-varying compaction parameters option in order to better simulate the land subsidence in aquifer systems comprising of a water-table aquifer overlying the confined aquifer(s).

5. CONCLUSION The land subsidence either as a subtle settling or a sudden collapse process threatens life and property. It is obvious that the increasing excessive use of groundwater favor this process via aquitard (interbed) drainage within the confined aquifers though, subsidence in the unconfined aquifers is also possible. Unfortunately, the compaction in the aquifer system due to declining head usually exceeds the inelastic deformation point and cannot be recovered even if the initial (pre-consolidation) heads are regained. Therefore, the aquifers storage capacity is damaged permanently.

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Figure 8. Maps of a) hydraulic head distribution and b) land subsidence in layer 1 calculated by the model for the year 2016 (ellipse mark zone of recent obruk formations)

As demonstrated in this paper, the effect and spatio-temporal extent of land subsidence can be inferred rapidly by means of numerical groundwater flow models coupled with an aquifer deformation package. As in the case of presented application, these models can be used even with a limited data set. However, the results would only be indicative of the zones susceptible to land subsidence. For more realistic, numerical models based on Biot’s theory have to be used. The modeled land subsidence is highly sensitive to the preconsolidation stress which is difficult to estimate from the predevelopment groundwater head because of the reasons mentioned below (e.g.

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 Using Groundwater Flow Modelling for Investigation of Land Subsidence in the Konya Closed Basin

Galloway and Burbey 2011 and the references therein). The aquitard drainage model uses the previous maximum vertical effective stress (i.e. preconsolidation effective stress) as threshold between the elastic and inelastic deformations. Inelastic deformation starts to occur when the vertical stress affecting the aquifer system exceeds the preconsolidation stress and, in general, almost all of the inelastic deformation is considered irreversible. The compressibility of aquitards in the inelastic range of stress is 1-2 orders of magnitude greater than the elastic range of stress (Riley 1998 in Galloway and Burbey 2011). Therefore, stresses that are larger than preconsolidation stress cause land subsidence. In general, predevelopment groundwater heads are used to estimate the preconsolidation stresses in the aquitards of the aquifer system based on an assumed equilibrium between the fluid pressure and effective stress. However, alluvial and basin-fill aquifers are usually overconsolidated so that the preconsolidation stresses exceed the predevelopment effective stresses (e.g. Galloway and Burbey 2011). Since natural processes like removal of overburden by erosion, decline of prehistoric groundwater heads, desiccation and diagenesis can lead to overconsolidation (e.g. Holzer 1981), the land subsidence in a typical alluvial aquifer occurs only after substantial drawdowns have increased effective stresses beyond the preconsolidation stress in pristine (native) state (Galloway and Burbey 2011).

REFERENCES Bayarı, C. S., Özyurt, N. N., & Kilani, S. (2009b). Radiocarbon age distribution of groundwater in the Konya Closed Basin, central Anatolia, Turkey. Hydrogeology Journal, 17(2), 347–365. doi:10.1007/ s10040-008-0358-2 Bayarı, C. S., Özyurt, N. N., & Pekkan, E. (2009c). Giant collapse structures formed by hypogenic karstification: the obruks of the Central Anatolia, Turkey. In A. B. Klimchouk & D. C. Ford (Eds.), Hypogene Speleogenesis and Karst Hydrogeology of Artesian Basins. Ukraine Inst. of Speleology and Karstology (pp. 83–90). Simferopol. Bayarı, C. S., Pekkan, E., & Özyurt, N. N. (2009a). Obruks, as giant collapse dolines caused by hypogenic karstification in central Anatolia, Turkey: Analysis of likely formation processes. Hydrogeology Journal, 17(2), 327–345. doi:10.1007/s10040-008-0351-9 Biot, M. A. (1941). General theory of three-dimensional consolidation. Journal of Applied Physics, 12(2), 155–164. doi:10.1063/1.1712886 Chatterjee, R. S., Fruneau, B., Rudant, J. P., Roy, P. S., Frison, P. L., Lakhera, R. C., & Saha, R. et al. (2006). Subsidence of Kolkata (Calcutta) City, India during the 1990s as observed from space by Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. Remote Sensing of Environment, 102(1–2), 176–185. doi:10.1016/j.rse.2006.02.006 Cooper, H. H. Jr. (1966). The equation of groundwater flow in fixed and deforming coordinates. Journal of Geophysical Research, 71(20), 4785–4790. doi:10.1029/JZ071i020p04785 Don, N. C., Hang, N. T. M., Araki, H., Yamanishi, H., & Koga, K. (2006). Groundwater resources management under environmental constraints in Shiroishi of Saga plain, Japan. Environmental Geology, 49(4), 601–609. doi:10.1007/s00254-005-0109-9

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Galloway, D. L., & Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8), 1459–1486. doi:10.1007/s10040-011-0775-5 Galloway, D. L., Hudnut, K. W., Ingebritsen, S. E., Phillips, S. P., Peltzer, G., Rogez, F., & Rosen, P. A. (1998). Detection of aquifer system compaction and land subsidence using interferometric synthetic aperture radar, Antelope Valley, Mojave Desert, California. Water Resources Research, 34(10), 2573–2585. doi:10.1029/98WR01285 Galloway, D. L., & Sneed, M. (2013). Analysis and simulation of regional subsidence accompanying groundwater abstraction and compaction of susceptible aquifer systems in the USA. Boletin De La Sociedad Geologica Mexicana, 65(1), 123–136. Gambolati, G., & Freeze, R. A. (1973). Mathematical simulation of the subsidence of Venice, 1: Theory. Water Resources Research, 9(3), 721–733. doi:10.1029/WR009i003p00721 Hanson, R. T., & Benedict, J. F. (1994). Simulation of ground-water flow and potential land subsidence, upper Santa Cruz Basin, Arizona. US Geological Survey Water Resources Investigation Report 93–4196. Retrieved from http://pubs.er.usgs.gov/usgspubs/wri/wri934196 Hanson, R. T., Li, Z., & Faunt, C. (2003a). Application of the multi-node well package to the simulation of regional-aquifer systems in the Santa Clara Valley, California. Proceedings of MODFLOW and More 2003: Understanding through Modeling Conference Golden, Colorado. Hanson, R. T., Martin, P., & Koczot, K. M. (2003b). Simulation of groundwater/surface-water flow in the Santa Clara-Calleguas groundwater basin, Ventura County, California. US Geological Survey Water Resources Investigation Report, 02–4136. Retrieved from http://pubs.usgs.gov/wri/ wri024136/text.html Harbaugh, A.W. (2005). The U.S. Geological Survey modular ground-water model-The ground-water flow process. U.S. Geological Survey Techniques and Methods: Reston, Virginia, United States Geological Survey. Harbaugh, A. W., Banta, E. R., Hill, M. C., & McDonald, M. G. (2000). United States Geological Survey, Open-File Report. Proceedings of MODFLOW ‘00, Reston, Virginia. Helm, D. C. (1972). Simulation of aquitard compaction due to changes in stress. Transactions - American Geophysical Union, 5(11), 979. Helm, D. C. (1975). One-dimensional simulation of aquifer system compaction near Pixley, Calif. 1: Constant parameters. Water Resources Research, 11(3), 465–478. doi:10.1029/WR011i003p00465 Helm, D. C. (1976). One-dimensional simulation of aquifer system compaction near Pixley, Calif. 2: Stress-dependent parameters. Water Resources Research, 1(3), 75–391. Hoffmann, J., Leake, S.A., Galloway, D.L., & Wilson, A.M. (2003). Ground-Water Model-User Guide to the Subsidence and Aquifer-System Compaction (SUB) Package. U.S. Geological Survey GroundWater Resources Program, Tucson, Arizona. Holzer, T. L. (1981). Preconsolidation stress of aquifer systems in areas of induced land subsidence. Water Resources Research, 17(3), 693–704. doi:10.1029/WR017i003p00693

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Jacob, C. E. (1940). On the flow of water in an elastic artesian aquifer. American Geophysical Union. Jacob, C. E. (1950). Flow of ground water. In: Rouse H (ed), Engineering hydraulics. Proceedings of the Fourth Hydraulics Conference, Iowa Institute of Hydraulic Research, Iowa City, IW. Kasmarek, M. C., & Strom, E. W. (2002). Hydrogeology and simulation of ground-water flow and land-surface subsidence in the Chicot and Evangeline aquifers, Houston-Galveston region, Texas. US Geological Survey Water Resources Investigation Report. 02–4022. http://pubs.usgs.gov/wri/wri024022/ Larson, K. J., Basagaolu, H., & Mario, M. (2001). Numerical simulation of land subsidence in the Los Banos-Kettleman City area, California. University of California Davis Water Resources Center technical completion report contribution 207. http://escholarship.org/uc/item/5h60p535?query=larson%20keith Leake, S. A. (1990). Interbed storage changes and compaction in models of regional ground-water flow. Water Resources Research, 26(9), 1939–1950. doi:10.1029/WR026i009p01939 Leake, S. A. (1991). Simulation of vertical compaction in models of regional ground-water flow. In A.I. Johnson (Ed.), Proceedings of the Fourth International Symposium on Land Subsidence. Houston, TX: IAHS Publication. Leake, S. A., & Galloway, D. L. (2007). Ground-water model: user guide to the Subsidence and AquiferSystem Compaction Package (SUB-WT) for water-table aquifers. US Geological Survey Techniques and Methods Report 6–A23. Retrieved from http://pubs.usgs.gov/tm/2007/06A23/ Leake, S. A., & Prudic, D. E. (1991). Documentation of a computer program to simulate aquifer-system compaction using the modular finite-difference ground-water flow model. In US Geological Survey Technical Water Resources Investigation (Bk. 6, Ch. A2). Retrieved from http://pubs.usgs.gov/twri/twri6a2/ Leighton, D. A., & Phillips, S. P. (2003). Simulation of ground-water flow and land subsidence in the Antelope Valley ground-water basin, California. Water-Resources Investigations Report 2003-4016. McDonald, M. G., & Harbaugh, A. W. (1984). A modular three-dimensional finite-difference groundwater flow model. U.S. Geological Survey Open-file Report. McDonald, M. G., & Harbaugh, A. W. (1988). A modular three-dimensional finite-difference ground-water flow model, in U.S. Geological Survey Techniques of Water-Resources Investigations: Reston, Virginia, U.S. United States Geological Survey (Bk 6, Ch A1). Retrieved from http://pubs.usgs.gov/twri/twri6a1/ Narasimhan, T. N., & Witherspoon, P. A. (1977). Numerical model for land subsidence in shallow groundwater systems. Proceedings of the Second International Symposium on Land Subsidence, Anaheim, CA. Retrieved from http://iahs.info/redbooks/a121/iahs_121_0133.pdf Neuman, S. P., Preller, C., & Narasimhan, T. N. (1982). Adaptive explicitimplicit quasi three-dimensional finite element model of flow and subsidence in multiaquifer systems. Water Resources Research, 18(5), 1551–1561. doi:10.1029/WR018i005p01551 Nishikawa, T., Rewis, D. L., & Martin, P. (2001). Numerical simulation of ground-water flow and land subsidence at Edwards Air Force Base, Antelope Valley, California. US Geological Survey Water Resources Investigation Report 01–4038.

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Okay, A., & Tüysüz, O. (1999). Tethyan sutures of northern Turkey. In: Durand B, Jolivet L, Horvath F, Seranne M (eds.) The Mediterranean basins: tertiary extensions within the Alpine Orogen. Geological Society, 156, 475–515. doi:10.1144/GSL.SP.1999.156.01.22 Osmanoğlu, B., Dixon, T. H., Wdowinski, S., Cabral-Cano, E., & Jiang, Y. (2011). Mexico City subsidence observed with persistent scatterer InSAR. International Journal of Applied Earth Observation, 13(1), 1–12. doi:10.1016/j.jag.2010.05.009 Riley, F. S. (1969). Analysis of borehole extensometer data from central California. In: Tison LJ (ed) Land subsidence. Proceedings of the Tokyo Symposium. IAHS Publication. Retrieved from http://iahs. info/redbooks/a088/088047.pdf Riley, F. S. (1998). Mechanics of aquifer systems: the scientific legacy of Joseph F. Poland. In J.W. Borchers (Ed.), Proceedings of the Dr. Joseph F. Poland Symposium on Land Subsidence, Sacramento, CA, Star, Belmont, CA. Terzaghi, K. (1923). Die berechnung der durchlässigkeitziffer des tones aus dem verlauf der hydrodymanischen spannungserscheinungen [The computation of permeability of clays from the progress of hydrodynamic strain]. Sitzungsberichte, Mathematisch-naturwissenschaftliche Klasse, Part IIa 132, Akademie der Wissenschaften, Vienna. Terzaghi, K. (1925). Settlement and consolidation of clay. New York: McGrawHill. Trescot, P. C. (1975). Documentation of finite-difference model for simulation of three-dimensional ground-water flow. U.S. Geological Survey Open-file Report. UNESCO. (1972). Glossary and multilingual equivalents of 227 Karst terms. Paris: UNESCO. Üstün, A., Tusat, E., & Yalvaç, S. (2010). Preliminary results of land subsidence monitoring project in Konya Closed Basin between 2006–2009 by means of GNSS observations. Natural Hazards and Earth System Sciences, 10(6), 1151–1157. doi:10.5194/nhess-10-1151-2010 Üstün, A., Tuşat, E., Yalvaç, S., Özkan, İ., Eren, Y., Özdemir, A., & Şimşek, F. F. et al. (2015). Land subsidence in Konya Closed Basin and its spatio-temporal detection by GPS and DInSAR. Environmental Earth Sciences, 73(10), 6691–6703. doi:10.1007/s12665-014-3890-5 Wilson, A. M., & Gorelick, S. (1996). The effects of pulsed pumping on land subsidence in the Santa Clara Valley, California. Journal of Hydrology (Amsterdam), 174(3-4), 375–396. doi:10.1016/00221694(95)02722-X Witherspoon, P. A., & Freeze, R. A. (1972). The role of aquitards in a multiple-aquifer system. Geotimes, 17(4), 22–24.

ADDITIONAL READING Galloway, D. L., & Burbey, T. J. (2011). Review: Regional land subsidence accompanying groundwater extraction. Hydrogeology Journal, 19(8), 1459–1486. doi:10.1007/s10040-011-0775-5

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Galloway, D. L., Jones, D. R., & Ingebritsen, S. E. (1999). U.S. Geological Survey Circular: Vol. 1182. Land subsidence in the United States.

KEY TERMS AND DEFINITIONS Aquitard: A hydrogeological unit comprising mainly of low-permeability interbeds that restrict the groundwater flow. A completely impermeable aquitard is called an aquiclude or aquifuge. Collapse Doline: A rare form of karst landscape, forms when the roof of an underlying karst cavity collapses due to over enlargement by dissolution. Formation of obruks may be triggered the reduced buoyancy of groundwater in areas where over pumping results in appreciable head decline. Compaction: A rapid and artificial process that results in the packing of soil particles more densely through the reduction of pore space. Consolidation: A natural process by which the volume of geologic material is reduced by gradual expulsion of pore water as a results of long term static loads. Effective Stress: A critical force that keeps a set of particles together. If an external force (e.g. pore water pressure) reduces the effective stress, the set of particles is fall apart. İnterbed: A low-permeability geologic material within a more permeable matrix. Karst: A specific landscape formed by the dissolution of carbonate and evaporitic rocks. Karst aquifers are characterized by large underground drainage systems in which turbulent flow conditions may be achieved in contrast to the granular aquifers. Modflow-Sub: A package of MODFLOW which is used to simulate the compaction of aquifers, interbeds and confining units of an aquifer system. Obruk: A Turkish karst term used to describe collapse dolines which are commonly exist in central Anatolia. Subsidence: The slow settlement and sudden collapse of landforms to a lower level as a result of the drainage of an aquitard by means of natural and/or anthropogenic processes.

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Chapter 17

Integration Between Urban Planning and Natural Hazards For Resilient City Şule Tüdeş Gazi University, Turkey Kadriye Burcu Yavuz Kumlu Gazi University, Turkey Sener Ceryan Balikesir University, Turkey

ABSTRACT Analyses and syntheses conducted before the urban planning process are significant. Accurate analysis and synthesis enable to determine proper site selection and the proper site selection is the basis of a sustainable urban plan. In this sense, fundamental analysis inputs of the proper site selection could be indicated as the related parameters of the earth sciences. The interpretation of these inputs require the essential analyses and syntheses of initially the geological and geotechnical research with geophysics, tectonic, topography, mineral and natural resources, hydrogeology, geomorphology and engineering geology. Synthesis maps composed of these inputs especially provide guides for natural thresholds consisting of landslide, flood, inundation, earthquake etc. for land use planning and site selection parts in the urban planning processes. In this regard, this chapter of the book contains the relation between the earth sciences parameters with the urban planning and the way these parameters lead the way of urban planning processes.

INTRODUCTION The mass urbanization of the human race is a relatively new phenomenon in the history of civilications (Urban geology and emerging dicipline, Wilson and Jackson, 2016) in 1900, when geology was maturing as a science only ten percent of the global population lived in urban settings; now, this proportion has surpassed 50 percent, fed by both overall population growt and rural -to-urban migration (Wilson DOI: 10.4018/978-1-5225-2709-1.ch017

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 Integration Between Urban Planning and Natural Hazards For Resilient City

and Jackson, 2016). People are having difficulty finding suitable settlement areas, because of that this rapid increase in the urban population and the accompanying plot and land demand to meet housing needs.With rapid and unplanned urbanization, people have begun to settle unconsciously in areas where settlement is inappropriate. This has also increased urban vulnerability while reducing urban livability, natural hazard safety and disaster sensitivity countries and urban areas, which are located on the active tectonic belt, are vulnerable in the context of natural hazards, as earthquakes, volcanic eruptions, tsunamis, landslides etc. Not only the developing countries, but also the developed ones, including Japan and USA have higher risks in that sense. Dangers caused by natural processes and the transformation of these dangers into risks might affect urban living areas and make them unlivable places. However, the main aim of the urban planning is to provide safe, sustainable, healthy and happy living spaces and resilient city for people. Besides enhancing the existing situation, the discipline of urban planning is responsible for developing applicable methods, which eliminate the vital problems which might be faced in the future, in the context of an anticipated projection. These goals should provide solutions arising from short, medium and long term projections. Geological, techtonical, hydrogeological, geomorphological, geotechnical and other related data, which are belonging to the cities, are included in the analyses and syntheses related with the site selection process in the context of urban planning comprises the first stage of the urban planning and in this stage, urban planner should consider the conservation-usage balance, natural hazards and sustainability concepts. The content and scope of these analyses and syntheses might change in the context of planning hierarchy (from upper scale to lower scales) and should be support themselves. Additionally, a feedback process should be developed through upper scales to lower scales. In the process of analysis and synthesis, the most significant threshold and restrictions are arising from the topographical, geological, geomorphological, geotechnical, hydrogeological, geographical and lithological characteristics of the urban land. Therefore, the evaluation of all of these geological data, determination of the thresholds, transforming the disadvantages to advantages, determination of the opportunities and threats in the physical context are the essential part of the urban land use planning. On the other hand, it is not possible to develop sustainable and hazard sensitive urban areas. In this context, studies related with urban geology field have gained significance in the world, especially after the industrial revolution. However, even in the developed countries, this field could not reach its well-deserved place (Wilson and Jackson, 2016). The mass urbanization of the human race is a relatively new phenomenon in the history of civilization (urban geography as emerging discipline) in 1900, when geology was maturing as a science, only 10 percent of the global population lived in urban settings; now, this proportion has surpassed 50 percent, fed by both overall population growth and rural to urban migration. And by 2030, it is expected to reach 60 percent(from Wilson and Jackson, 2016). this increasing population in the world cities is especially dramatic in the context of the developing countries. Rapid increase in the population growth rate is getting ahead of urbanization rate and this situation might cause the unplanned configuration in the cities. This causes the problems, as inappropriate site selection, squatting, as well as underestimating geological and physical environment. Therefore, it is impossible to prevent from the undesired consequences of earthquakes, floods, landslides and other related natural hazards. Vital resources, as water, soil, forest, mines etc. fall short of and this situation might threaten the future generations, as well as the biological lifetime of the world decreases. Although many of the researches give emphasis on the issue, studies related with the urban geology could not reach its well-deserved place, as mentioned before. Another significant problem is that urban 592

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planners do not have adequate control over the data collection and analysis-synthesis stages, as well as a common language among the related disciplines could not be sufficiently developed. The content and scope of the data is changing, depending on the scale of the plan and the related scenarios. For instance, for lower scale urban plans (e.g. 1/1 000 scaled implementation plans), geotechnical data in the context of civil engineering is significant, while for the upper scale urban plans (e.g. 1/25 000 scaled environmental plans), data in the context of engineering geology is considered as important. Hence, considering for each planning scale, geological data should be analyzed, depending on the scale of the urban plan and its details. Additionally, the data should be classified, according to the scale of the urban plan. Considering the fact that studies related with the urban geology remain insufficient in both Turkey and global context, it is highly important for urban planners to collect appropriate geological data suitable to the aim and scale of the related plan. So that, it would be possible to develop geological synthesis maps and they enable urban planners to make proper decisions about site selection process in the urban planning context. In this study, these mentioned concepts will be clarified and be systematized in the sense of urban planning. Therefore, a systematic approach will be provided to urban planners for data collection, analysis and synthesis stages to build resilient city . 1. Urban planning processes 2. The urban geology definition and its content 3. Geoscientific data and natural hazards in the context of urban planning and its applications Urban geology might be defined as the application of engineering geology on the cities and urban development areas. In this sense, urban geology is a part of and subfield of the engineering geology. Urban geology provide to engineers, planners, decision makers, and the general public with the geoscience information required for sound regional planning in densely populated areas (Anon, 2008; Culshaw and Price, 2011). As well as all of the definitions are true, as Culshaw and Price (2011) states the definition and the content of the urban geology should be extended. A systematic approach should be developed by considering the other disciplines related with the urban geology. The expectation of the urban planner, civil engineer, architect, real estate developer, environmental engineer about the urban geology is the same within its fundamental parameters, although they might change. This situation enlarges the definition of the urban geology in a broader extent. Thereby, as some of the researchers (McGill,1964; Rau, 2003;Wilson and Jackson, 2016) agree, perhaps urban geology could be a separate field of science. The aim of the urban planning is to increase the quality of life, as well as sustaining the sustainability in the world having scarce natural resources. By assuring this, it is also responsible for the selection of the living spaces which are far away the hazards and risks, determination of the land where is economical and safe (therefore, costs are reduced), provision of an efficient resource management, protection of the natural resources by preventing unhealthy housing conditions, provision of healthy environmental conditions and equalization of the socio-economic conditions of the people. To reach these aims is possible by the helps of geological data. In this sense, systemization, presentation and evaluation of these data is vital. In this context, urban geology could be considered as a part of the environmental look of the sustainability concept in a sense. These geological data exist on a wide range of basis and urban geology should be specific to the studied area and the related data should be given to the decision makers and urban planners. As stated previously, urban planners work in the context of different scales and urban development scenarios. 593

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Hence, the might be in difficulty for sortation, systemization and evaluation of the geological data. Urban planners should give emphasis on urban geology studies in the process of urban planners’ selfdevelopment process. The expectations of the urban planners, decision makers and members of local governments from the field of geoscience could be listed as below: • • • • • • • • • • • •

• • •

Proposing necessary measures to determine and prevent from the geological hazards in the urban development areas, Determining existing geo risks and proposing necessary measures in the living spaces of the urban areas, Determining advantageous and disadvantageous lands in the city and in the nearby of the city, Determining the effect of geological conditions on especially industrial, residential, institutional and educational areas, Providing detailed ground research (from upper to lower scales) in the context of urban development areas, Providing input to the usage of land resources and their management, Providing effective cost and life safety, Being a guide to optimal land use planning Determining the industrial mineral resources and mineral deposits, providing reserves and specifying their usages with industry and technology to sustain economic development of the city, Providing the location, reserve and technic conditions of the building materials which are needed for the urban development, Determining the regional and local hydrogeological conditions by starting from the basin where the city is located on to provide an efficient water management and to prevent the risks arising from floods, Producing the maps including the geoscientific data. These maps could be listed as engineering geology maps, micro zonation maps, land suitability maps, geomorphology maps, mining maps, geotechnical maps, hydrogeology maps, earthquake risk and hazard maps, landslide risk and hazard maps and so on, Determining the site of the garbage storage yards, Determining the network and ground characteristics of the structures and infrastructures, Proposing the measures related with the determination of the geological protection areas,

The scale of those information might change in the context of environmental plans, master plans or implementation plans. While the data related with engineering geology could be enough for 1/25 000 scaled environmental plan, there might be a need of a detailed geotechnical work including drilling studies for 1/1 000 scaled implementation plans. All of the urban geology related maps which are produced considering the planning hierarchy and the planning scale would be easier the urban planners’ activities, as well as these maps enable urban planners to use geoscientific knowledge in an affective and appropriate way. Well managing the relation between the environmental and natural resources with the humankind in the context of urbanized areas is necessary to provide sustainable development and protection. The aim of the various disciplines should be complied with the nature, not to manage it. If the language of the nature will be well read, both the safe living spaces are developed and the soil, forest and the water are protected. By avoiding to settle on water basins, build risky structures on soft ground and cut forests; we 594

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could both protect vital resources and provide safe sheltering areas. Thereby, by working coordinately; engineering geologists, urban planners, civil engineers and real estate developers could provide an environment and hazard sensitive sustainable world. In this sense, urban geology should be considered as a separate subset of applied geology field. Finally, all these geoscientific aspects which are detailed clarified in this chapter will guide to urban planner to create sustainable, safer and resilient city.

BACKGROUND Urban Planning Procesess Planning process includes those stages; 1: Research, 2:Analysis-Synthesis, 3: Determination of the aim and the objectives, 4: Planning and 5: Application and evaluation 1. Research: This stage begins with the collection of the physical, environmental, economic and social data related with the planning area. The data, which is required to evaluate in the planning process, is related to the environmental, social, demographic, economic, financial and administrative structure of the planning area. If those data are listed hierarchical: a. Introducing the location of the planning area in the country and the region in the context of geographical, social and economic dynamics. b. Social and Demographic Structure Analysis Data: Settlement populations and projections, population densities and their distribution, urbanization rate and its dynamics, migrations, social and cultural structure of the population. c. Economic Structure: National and international hinterland relations of the planning area. d. Sectoral distribution, economic growth and size, labor force projections related to agriculture, industry and services. e. Land use analyses include sub-analyses as: i. Determination of the size and the densities of the urban usages as, housing, commerce, industry, urban greenery spaces, parks, storage, public spaces etc. ii. Determination of the population projections, depending on the potential urban growth structure. iii. Density distributions and the determination of the urban sprawl. f. Ownership Status: Public, military, private etc. It is necessary to determine the ownership and its distribution. g. The existence of the historical, urban and environmental values is evaluated in the sense of tourism and conservation. Potentials are revealed for investments and incentives. Problems related with all of the natural-cultural values as, historical protection sites, characteristics of the historical pattern, archeological sites, national parks, coasts etc. are solved and necessary measures are taken to protect those areas. h. By examining administrative structure, legislative framework and current master plan; application of the master plans, implementation plans, environmental plans and conservation plans and the relationship among them are sustained. Existing organization, off-plan developments, plan boundaries, conservation site boundaries are determined. Additionally, depending on the 595

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2.

3.

4.

5.

legislative framework related to limitation of the historical, natural, archeological, geological and urban protection site, formation of the water resources protection zones, assigning the flight corridors and the protection of the facilities, which store hazardous substances, planning decisions are developed. Urban decision makers, as NGOs (Non-governmental organizations) are also added to the planning process. Analysis-Synthesis: After the determination of the aim and objectives, which are necessary to solve the problem and for the sustainable urban development; various scenario alternatives, which are required to reach the aim and the selection of the best decision in the sense of alternative selection; scientific method and synthesis should be developed. Therefore, collected data are included in the synthesis and the planning process. The collected and analyzed data should promote the decisionmaking mechanisms of the planning process, by proper synthesis. It is possible with the scientific methods and techniques. Those methods and techniques could be listed as: a. Statistical methods for population and labor force projections (Kernel method etc.). b. Multi criteria analysis techniques, GIS-based decision support systems, system analyses, graphics, three-dimensional urban modelling and visualization techniques etc. Statistical and mathematical techniques (grey relation analyses, topsis, electre, ahp etc.). c. Questionnaires, which enable the participation of the public and the expert opinions. d. The selection of the alternatives. e. By evaluating the application, economy, finance, legislative perspective, regulations and politics, implementation starts. Determination of the Aim and the Objectives: In the beginning stage of the planning, local dynamics, environmental characteristics and socio-political features of the planning area are considered and then, depending on the revealed physical, economic and social parameters; aim and objectives are determined. Planning Process: It is a necessity that re-evaluation of the physical and geologic, environmental and socioeconomical problems of the planning area in the context of the determination of the aims, objectives and the plan alternative, to sustain the sustainable and resilient cities. It is also significant in the sense of the determination of the proper plan alternatives. The collected, analyzed and synthesized data related with the planning urban area enables to develop more rational solutions an those solutions could be developed with the different plan alternatives. Developing multi alternatives in the planning procedure enables decision makers to take proper planning decisions. It is significant to benefit from the GIS-based decision support systems, which are integrated with the multi criteria analyses techniques, in the context of the evaluation of the plan alternatives and related planning decisions, as well as carrying out the aims and objectives in the sense of creating economically viable, sustainable and livable cities. Application, Evaluation and Feedback: In the conclusive stage of the planning process, appropriate implementation and programs are developed for the chosen plan alternative, using modern scientific methods and techniques, explained above. In this stage, a. Priority areas are determined, b. In which program the plan will be implemented is finalized c. How the organization model will process is determined

Depending on those components, the application process of the planning is sustained systematically and issueless. 596

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None of those stages in the planning process is disconnected. The process works relational and depends on feedbacks. There are flexible transitions and feedbacks among the stages of the process. Those transitions and feedbacks enable the applicability of the planning process in a complete and appropriate way.

Urban Geology and Planning Considering that more than half of the world’s population live in urban areas in our day, especially, it is clear that urban geological studies have vital importance to the whole humanity on a global scale and are the basis for creating cities whivch are sustainable, healthy, economical habits and durable. Intuitively, engineering geologists realize that geology is important for urban development and regeneration (Culshaw and Price, 2011). The said authors examined the perception, definition and application areas of the field of urban geology from 1877 to today. In this sense, he put emphasis on these definitions defined by various researches as: 1. ‘’Since the science of geology is concerned with all aspects of the crust of the Earth, the use of geological information, and of geological methods to obtain new information about local subsurface conditions, should therefore be an essential part of the physical planning of all cities ‘’(Legget, 1973, 1969; Culshaw and Price, 2011) 2. ‘’Urban geology is the study of land resources and geologic hazards as they relate to the development and expansion of urban areas’’ (Dai et al., 1994; Culshaw and Price, 2011). 3. “Urban geoscience (urban geology) is an interdisciplinary field in the geo- and socio-economic sciences addressing Earth related problems in urbanized areas” (De Mulder, 1996; United Nations, 1996a; Culshaw and Price, 2011). 4. “Urban geology spans both regional and applied geology. Some emphasis is usually assumed in the application of geological principles and knowledge to the soluiton of construction, and now environmental, problems in or near urban areas (Karrow and White, 1998b; Culshaw and Price, 2011). Wilson et al., (2016) also described urban geologyas ‘’In the broadest terms, urban geology is the application of the earth sciences to problems arising at the nexus of the geosphere, hydrosphere and biosphere within urban and urbanizing areas. . In this way, it goes beyond the application of geology in civil engineering (commonly called “engineering geology”) (Wilson et al., 2016). Urban geology draws on the entire toolbox of the earth sciences, from stratigraphy to geochemistry and hydrogeology to geophysical exploration techniques; and it often makes linkages to the biological and environmental sciences’’ (Wilson et al., 2016). Geological information of the urban areas is an important input not only urban planners but also other profession such as civil engineering, environmental engineering (Table 1). As shown in the Table 1, urban planners need the most geo scientific information from the other professionals. However, there is communication problems between geoscientists (e.g. Geological engineers, geotechnical engineers, geophysics engineers etc.) and urban planners. Terminology of the geo scientific information is too difficult for urban planners. They are forced to understand and interpret geo scientific information for decision making for land use, planning and urban site selection. Because of that, geo information for urban areas must be provided in a way that they can understand, use and apply easily. Therefore, developing the interdisciplinary terminology according to users is important. Especially, 597

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engineering geologists must develop clear, interpretable, understandable legend for geo scientific maps which especially must be base for environmental plans, master development plans and implementary development plans for urban planners. In addition, geo scientific map scales must be compatible with scales of urban plans. Urban planners spend a lot of effort to gather data in urban areas and environment and bring them together in properly to interpret and manage. These data can be physical, geological, spatial, environmental, economic and social. Especially, to interpret and understand of geoscientific data is too difficult for them. Therefore, these spatial data must be analyzed and synthezed within a systematic for using them. Studies related with urban geology make easier the site selection processes in the context of urban planning and reduces the related costs, as well as these studies directly refer to the world population. Table 1. Professionals who study urban and environment need the geological information (Culshaw and Price, 2011 modified) Professions Need Geoscientific Information

Geoscientific Information Requirements

Real estate development specialists

Possible and existing geological restrictions on development and construction. Geo hazards and geo risks.

Architects

• Geo hazard susceptibility • Ground foundation requirements • Geological resources • Natural and human-made heritage for conservation

Survey engineers

• Geological conditions • Landslide stability • Dam deformations • Landslide monitoring • Ground movement • Fault line analyses • Tunnel stability • Mining

Civil engineers

• Physical, geo chemical and geo technical properties of the geological materials. • Geological properties of natural and artificial materials • Ground water conditions • Geo technical properties of the foundations • Landslide stability • Rock stability • Mass movements • Excavatability • Site investigations • Geo hazards and geo risks

Archeologists

• Distribution and properties of geological materials • Conditions and distributions of the ground water • Stratigraphy and geological processes • Past land use

Environmental engineers

• Distribution of the ground water • Ground water conditions • Geochemical and hydrological properties of the ground water • Past land use • Physical, geo technical and geo chemical properties of geological materials • Processes of the anthropogenic and geological at the geological times • Geo hazards and geo risks • Geo hazards susceptibility

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Table 1. Continued Professions Need Geoscientific Information Urban and regional planners

Geoscientific Information Requirements

• Geological restrictions and opportunities • Natural resources • Mining • Conditions of the ground water and surface water • Distribution of the ground water • Geo hazards • Geo risks • Geomorphology • Geo hazard susceptibility • Rational and optimal land use particularly on the base of geological conditions • Ground movement • Mass movements • Slope stability • Rock stability • Hydrogeological conditions • Map of bedrock, superficial and artificial deposits • Geo heritage • Ground conditions • Proper site selection on the base of geo scientific information • Land use • Natural and artificial geological materials and their physical, geo-mechanical and geo-chemical properties and dispersion • Geo hazards and possible impact on urban area and environment • Strategic development and decision making on the base of geology in urban environment • Site investigation information • Investigations of ground movements • Dispersion and range of industrial minerals • Minerals pose a threat in residential areas • Tectonic properties of the region • Earthquake susceptibility

Preventing natural hazards, managing water and natural resources are required to sustain the maintenance of the humankind. Therefore, creating sustainable living spaces and urban areas is directly linked with the urban geology. Site selection analysis in the context of the analysis and the synthesis stages of the urban planning and especially the land use planning should be considered as a whole with the studies related with the urban geology. In this sense, planning activities, especially in the site selection process, regarding to different scales, as 1/1000, 1/5000, 1/25000 etc. should be implemented by considering thefundamentals of urban geology. Used geological base maps, as hydrogeology, geotechnical, geomorphology, geophysics maps etc. should be appropriate considering the scale of urban planning.Tudes and co-workers have prepared the geotechnical microzonation map of the Gumushane NE Turkey to serve as a base for the 1/25000 environmental and 1/5000 implementary development planning and for providing geoscientific knowledge to the plan decisions for urban planners (Figure 1), (Tudes et al., 2012). By combining all of these maps in the perspective of various quantitative multi criteria decision making techniques, such as Analytic Hierarchy Process, Simple Additive Weighting, ELECTRE, Grey Relation Analysis etc. is significant in the sense to enable planning procedures related with the sustainable site selection and land use planning, determining the fact of the main aim and the scenario assigned to these plans.In this context, Tudes et al. (2012) also created the settlement suitability map based on geoscientific

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Figure 1. Geotechnical micro zonation map of Gumushane settlement area (WD: weathering degree of rock mass, F: fresh, SW: slightly weathered, MW: moderately weathered, HW: highly weathered, CW: completely weathered, RS: residual soil, GSI: geological strength index, σcm and Em unconfined compressive strength and elasticity modulus of rock mass, respectively (Tudes et al., 2012).

components of the said urban settlement using the Analytic Hierarchy Process (AHP) method in the above mentioned studies (Figure 2).With decisions, which are suitable to the natural characteristics of the land and user the geological advantages, as well as the avoider the geological thresholds and distant to the dangers and risks; the foundation of a sustainable world is laid by sustaining the life and commodity safety of the humankind, considering the fact of natural resources’ exploitation and the wellbeing of the future generations. In this context, urban geology could be considered as an essential component and discipline of the urban planning. However, studies related with this issue remains insufficient, al-

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though developed countries have these studies related with the linked relation with planning and urban geology. Especially in the context of developing countries; urban planning activities underestimate the benefits of urban geology studies, related with geological thresholds, advantages and natural hazards. Accordingly, thousands of people are suffering from the earthquakes, tsunamis, floods, landslides and the other natural hazards each year, in the global context. Besides from the loss of lives, the economy falls, as well as groundwater resources, agricultural lands and the other living spaces are deteriorated as the consequences of the unpreparedness to the natural hazards in the urban planning procedures. Hence, as Wilson et al., (2016) stated, urban geology should be considered as a special discipline and should be developed in this sense, because urban geology provides essential data for either urban designing and planning stage or construction and design stages. It provides input for various disciplines as architecture, construction, urban planning and real estate. Therefore, it could be said that urban geology provides geological information with different details to the disciplines, could be linked with various scales from 1/50 to 1/100 000. In other words, urban geology provides geological data and information by combining them as an interdisciplinary manner, in the context of disciplines, as civil engineering, architecture, urban and regional planning, environmental engineering, biology and real estate development. Hereby, it is possible to make proper decisions in the sense of economic and sustainable environment, as well as hazard sensitive urban areas. Figure 2. Urban suitability of Gumushane settlement area (A: well suited for settlement, N: suitable for settlement, C: moderately suitable for settlement, D: with geotechnical precautions, E: Not suitable for settlement (Tudes et al., 2012)

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Geological Components such as geoheritage, row material resources, hydrolic hazards (flood hazard), soil and water pollution have a part different ways in sustainability and resilience in planning. As Culshaw and Price (2011) states urban geology researches in the context of global scale goes back to the studies in between 1877 and 1973, could be considered as the initial part of the geological researches developed for the urban area and its nearby environment (Kingsley, 1877; Jahns, 1958; Leighton, 1966; Leeds, 1966; Legget, 1969; Alfors et al., 1973; Legget, 1973). Today, these studies remain insufficient in the sense of geology, especially for the engineering geology, required for the urban planning and the construction stages in the urban areas. Forster and Culshaw, 1990; Bozzano et al., 1999; Lo and Diop, 2000; Howland, 2001; Dai et al., 2001; Wotling and Bouvier, 2002; Pareyra and Rimoldi, 2003; Haworth, 2003; Nott, 2003; Rao and Reddy, 2004; Huang et al., 2006 have produced base maps providing reliable information for urban planning and land use (Tüdeş et al., 2012). Similarly, Legget (1973, 1969) states that urban geology is the fundamental part of the physical planning. Required urban geology studies for urban and regional planning should: • • • •

• •

Determine the geo hazards, advantages and disadvantages caused by geological formation and geological thresholds to lead the urban planning process. Provide necessary detailed site investigation in order to select urban site and development areas. Provide base geoscientific information to the urban planner for the usage and management of land resources. Provide geological data in the context of urban and regional planning, related with land use planning activities (including the scales, from the top of the planning hierarchy to the bottom of this hierarchy, for ex. for 1/100 000 and 1/25 000 scaled environmental plan, 1/5 000 scaled master plan and 1/1 00 scaled implementation plan). Determine the hydrogeological data which includes the sites of the underground water resources and the boundaries of the catchment basins. Provide data for the site selection of: ◦◦ Organized industrial sites, ◦◦ Treatment facilities, ◦◦ Airports and the other transportation routes and stops in the context of urban and regional scale. ◦◦ Significant engineering structures as tunnels, viaducts etc.

In this sense, the related geological data is ranging from the regional scale to the urban, neighborhood and even parcel scale. The information included in these data is getting more detailed, when reaching to the parcel level. Additionally; • • •

602

If the planning area is located on the land, including active fault zone and in the boundaries of the earthquake zone; the tectonic, seismicity and the other related characteristics of the land should be investigated by considering different earthquake scenarios. In addition to this, Inundation and flood risks should be determined, as well as, flood plain boundaries should be provided to the urban and regional planner in the planning process. Probable mass movements, as landslide, rock fall etc. should be mapped in the context of upper and sub scaled maps.

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• •

• •

Geologically protected area boundaries should be determined and there will be no construction process in these areas. The location of raw materials should be determined and the reserve and economical values of those materials should be identified and the necessary information should be provided to the urban planner. On the other hand, raw material resources will be in the risk of construction processes in the urban areas and this situation might cause the loss of these resources for the future generations. If necessary, geotechnical maps should be produced in order to determine the construction conditions for the 1/1 000 scaled implementation plans in the urban and regional planning process. The characteristics of the ground and underground geological conditions of the planning area should be defined.

Urban geology identifies and evaluates the engineering geological background, natural resources, geological environment, hydrogeological and geomorphological information, as well as natural hazards and construction conditions, which are unique to the city is studied for urban planning. It obtains a wide range of geological maps at a variety of upper, medium and lower scales in accordance with the scale of the urban plan to work by urban planner. When it is necessary, these maps showing the geological constraints on urbanization and construction. In addition, it is produced integrated geo hazard map that weights and combines the different geo hazards by the methods of multi criteria decision making analyses to demonstrate overall geo hazard types were determinated. As Jonas (2006) stated, urban geology should provide detailed geoscientific data and information for development, regeneration, proper site selection, land use planning and sustainability in urban areas for urban planners. Jonas (2006) proposes which studies should be conducted in the context of urban planning related with the urban geology in its special publication (Anon, 2006b). These studies are classified depending on the geological background of the related urban area. In addition to this, advisory proposals (Anon, 2006b)related with this issue were modified and given in Table 2. After all also geoscientific information which is required by the urban and regional planners in the context of analysis, synthesis, site selection, land use planning etc. purposes were given inTable 2. Professional geologists even give improved value to public and private foundation during land use development, hazard and risk analyses, mitigation, emergency planning and process, natural disaster response and recovery.Culshaw and Price (2011) have compiled the various definitions of urban geology, made by different researchers, in their book titled as “The contribution of urban geology to the development, regeneration and conservation of cities”. The definitions included in this book could be stated as: Since the science of geology is concerned with all aspects of the crust of the Earth, the use of geological information, and of geological methods to obtain new information about local sub surface conditions, should therefore be an essential part of the physical planning of all cities. (Legget, 1973,1969; Culshaw, 2011) The study of land resources and geologic hazards as they relate to the development and expansion of urban areas. (Dai et al., 1994; Culshaw, 2011) an interdisciplinary field in the geo- and socio-economic sciences addressing Earth-related problems in urbanized areas. (De Mulder, 1996; United Nations, 1996a; Culshaw and Price, 2011)

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spans both regional and applied geology. Some emphasis is usually assumed in the application of geological principles and knowledge to the solution of construction, and environmental problems in or near urban areas. (Karrow and White, 1998b; Culshaw and Price, 2011) Engineers, planners, decision makers, and the general public with the geoscience information required for sound regional planning in densely populated areas. (Anon, 2008; Culshaw and Price, 2011) Tüdes et al. (2012) states in their article titled as “Geoenvironmental evaluation for planning: an example from Gümüşhane city, close to the North Anatolia Fault Zone, NE Turkey”, that geoenvironmental assessments for planning have been gaining in importance since studies of urban geology began in the 1960s. “Urban geology as the study of land resources and geological hazards which are related to the development, redevelopment and expansion of urban areas” (Fuchu et al., 1995; Tüdes et al., 2012). Culshaw and Price (2011) define urban geology as “The study of the interaction of human and natural processes with the geological environment in urbanized areas, and the resulting impacts, and the provision of the necessary geo-information to enable sustainable regeneration and conservation”.With a comprehensive defination, it can be said that urban geology is a sub discipline in applied geology, providing geoscientific information on urban environments as a base for urban and regional planners, civil engineers, architects, environmental engineers and experts of real estate development for rational land use planning, urban development, proper site selection, urban design, building and construction.

GEOLOGICAL HAZARDS IN URBAN PLANNING Geological hazards involve slope stability problems, subsidence, seismic hazards such as earthquake hazards, volcanic hazards etc. These geological hazards find out some troubles for construction and development. Therefore, they must be properly regarded in land use planning. To implement geologic hazard mitigation planning contains important phases, evaluating and mapping the hazards. Most geologic hazards find out different degree of the risks depending on the site selection. This degree must be determined spatially and perform mitigation proceeding appropriately. By way of example in Turkey, Marmara Region is susceptible to earthquake risk, but some specific urban areas have a greater ground shaking and damage risk due to distinctive subsurface geology and geotechnic and geomechanic properties of the land (Figure 3) as an example it was presented that demage and destruction due to local geological conditions and liquefaction in 1999 Marmara Earthquake in the spesific region in the Figure 3. Consequently soil amplification map and potential liquefaction maps should be prepared as a base for urban plans. Esin and Ceryan (2015), the other example, prepared the liquefaction susceptibility map of Burhaniye settlement area according to Sonmez (2003) method based on deterministic approach. In the said study, considering the distance from the study area investigated and the length of the active faults affecting the Burhaniye (Balıkesir) settlement area, maximum ground acceleration was calculated by the attenuation relationship. The liquefaction susceptibility map of Burhaniye settlement area maps were created using earthquake scenario of Mw=7.2 affecting the said settlement area and the value of probable maximum ground acceleration, 0.37g (Figure 4). Steep sloping areas have a much more risk than others, depending on geo morphology and physical, geo mechanical, geo technic properties of the subsurface materials. This relatively risk rating must be

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Figure 3. Buildings collapsed at the 1999 Marmara Earthquake in Turkey (http://www.habervaktim. com/foto-galeri/17-agustos-1999-marmara-depremi-3609-p3.htm)

Figure 4. Liquefaction potential map of the Burhaniye settlement area (Balıkesir, Turkey). (Esin and Ceryan 2015)

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Table 2. The summary of essential geoscientific information and data, which may change depending on the geological backround of each urban area, for Urban Planner (Jonas, 2006 modified). Essential main geosientific componenet

Necessary geoinformation and methods on understanding of these components for urban planning Structural geology, Neotectonics, basin analysis

Industrial raw materials, mineral deposits, geological hazards

Basement rocks

Depth, thickness, spatial spreading and stratigraphy of basement rocks

Sedimentary rocks

Industrial raw materials, karstification, water supply

Physiographic region (with geotechnical practice implications)

Spatial distribution, hydrogeological condition, morphology, structural geology elements

Surficial geologic and soil units

Transport- accumulation areas, groundwater condition, problematic soil

Stratigraphic chart with basic engineering characteristics

Rock mass structure, discontinuity properties, weathering conditions, erodobility, hydrogeological conditions,

General foundation-related geologic units

Rock mass structure, frequency of discontinuity, solubility in water in of geological units and clayey soil and other problematic soil

Failure of rock foundation for heavy engineering structure, karstification, liquefaction, collapse, swelling,

Exploration methods

mapping at 1 / 5000-1 / 1000 scale mapping for geotechnical units, drilling, geophysical studies, field experiments, hydrogeological maps

Lateral and vertical change of lithological and engineering characteristic of geotechnical units, ground water condition

Typical foundation types in use

Engineering behavior of the soil and rocks in static and dynamic condition, soil-structure interaction analysis, groundwater depth

bearing capacity, ground and soil improvement

General laboratory test methods

Undisturbed and disturbed sampling, experiments to identify, classify and determine engineering behavior of rock materials and soil

Petrographic, index and physicomechanical properties of rock material and soil, engineering classification of rock materials and soil

Regionally important geologic materials(RIMs; those exhibiting unusual properties/characteristics of a negative geotechnical nature)

Problematic soils (rapid clay, sheeting clay, sedimentary soils, soils with liquefaction potential high, etc.), water-soluble rocks such as limestone evaporates, mineralization that reduce the usability of waters and soils

Quality water, areas that are not suitable for settlement or require precautions for settlement, areas that are harmful for health,

Regionally important geologic anomalies

Anomaly of elements harmful to human health or radioactive element in rock, soil or water. mineral deposits

Medical geology investigation, the reserves of mines and industrial materials,

Traditional types and uses

The amount of material, convenience in extraction and use, suitability to topographic and climatic conditions

Existing open-pit or closed-pit mining activities, durability in environmental condition

Sources and extraction methods

Drilling, excavation by exploding, geophysical methods, open-pit or closed-pit mining activities,

Negative influences of historical monuments, settlements and water basins

Regulations and zoning affecting extraction and closure

Urban law, public interest, protection of water basin, expropriation, local government planning,

proximity to the settlement area, effect on historical monuments, site areas, effects on water basin,

Environmental impact of extraction

evaluation of environmental impact

Environmental pollution, effect on engineering structures, stability of slope, Improvement and re-evaluation of mining area and contaminated areas

Brief on regional geology

Geologic setting Geology of the city

Geotechnical characteristics

Geotechnical characteristics

Materials of construction

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Table 2. Continued Essential main geosientific componenet

Geologic constraints

Geologic constraints

Necessary geoinformation and methods on understanding of these components for urban planning Classification (e.g. ground instability; loss of ground, subsidence, unstable soils and/or weak rock units, volcanic eruptions, tsunami, etc.) Geologic elements of hazards detection and warning systems and loss–reduction applications Geologic aspects of natural risk

Geotechnical microzonation arrays, sensitivityhazard risk analysis

Disaster information system, risk management

Unstable soil and weak rock

Collapse-prone soils Expansive soils Slaking weak rock

Loss-of-ground phenomena

Subsidence (natural ground and of underground workings) Karstic ground failure Mass movement

Geologic effects of violent weather

Debris-flows Storm-induced slope failures Wildfire suppression

Earthquake-induced geologic effects

Ground-motion amplification Liquefiable soils Tsunami

Geologic effects of volcanism

Volcanic eruptions Ash falls Pyroclastic flows Geologically-channeled floods Lava flows Lahars Toxic gas clouds

Recurrence and forecasting

Obtaining mechanism from literature study, field survey and utilization of related database. Deterministic and probability methods for estimation of frequency. Disaster information system

Classification and nature of threat

Mitigation of risk(presenting only geologic considerations and effects)

Geological hazard maps, the population and engineering structure falling in the area for each hazard level, precautions to be taken to reduce the geological hazards disaster information system and risk management

Planning for disaster response

History

Technological uncertainties, to the existence of economic and institutional biases against recovery, and to the lack of financial assistance for planning and implementation

Practical estimation of recurrence intervals Uses of forecasting and predictions in loss reduction or avoidance

Response techniques (to include planning, preparedness, mitigation, and evacuation) Post-event recovery and mitigation

Resource Recovery

Classification of resources (water, industrial minerals, petroleum)

Waste planning

Areal extent of each resource Constraints to resource recovery Mitigation of recovery effects

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Table 2. Continued Essential main geosientific componenet Seismicity of the city

Necessary geoinformation and methods on understanding of these components for urban planning Historic record Notable events Generalized recurrence interval Ground motion amplification factors

Presence and mechanisms of active faults, Paleo-seismological studies, earthquake databases, Earthquake magnitude, attenuation relationships, dynamic characteristics of the soil

Seismic microzonation, groundstructure interaction, measures to reduce risk

Resource Conservation and Recovery

Control hazardous waste including the generation, transportation, treatment, storage, and disposal of hazardous waste

Loss of ground (e.g. liquefaction, hillside failures) Seismic design provisions in force(legislation, codes and other forms of geologically-based risk mitigation measures) Environmental concerns

Water supply (surface and subsurface sources, reclamation of water) Wastewater treatment Waste management (solid, special, hazardous and radioactive) Remediation of uncontrolled hazardous waste sites

Historic dump sites Contaminated sediment Contaminated water (surface and subsurface) Remediation case history briefs of notable site cleanups (uncontrolled hazardous waste sites remediated under national laws) Brownfield redevelopments

Reclamation of mined ground

Mining permitting, reclamation and closure issues

Discharges into aquifers and surface waters Monitoring and mitigation Asbestiform and other toxic mineralization Stability of beneficiation slimes and tailings dams

Low- and high-level radioactive waste and stored ores and concentrates Applicable government programs in force Wetlands factor Flooding Shoreline erosion

Geologic conditions susceptible to erosion Geologic parameter influential to mitigation design

Environmental concerns

Sea level changes

Impacts on land use

Salt water intrusion into fresh water aquifers Inundation of infrastructure, farm lands, and structures

Geologically-based mitigative techniques Major engineered structures

Being affected by the earthquake geological hazard and ground conditions

Geomorphological state, hydrogeological conditions and geological environment of the construction site, structure-ground interaction

Hazard and risk analysis

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Table 2. Continued Essential main geosientific componenet Use of underground space

Necessary geoinformation and methods on understanding of these components for urban planning Introduction and history Water supply conveyance tunnels Transportation routing tunnels Sewerage and/or flood control tunnels Commodity storage caverns Energy storage caverns (natural gas, petroleum, compressed air) National defense caverns

Geologic parameters attendant to socio-political conditions

Living space, natural hazards, vulnerability, and acceptable risk Moving people (to and from employment locations) Complex emergencies and natural resources and hazards; past and present Related effects of warfare or other anthropogenic calamities Global climate change impacts as known and projected through 2100 A.D. Human migration affected by changes in natural resources, natural hazards or changes in the sustainability of locations versus population needs

described and preventions to reduce all risks which restrict urban environmental development, sustainable and sensitive disaster vulnerability must be applied by building solid structures and proper selecting land for construction. Consequently, always it is needed geologic and geo scientific data and information to reduce vulnerability in urban areas and the environment. Unfortunately, these data, information and maps are not available even for the whole urban area in the developed countries. The maps which will base for land use and development must involve identifying geologic formations, the degree of consolidation materials, and the places of areas which have mine, landslide, faults, subsidence, karst, geo-heritage, mass movements. They must explain geologic history, mineral resource reserve of the area and all the geologic factors affecting land development.

Landslide Definition and Importance of Slope Stability Slope failures occur when the gravitational force of slope materials exceeds the resisting forces of friction, strength, and cohesion of the supporting materials” (Randolph, 2004). In sloped regions have some geological conditions, relation between geological and morphological properties, such as steepness, layer structure and gradient of slope for bedded rocks, fracturing of the magmatic rock, physical mechanical properties of the soil, can occur slope failure. In addition, if moisture, water, additional land (such as construction on the slope), and undercutting of the heel of the slope existing in the setting; slope failure can take place quickly. Conditions of failure can develop by naturally or humans’ activities. The conceptes related slope stability are briefly defined at the below:

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• • • •

Slide: Broken and weathered rock mass and soil moves down slope failuring the gravity by sliding on a surface Fall: Rock mass, soil or a combination of both moves downslope by undercutting and toppling Slump: Weathered rock or loosely consolidated material moves a short distance down a slope Flow: Can be identified by the velocity of movement and by the materials, debris avalanches included

Each year avalanches, landslides, debris flows and the other mass movements give rise to disasters in all over the world. For example, there were 19 745 people lost their lives and 17 230 million dollar economic loss in Turkey between the years of 1992 and 1999, due to the geologic hazards, as earthquakes, avalanches, debris flows, floods etc. (JICA, 2004). Slope failures occur when the gravitational force of slope materials exceeds the resisting forces of friction, strength, and cohesion of the supporting materials” (Randolph, 2004).The examples for landslides affecting the urban settlement area were given in Figure 5.

Landslide Map for Land Use and Planning Various types of maps are used to demonstrate hazard from landslides. These maps can be simple or complex. For example, some show the locations of old landslides to specify possible instability to avoid being construction and land development. However, complex quantitative landslide maps comprise probabilities in the way that slope angle, cohesion, friction angle, rainfall, material type, and levels of earthquake shaking. There are some kinds of these maps which identify and indicate hazards.

Figure 5. Examples for landslides affecting the settlement; the mudflow in May 2015 in Meiyu-baiu city, China (a), the Rockfall in Gumushane NE Turkey, in April 2016 (b) Plane failures of rock slope in Araklı-Trabzon NE Turkey(from Ceryan 2009) in October 2006 and in Gumushane NE Turkey in August 2008 (c) and (d), respectively

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Landscape inventory maps demonstrate landslide site dimensions, geographical locations of all landslide in the region or areas. These maps are important in all scales of the study of urbanization especially for regional planning, urban planning, land development. The distribution of old movement refers the site of future land sliding. Inventory maps do not determine the landslide probability of the field. A landslide inventory map includes dataset give landslide locations. Small scale maps (upper scale) do not contain detailed data to categorize distinctive types of landslides. However, largerscale (sub scale) maps differentiate sources from deposits and kinds of landslides. Accordingly, in planning, landslide inventory maps are used for small scale regional planning or upper scaled strategic planning as a base. Whereas, susceptibility,hazardand risk maps usually can be base for upper scale urban planning, such as master development plans and implementery development plans.The purpose of slope instability zonation or susceptibility mapping is to highlight the regional distribution of potentially unstable slopes based on a detailed study of the factors responsible for landslide (Aleotti and Chowdhury 1999; Ayalew et al. 2005).For example, Ceryan and Ceryan (2008) presented the slope instability zonation map for the Dogankent area, Giresun, NE Turkey. using Rock Engineering System. In the said study, water condition,s weathering, shear strength parameters, slope angle, vegetation density, distance from faults and shear zones well-developed, and discontinuity frequency were used as input parameters to obtain susceptibility of slope (Figure 6). Landslide hazard maps point out the probability of landslides taking place in a particular area. The Landslide hazard maps must demonstrate the possibility of moving down of the slope at a particular period. Harp et al. (2006) used the function of factor of safety to determine the distribution of shallow Figure 6. The landslides susceptibility maps of Dogankent area NE Turkey (1: very low and low, 2: medium, 3: high-very high; soil slope, 4: very low and low, 5: medium, 6: high-very high) (Ceryan and Ceryan, 2008)

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landslide concentration values and to establish relative hazard categories for shallow landslide source areas for Seattle in USA (Figure 7). They divided into three obvious categories of hazard in landslide hazard map for southwest portion of Magnolia area in Seattle. These categories: 1. Highest relative hazard category (Fs=0.5-1.5 (>75 shallow landslides / km2) 2. Medium relative hazard category (Fs=0.5-1.5 (>75 shallow landslides / km2) 3. Low relative hazard category (Fs>2.5, (