Metaheuristic Optimization Algorithms in Civil Engineering: New Applications 303045472X, 9783030454722

Table of contents :
Preface
Acknowledgements
Contents
1 Introduction
1.1 Engineering Design and Optimization
1.2 Application of Metaheuristic Optimization Algorithms in Civil Engineering
1.3 Organization of the Present Book
References
2 Optimum Stacking Sequence Design of Composite Laminates for Maximum Buckling Load Capacity
2.1 Introduction
2.2 Theoretical Framework
2.3 Problem Statement
2.4 Optimization Algorithms
2.4.1 JAYA Algorithm
2.4.2 Grey Wolf Optimizer
2.4.3 Colliding Bodies Optimization
2.4.4 Salp Swarm Algorithm
2.4.5 Genetic Algorithm
2.4.6 Quantum-Inspired Evolutionary Algorithm
2.5 Anti-optimization Problem
2.5.1 Golden Section Search (GSS)
2.6 Numerical Results for Deterministic Loading
2.6.1 Case 1
2.6.2 Case 2
2.6.3 Case 3
2.6.4 Case 4
2.6.5 Case 5
2.6.6 Case 6
2.7 Numerical Results for Uncertain Loading
2.7.1 A Comparison of the Effect of Different Materials
2.7.2 An Investigation on the Effect of Aspect Ratio
2.7.3 An Investigation on the Effect of Loading Domain
2.7.4 A Comparison Among the Performance of the Different Optimization Algorithms
2.8 Discussions and Conclusion
References
3 Optimum Design of Castellated Beams with Composite Action and Semi-rigid Connection
3.1 Introduction
3.2 Design of Castellated Beams
3.2.1 Flexural Capacity
3.2.2 Shear Capacity
3.2.3 Web Post-buckling
3.3 Design of Composite Beams
3.4 Semi-rigid Connection
3.5 Semi-rigid Composite Castellated Beam
3.5.1 Deflection of Semi-rigid Composite Castellated Beam
3.5.2 The Vibration of Semi-rigid Composite Castellated Beam
3.6 Optimization Algorithms
3.6.1 CBO and ECBO
3.7 Problem Definition
3.7.1 Cost Function
3.7.2 Variables
3.7.3 Constraints
3.7.4 Penalty Function
3.8 Design Examples
3.8.1 Example 1
3.8.2 Example 2
3.8.3 Example 3
3.9 Discussions and Conclusion
References
4 Optimal Design of Steel Curved Roof Frames by Enhanced Vibrating Particles System Algorithm
4.1 Introduction
4.2 Curved Roof Modeling
4.3 Formulation of the Problem
4.3.1 Objective Function
4.3.2 Design Constraints
4.4 Structural Loading
4.4.1 Loading Combinations
4.4.2 The Dead and Collateral Loads (D)
4.4.3 The Live Load (L)
4.4.4 The Balanced and Unbalanced Snow Loads (S)
4.4.5 The Seismic Load (E)
4.4.6 The Wind Loads (W)
4.5 Optimization Algorithms
4.5.1 Vibrating Particles System
4.5.2 Enhanced Vibrating Particles System
4.5.3 Gray Wolf Optimizer
4.5.4 Enhanced Colliding Bodies Optimization
4.5.5 Salp Swarm Algorithm
4.5.6 Grasshopper Optimization Algorithm
4.5.7 Harmony Search
4.6 Design Examples
4.7 Discussions and Conclusion
References
5 Geometry and Sizing Optimization of Steel Pitched Roof Frames
5.1 Introduction
5.2 Problem Definition
5.2.1 Objective Function
5.2.2 Variables
5.2.3 Loading
5.2.4 Structural Analysis
5.2.5 Strength Design Criteria
5.2.6 Displacement Criteria
5.2.7 Penalty Function
5.3 Optimization Algorithms
5.3.1 Simulated Annealing Optimization
5.3.2 Particle Swarm Optimization
5.3.3 Artificial Bee Colony
5.3.4 Whale Optimization Algorithm
5.3.5 Grey Wolf Optimizer
5.3.6 Invasive Weed Optimization
5.3.7 Harmony Search
5.3.8 Colliding Bodies Optimization
5.3.9 Enhanced Colliding Bodies Optimization
5.4 Examples
5.4.1 Example 1
5.4.2 Example 2
5.5 Discussions and Conclusion
References
6 Two-Stage Optimal Sensor Placement Using Graph-Theory and Evolutionary Algorithms
6.1 Introduction
6.2 Sensor Placement Criterions
6.2.1 Modal Assurance Criterion
6.2.2 Visualization of Mode Shapes
6.3 Partitioning Techniques
6.3.1 Preliminaries from Graph Theory
6.3.2 k-Means Method
6.3.3 Spectral Partitioning
6.4 Optimization Methods
6.4.1 Steps of the QEA
6.4.2 The Dynamical Quantum-Inspired Evolutionary Algorithm (DQEA)
6.5 The Proposed Two-Stage Approach
6.5.1 Stage 1 (Structural Partitioning)
6.5.2 Stage 2 (Optimization of Sensor Placement)
6.6 Numerical Results and Discussions
6.6.1 Benchmark Model
6.6.2 Performance of the Methods on TMAC Criterion
6.6.3 Assessing the Mode Shape Visualization Criterion
6.7 Discussions and Conclusion
References
7 The Charged System Search Algorithm for Adaptive Node Moving Refinement in Discrete Least-Squares Meshless Method
7.1 Introduction
7.2 Discrete Least Squares Meshless (DLSM)
7.2.1 Moving Least Squares Shape Functions
7.2.2 Discrete Least-Squares Meshless Method
7.3 Charged System Search
7.4 Error Indicator and Adaptive Refinement
7.5 The Link Between the CSS and Adaptivity
7.5.1 Objective Function
7.5.2 Selected Parameters
7.6 Numerical Examples
7.6.1 Infinite Plate with a Circular Hole
7.6.2 A Cantilever Beam Under End Load
7.7 Discussions and Conclusion
References
8 Performance-Based Multi-objective Optimization of Large Steel Structures
8.1 Introduction
8.2 Employed Multi-objective Optimization Algorithm
8.2.1 NSGA-II-DE
8.2.2 GA Operators
8.2.3 Constraint Handling
8.3 Seismic Optimum Design Procedure
8.3.1 Loading and Constraints for Optimum Seismic Design
8.3.2 Nonlinear Static Analysis (Pushover Analysis)
8.3.3 Lifetime Seismic Damage Cost
8.4 Meta-modeling for Predicting the Response
8.4.1 Approximation Model Selection and Training
8.4.2 Model Management
8.5 The Proposed Framework
8.6 Numerical Results
8.6.1 2D Example
8.6.2 3D Example
8.7 Discussions and Conclusion
References
9 Optimal Seismic Design of Steel Plate Shear Walls Using CBO and ECBO Algorithms
9.1 Introduction
9.2 Different Techniques for Simulating Steel Plate Shear Walls
9.2.1 Strip Models
9.2.2 Pratt Truss Model
9.2.3 Truss Model
9.2.4 Partial Strip Model
9.2.5 Multi-angle Model
9.2.6 Modified Strip Model
9.2.7 Cyclic Strip Model
9.2.8 Orthotropic Membrane Model
9.3 Design Requirements
9.3.1 Requirements for Low Seismic Design
9.3.2 Requirements for High Seismic Design
9.4 CBO and ECBO Algorithms
9.4.1 Colliding Bodies Optimization (CBO)
9.4.2 Enhanced Colliding Bodies Optimization
9.5 Structural Optimization
9.5.1 Optimization Formulation
9.6 Numerical Examples
9.6.1 Low Seismic Design Example
9.6.2 High Seismic Design Example
9.6.3 Performance-Based Design Optimization of SPSW
9.6.4 Optimum Design of 6- to 12-Story SPSW
9.7 Discussions and Conclusion
References
10 Colliding Bodies Optimization Algorithm for Structural Optimization of Offshore Wind Turbines with Frequency Constraints
10.1 Introduction
10.2 Configuration of the OC4 Reference Jacket
10.3 Finite Element Model
10.4 Loading Conditions
10.4.1 Wave Loading
10.4.2 Wind Loading
10.4.3 Load Combinations
10.5 The Structural Optimization Problem
10.5.1 Design Variables
10.5.2 Cost Function
10.5.3 Colliding Bodies Optimization Algorithm
10.6 Results
10.6.1 Hydrodynamic Loading
10.6.2 Aerodynamic Loading
10.6.3 Final Results
10.7 Discussions and Conclusion
References
11 Colliding Bodies Optimization for Analysis and Design of Water Distribution Systems
11.1 Introduction
11.2 Water Distribution Network Optimization Problem
11.2.1 Analysis Phase
11.2.2 Design Phase
11.3 The Colliding Bodies Optimization Algorithm
11.3.1 Collision Laws
11.3.2 The CBO Algorithm
11.4 A New Algorithm for Analysis and Design of the Water Distribution Networks
11.5 Design Examples
11.5.1 A Two-Loop Network
11.5.2 Hanoi Water Distribution Network
11.5.3 The Go Yang Water Distribution Network
11.6 Discussions and Conclusion
References
12 Optimization of Tower Crane Location and Material Quantity Between Supply and Demand Points
12.1 Introduction
12.2 Problem Statement
12.3 Optimization Algorithms
12.3.1 Colliding Bodies Optimization
12.3.2 Enhanced Colliding Bodies Optimization
12.3.3 Vibrating Particles System
12.3.4 Enhanced Vibrating Particles System
12.3.5 Encoding of Solutions
12.4 Numerical Examples
12.5 Discussion and Conclusions
12.5.1 Results and Discussion on Single Tower Crane Layout
12.5.2 Results and Discussion for the Multi-tower Crane Layout Problem
12.5.3 Discussions and Conclusion
References
13 Optimization of Building Components with Sustainability Aspects in BIM Environment
13.1 Introduction
13.2 Proposed Framework to Opt Desired and Optimum Selection for Building Components
13.2.1 Initial Preparation Phase
13.2.2 Optimization Phase
13.2.3 Efficiency Evaluation Phase
13.2.4 Multi-attributes Decision Making Phase
13.3 Methods Used in the Proposed Framework
13.3.1 Enhanced Non-dominated Sorting Colliding Bodies Optimization (ENSCBO)
13.3.2 Data Envelopment Analysis (DEA)
13.3.3 The Compromise Ranking Method VIKOR
13.4 Implementation of a Case Study and the Corresponding Results
13.5 Discussions and Conclusion
References
14 Multi-objective Optimization of Construction Site Layout
14.1 Introduction
14.2 Methodology
14.2.1 Optimization Metaheuristic Algorithms
14.2.2 Data Envelopment Analysis
14.3 Case Study and Discussion of Results
14.3.1 Description of the Case Study
14.3.2 Results
14.4 Discussions and Conclusion
References
15 Multi-objective Electrical Energy Scheduling in Smart Homes Using Ant Lion Optimizer and Evidential Reasoning
15.1 Introduction
15.2 Methodology
15.2.1 Preparing Required Information About Appliances Scheduling Operation
15.2.2 Multi-objective Optimization (MOO)
15.2.3 Multi-criteria Decision Making (Shannon’s Entropy)
15.2.4 Evidential Reasoning
15.3 The Multi-objective Home Appliance Scheduling Problem
15.3.1 Objective Functions
15.4 Implementation of the Proposed System
15.4.1 Numerical Example
15.4.2 Parameter Configuration
15.4.3 Pareto Selection
15.4.4 Determining the Weights
15.4.5 Ranking Solutions
15.4.6 Discussions
15.5 Conclusion
Appendix
References

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