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دانلود کتاب Metaheuristic Optimization Algorithms in Civil Engineering: New Applications

الگوریتم های بهینه سازی فرا-ابتکاری در مهندسی عمران: برنامه های جدید

Metaheuristic Optimization Algorithms in Civil Engineering: New Applications

مشخصات کتاب

Metaheuristic Optimization Algorithms in Civil Engineering: New Applications

دسته بندی: کامپیوتر
ویرایش:  
نویسندگان:   
سری: Studies in Computational Intelligence, 900 
ISBN (شابک) : 303045472X, 9783030454722 
ناشر: Springer 
سال نشر: 2020 
تعداد صفحات: 382 
زبان: English 
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) 
حجم فایل: 14 مگابایت 

قیمت : 36000 تومان



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فهرست مطالب

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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