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Metaheuristic Applications in Structures and Infrastructures
- 1st Edition - January 31, 2013
- Editors: Xin-She Yang, Siamak Talatahari, Amir Hossein Alavi
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 9 8 3 6 4 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 9 8 3 7 9 - 4
Due to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low-cost structures has become cr… Read more
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Request a sales quoteDue to an ever-decreasing supply in raw materials and stringent constraints on conventional energy sources, demand for lightweight, efficient and low-cost structures has become crucially important in modern engineering design. This requires engineers to search for optimal and robust design options to address design problems that are commonly large in scale and highly nonlinear, making finding solutions challenging. In the past two decades, metaheuristic algorithms have shown promising power, efficiency and versatility in solving these difficult optimization problems.
This book examines the latest developments of metaheuristics and their applications in structural engineering, construction engineering and earthquake engineering, offering practical case studies as examples to demonstrate real-world applications. Topics cover a range of areas within engineering, including big bang-big crunch approach, genetic algorithms, genetic programming, harmony search, swarm intelligence and some other metaheuristic methods. Case studies include structural identification, vibration analysis and control, topology optimization, transport infrastructure design, design of reinforced concrete, performance-based design of structures and smart pavement management. With its wide range of everyday problems and solutions, Metaheursitic Applications in Structures and Infrastructures can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheuristics, optimization in civil engineering and computational intelligence.
- Review of the latest development of metaheuristics in engineering.
- Detailed algorithm descriptions with focus on practical implementation.
- Uses practical case studies as examples and applications.
Advanced students, researchers and professional engineers in metaheursitics, optimization and computational intelligence and computational methods in civil engineering.
List of Contributors
1. Metaheuristic Algorithms in Modeling and Optimization
1.1 Introduction
1.2 Metaheuristic Algorithms
1.3 Metaheuristic Algorithms in Modeling
1.4 Metaheuristic Algorithms in Optimization
1.5 Challenges in Metaheuristics
References
2. A Review on Traditional and Modern Structural Optimization: Problems and Techniques
2.1 Optimization Problems
2.2 Optimization Techniques
2.3 Optimization History
2.4 Structural Optimization
2.5 Metaheuristic Optimization Techniques
References
3. Particle Swarm Optimization in Civil Infrastructure Systems: State-of-the-Art Review
3.1 Introduction
3.2 Particle Swarm Optimization
3.3 Structural Engineering
3.4 Transportation and Traffic Engineering
3.5 Hydraulics and Hydrology
3.6 Construction Engineering
3.7 Geotechnical Engineering
3.8 Pavement Engineering
3.9 PSO Applications in Other Civil Engineering Fields
3.10 Concluding Remarks
References
Part One: Structural Design
4. Evolution Strategies-Based Metaheuristics in Structural Design Optimization
4.1 Introduction
4.2 Literature Survey
4.3 The Structural Optimization Problem
4.4 Problem Formulations
4.5 Metaheuristics
4.6 39-Bar Truss—Test Example
4.7 Conclusions
References
5. Multidisciplinary Design and Optimization Methods
5.1 Introduction
5.2 Coupled Multidisciplinary System
5.3 Classifications of MDO Formulations
5.4 Single-Level Optimization
5.5 Multilevel Optimization
5.6 Optimization Algorithms
5.7 High-Fidelity MDO Using Metaheuristic Algorithms
5.8 Test Problem
5.9 Conclusions
References
6. Cost Optimization of Column Layout Design of Reinforced Concrete Buildings
6.1 Introduction
6.2 Statement of the Problem
6.3 Formulation in a New Space
6.4 The Optimization Problem
6.5 ACO Algorithm for Column Layout Optimization
6.6 Conclusions
References
7. Layout Design of Beam–Slab Floors by a Genetic Algorithm
7.1 Introduction
7.2 A Representation of Beam–Slab Layouts
7.3 A Representative Optimization Problem
7.4 A GA for Beam–Slab Layout Design
7.5 Examples
7.6 Future Challenges
References
8. Optimum Design of Skeletal Structures via Big Bang–Big Crunch Algorithm
8.1 Introduction
8.2 Statement of the Optimization Design Problem
8.3 Review of the Utilized Methods
8.4 The Proposed Method
8.5 Design Examples
8.6 Concluding Remarks
References
9. Truss Weight Minimization Using Hybrid Harmony Search and Big Bang–Big Crunch Algorithms
9.1 Introduction
9.2 Statement of the Weight Minimization Problem for a Truss Structure
9.3 Harmony Search
9.4 Big Bang–Big Crunch
9.5 Simulated Annealing
9.6 Description of Test Problems
9.7 Results of Sensitivity Analysis
9.8 Results of the Large-Scale Optimization Problem
9.9 Summary and Conclusions
References
10. Graph Theory in Evolutionary Truss Design Optimization
10.1 Introduction
10.2 Truss Design
10.3 Graph Theory
10.4 Evolutionary Algorithm
10.5 Application
10.6 Conclusions
References
11. Element Exchange Method for Stochastic Topology Optimization
11.1 Introduction
11.2 Overview of Topology Optimization Methods
11.3 Element Exchange Method
11.4 EEM Application
11.5 Influence of EEM Operations and Parameters on Optimization Results
11.6 Conclusion
References
Part Two: Structural Control and Identification
12. Evolutionary Path-Dependent Damper Optimization for Variable Building Stiffness Distributions
12.1 Introduction
12.2 Concept of Adaptive Sensitivity
12.3 Structural Model with Passive Dampers
12.4 Critical Excitation for Variable Design
12.5 Optimal Design Problem
12.6 Optimality Conditions
12.7 Solution Procedure of Optimal Design Problem
12.8 Numerical Examples
12.9 Conclusions
Acknowledgments
References
13. Application of Genetic Algorithms in Ground Motion Selection for Seismic Analysis
13.1 An Introduction to Structural Nonlinear Response-History Analysis
13.2 A Snapshot of the Genetic Algorithm as One of the Popular Metaheuristics
13.3 Code-Conforming Ground Motion Selection
13.4 Ground Motion Record Selection in PBEE
13.5 Conclusions
References
14. Optimization of Tuned Mass Damper with Harmony Search
14.1 Introduction
14.2 A Passive Structural Control Device: Tuned Mass Damper
14.3 Optimization of TMDs with HS
14.4 Numerical Examples
14.5 Conclusion
Acknowledgments
References
15. Identification of Passive Devices for Vibration Control by Evolutionary Algorithms
15.1 Introduction
15.2 Parametric Identification of Fluid Viscous Dampers
15.3 Differential Evolution Algorithms
15.4 Particle Swarm Optimization Algorithms
15.5 Viscous Damper Identification Using Experimental Data
15.6 Conclusions
Acknowledgment
References
16. Structural Optimization for Frequency Constraints
16.1 Introduction
16.2 Formulation of a Structural Optimization Problem with Frequency Constraints
16.3 Formulation of Optimization Problem of an Arch Dam with Frequency Constraints
16.4 Metaheuristics
16.5 Neural Networks
16.6 Numerical Examples
16.7 Conclusions
References
17. Optimum Performance-Based Seismic Design of Frames Using Metaheuristic Optimization Algorithms
17.1 Introduction
17.2 A Brief Review of Metaheuristic Algorithms
17.3 Statement of Seismic Design of Frames
17.4 Pushover Analysis for Performance-Based Design
17.5 Utilized Metaheuristic Algorithms
17.6 Design Examples
17.7 Concluding Remarks
References
18. Expression Programming Techniques for Formulation of Structural Engineering Systems
18.1 Introduction
18.2 Genetic Programming
18.3 Application to Structural Engineering Problems
18.4 Model Validity
18.5 Conclusions
References
19. An Evolutionary Divide-and-Conquer Strategy for Structural Identification
19.1 Introduction
19.2 Recent Studies on Sub-SI
19.3 Multifeature GA
19.4 Divide-and-Conquer-Based Structural Identification
19.5 Numerical Study
19.6 Applications to Local Damage Detection
19.7 Experimental Verification
19.8 Conclusions
References
Part Three: Construction Management and Maintenance
20. Swarm Intelligence for Large-Scale Optimization in Construction Management
20.1 Introduction
20.2 SI-Based Optimization Algorithms
20.3 Experiments and Discussion
20.4 Conclusions
References
21. Network-Level Infrastructure Management Based on Metaheuristics
21.1 Introduction
21.2 Problem Description
21.3 Ant Colony Optimization
21.4 Genetic Algorithms
21.5 Test Case
21.6 Test Results
21.7 Summary and Outlook
Acknowledgments
References
22. Large-Scale Maintenance Optimization Problems for Civil Infrastructure Systems
22.1 Introduction
22.2 Large-Scale Maintenance Optimization Problem
22.3 Metaheuristic Solution Approaches
22.4 Case Studies
22.5 Summary
References
23. Metaheuristic Applications in Bridge Infrastructure Maintenance Scheduling Considering Stochastic Aspects of Deterioration
23.1 Introduction
23.2 Deterioration Modeling
23.3 Solution Algorithm
23.4 Experimental Procedure
23.5 Evaluation of FRP Composite Materials in Bridge Applications
23.6 Application of AFRP Bars in a Full-Scale Bridge Deck Slab
23.7 Experimental Results
23.8 Discussion
Acknowledgments
References
- No. of pages: 568
- Language: English
- Edition: 1
- Published: January 31, 2013
- Imprint: Elsevier
- Hardback ISBN: 9780123983640
- eBook ISBN: 9780123983794
XY
Xin-She Yang
Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).
Affiliations and expertise
School of Science and Technology, Middlesex University, UKST
Siamak Talatahari
Dr. Siamak Talatahari received his Ph.D degree in Structural Engineering from University of Tabriz, Iran. After graduation, he
joined the University of Tabriz where he is presently Professor of Structural Engineering. He is the author of more than 100 papers
published in international journals, 30 papers presented at international conferences and 8 international book chapters. Dr. Talatahari
has been recognized as Distinguished Scientist in the Ministry of Science and Technology and as Distinguished Professor at the
University of Tabriz. He also teaches at the Yakin Dogu University, Nicosia, Cyprus. In addition, he is a co-author with our author
Xin-She Yang of Swarm Intelligence and Bio-Inspired Computation: Structural Optimization Using Krill Herd Algorithm;
Metaheuristics in Water, Geotechnical and Transport Engineering, and Metaheuristic Applications in Structures and
Infrastructures, all published by as Insights by Elsevier.
Affiliations and expertise
Department of Civil Engineering, University of Tabriz, Tabriz, Iran.
School of Civil and Environment Engineering, University of New South Wales, Sydney, Australia.AA
Amir Hossein Alavi
Amir Hossein Alavi is an Assistant Professor in the Department of Civil and Environmental Engineering, and holds courtesy appointments in the Department of Bioengineering and Department of Mechanical Engineering and Materials Science, at the University of Pittsburgh, United States. His multidisciplinary scientific studies are organized around three research thrusts: 1) mechanics and electronics of multifunctional materials and structures, 2) embedded self-powered sensing systems, and 3) data-driven characterization, design and discovery of engineering systems.
Affiliations and expertise
Department of Civil and Environmental Engineering, Department of Bioengineering, University of Pittsburgh, Pittsburgh, USA