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Metaheuristic Applications in Structures and Infrastructures - 1st Edition - ISBN: 9780123983640, 9780123983794

Metaheuristic Applications in Structures and Infrastructures

1st Edition

Editors: Amir Hossein Gandomi Xin-She Yang Siamak Talatahari Amir Hossein Alavi
eBook ISBN: 9780123983794
Hardcover ISBN: 9780123983640
Imprint: Elsevier
Published Date: 31st January 2013
Page Count: 568
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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 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.

Key Features

  • 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.

Table of Contents

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


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


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


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


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


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


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


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


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


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


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


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



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


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



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



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


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


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


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


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


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



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


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




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© Elsevier 2013
31st January 2013
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About the Editors

Amir Hossein Gandomi

Dr. Amir H. Gandomi is anARC DECRA Fellow at the Faculty of Engineering andInformation Technology, University of Technology Sydney, Australia. Prior to joining UTS, Dr. Gandomi was an Assistant Professor at Stevens Institute of Technology, USA and a Distinguished Research Fellow in BEACON center, Michigan State University, USA. Dr. Gandomi has published over two hundred journal papers and seven books which collectively have been cited 19,000+ times. He has been named as one of the most influential scientific mindsand Highly Cited Researcher (top 1% publications and 0.1% researchers) for four consecutive years, 2017 to 2020. He also ranked 18th in GP bibliography among more than 12,000 researchers. He has served as associate editor, editor and guest editor in several prestigious journals such as AE of SWEVO, IEEE TBD, and IEEE IoTJ. Dr. Gandomi is active in delivering keynotes and invited talks. His research interests are global optimization andbigdata analytics using Machine Learning and evolutionary computations in particular.

Affiliations and Expertise

The University of Akron,USA

Xin-She Yang

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 at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).

Affiliations and Expertise

School of Science and Technology, Middlesex University, UK

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

Professor, Department of Civil Engineering, University of Tabriz, Iran

Amir Hossein Alavi

Affiliations and Expertise

Iran University of Science and Technology, Iran The Institute of Higher Education of Eqbal Lahoori, Iran

Ratings and Reviews