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Metaheuristics in Water, Geotechnical and Transport Engineering
- 1st Edition - September 1, 2012
- Editors: Xin-She Yang, Siamak Talatahari, Amir Hossein Alavi
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 3 9 8 2 9 6 - 4
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 2 8 2 6 0 - 4
- eBook ISBN:9 7 8 - 0 - 1 2 - 3 9 8 3 1 7 - 6
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 often 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 water, geotechnical and transport engineering offering practical case studies as examples to demonstrate real world applications. Topics cover a range of areas within engineering, including reviews of optimization algorithms, artificial intelligence, cuckoo search, genetic programming, neural networks, multivariate adaptive regression, swarm intelligence, genetic algorithms, ant colony optimization, evolutionary multiobjective optimization with diverse applications in engineering such as behavior of materials, geotechnical design, flood control, water distribution and signal networks. This book can serve as a supplementary text for design courses and computation in engineering as well as a reference for researchers and engineers in metaheursitics, optimization in civil engineering and computational intelligence.
- Provides detailed descriptions of all major metaheuristic algorithms with a focus on practical implementation
- Develops new hybrid and advanced methods suitable for civil engineering problems at all levels
- Appropriate for researchers and advanced students to help to develop their work
Academic researchers and lecturers in civil engineering and computer sciences as well as industrial practitioners.
List of contributors
1. Optimization and Metaheuristic Algorithms in Engineering
1.1 Introduction
1.2 Three Issues in Optimization
1.3 Metaheuristics
1.4 Artificial Neural Networks
1.5 Genetic Programming
References
PART ONE: Water Resources
2. A Review on Application of Soft Computing Methods in Water Resources Engineering
2.1 Introduction
2.2 Soft Computing Techniques
2.3 Implementation of Soft Computing Techniques
2.4 Conclusion
Acknowledgments
References
3. Genetic Algorithms and Their Applications to Water Resources Systems
3.1 Introduction
3.2 Genetic Algorithms
3.3 Review of GA Applications to Water Resource Problems
3.4 The GA Process for a Reservoir Operation Problem
3.5 Conclusions
References
4. Application of the Hybrid HS–Solver Algorithm to the Solution of Groundwater Management Problems
4.1 Introduction
4.2 Development of the Hybrid HS–Solver Algorithm
4.3 Formulation of the Management Problem
4.4 Numerical Applications
4.5 Conclusions
Acknowledgments
References
5. Water Distribution Networks Designing by the Multiobjective Genetic Algorithm and Game Theory
5.1 Introduction
5.2 The Objectives of WDN Optimization
5.3 The Hydraulic of WDN
5.4 Basic Concepts: GA, Multiobjective Optimization, and Game Theory
5.5 Methodology
5.6 Case Study
5.7 The Biobjective Optimization Problem
Acknowledgments
References
6. Ant Colony Optimization for Estimating Parameters of Flood Frequency Distributions
6.1 Introduction
6.2 A Review of Previous Work
6.3 Standard ACO
6.4 Improved ACO
6.5 Other Well-Known Methods of Parameter Estimation
6.6 Frequency Distributions
6.7 Simulation and Application
6.8 Results and Discussion
6.9 Conclusions
References
7. Optimal Reservoir Operation for Irrigation Planning Using the Swarm Intelligence Algorithm
7.1 Introduction
7.2 Literature Review
7.3 Method Description
7.4 Case Study
7.5 Mathematical Modeling
7.6 Results and Discussion
7.7 Conclusions
References
PART TWO: Geotechnical Engineering
8. Artificial Intelligence in Geotechnical Engineering: Applications, Modeling Aspects, and Future Directions
8.1 Introduction
8.2 AI Applications in Geotechnical Engineering
8.3 Overview of AI
8.4 Discussion and Conclusions
References
9. Hybrid Heuristic Optimization Methods in Geotechnical Engineering
9.1 Introduction
9.2 Some Basic Heuristic Optimization Algorithms
9.3 Demonstration of the Coupling Methods
9.4 Application of Coupling Methods in the Slope Stability Problem
9.5 Discussion and Conclusions
Acknowledgment
References
10. Artificial Neural Networks in Geotechnical Engineering: Modeling and Application Issues
10.1 Introduction
10.2 Basic Formulation
10.3 Modeling and Application Issues in General
10.4 Future Challenges
10.5 Conclusions
References
11. Geotechnical Applications of Bayesian Neural Networks
11.1 Introduction
11.2 Neural Networks
11.3 Bayesian Neural Network
11.4 Evolutionary Bayesian Back-Propagation Neural Network
11.5 Examples
11.6 Conclusions
References
12. Linear and Tree-Based Genetic Programming for Solving Geotechnical Engineering Problems
12.1 Introduction
12.2 Previous Studies on Applications of TGP and LGP in Geotechnical Engineering
12.3 Tree-Based Genetic Programming
12.4 Application to Geotechnical Engineering Problems
12.5 Discussion and Future Directions
12.6 Conclusions
References
13. An EPR Approach to the Modeling of Civil and Geotechnical Engineering Systems
13.1 Introduction
13.2 Evolutionary Polynomial Regression
13.3 Data Preparation
13.4 Stability Analysis of Slopes Using EPR
13.5 EPR Modeling of the Behavior of Rubber Concrete
13.6 Application of EPR in Constitutive Modeling of Materials
13.7 Summary and Conclusion
References
14. Slope Stability Analysis Using Multivariate Adaptive Regression Spline
14.1 Introduction
14.2 Method
14.3 Application of MARS to Slope Stability Analysis
14.4 Results and Discussion
14.5 Conclusion
References
PART THREE: Transport Engineering
15. Scheduling Transportation Networks and Reliability Analysis of Geostructures Using Metaheuristics
15.1 Introduction
15.2 Problem Statement and Research Impact
15.3 Metaheuristic Algorithms
15.4 Scheduling Transportation Networks
15.5 Reliability Analysis of Geostructures
15.6 Conclusions
References
16. Metaheuristic Applications in Highway and Rail Infrastructure Planning and Design: Implications to Energy and Environmental Sustainability
16.1 Introduction
16.2 Highway Infrastructure Planning and Design
16.3 Rail Infrastructure Planning and Design
16.4 Discussion of Metaheuristics Commonly Applied in Highway and Rail Infrastructure Planning and Design
16.5 GA Application in Highway and Rail Infrastructure Planning and Design
16.6 GA Application to Rail Infrastructure Planning and Design
16.7 The Ant Highway Alignment Optimization Algorithm
16.8 The Ant Algorithm Applied to the SLO Problem
16.9 Implications to Environment and Energy Sustainability
16.10 Conclusions and Future Works
Acknowledgments
References
17. Multiobjective Optimization of Delay and Stops in Traffic Signal Networks
17.1 Introduction
17.2 Background
17.3 Modifications to NSGA-II Design
17.4 Methodology
17.5 Results
17.6 Conclusion
References
18. An Improved Hybrid Algorithm for Stochastic Bus-Network Design
18.1 Introduction
18.2 The Main Entities of the BNDP: The Operator and the User
18.3 Hybrid Method for Stochastic Bus-Network Design
18.4 Practical Experience
18.5 Conclusions and Future Research Work
Acknowledgments
References
19. The Hybrid Method and its Application to Smart Pavement Management
19.1 Introduction
19.2 Methodology
19.3 Conclusions
References
- No. of pages: 496
- Language: English
- Edition: 1
- Published: September 1, 2012
- Imprint: Elsevier
- Hardback ISBN: 9780123982964
- Paperback ISBN: 9780323282604
- eBook ISBN: 9780123983176
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