Metaheuristics in Water, Geotechnical and Transport Engineering - 1st Edition - ISBN: 9780123982964, 9780123983176

Metaheuristics in Water, Geotechnical and Transport Engineering

1st Edition

Editors: Xin-She Yang Amir Hossein Gandomi Siamak Talatahari Amir Hossein Alavi
eBook ISBN: 9780123983176
Hardcover ISBN: 9780123982964
Paperback ISBN: 9780323282604
Imprint: Elsevier
Published Date: 1st September 2012
Page Count: 496
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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 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.

Key Features

  • 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.

Table of Contents

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


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



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


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



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



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


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


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


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



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


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


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


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


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


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


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



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


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



19. The Hybrid Method and its Application to Smart Pavement Management

19.1 Introduction

19.2 Methodology

19.3 Conclusions



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About the Editor

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

Amir Hossein Gandomi

Affiliations and Expertise

The University of Akron,USA

Siamak Talatahari

Affiliations and Expertise

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