Metaheuristics in Water, Geotechnical and Transport Engineering

Metaheuristics in Water, Geotechnical and Transport Engineering

1st Edition - September 1, 2012

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  • Editors: Xin-She Yang, Amir Hossein Gandomi, Siamak Talatahari, Amir Hossein Alavi
  • Paperback ISBN: 9780323282604
  • eBook ISBN: 9780123983176

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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


Product details

  • No. of pages: 496
  • Language: English
  • Copyright: © Elsevier 2012
  • Published: September 1, 2012
  • Imprint: Elsevier
  • Paperback ISBN: 9780323282604
  • eBook ISBN: 9780123983176

About the Editors

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

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

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.

Amir Hossein Alavi

Affiliations and Expertise

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

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