A Primer on Machine Learning Applications in Civil Engineering: 1st Edition (Hardback) book cover

A Primer on Machine Learning Applications in Civil Engineering

1st Edition

By Paresh Chandra Deka

CRC Press

332 pages | 67 Color Illus. | 46 B/W Illus.

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Hardback: 9781138323391
pub: 2019-11-28
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Description

Machine Learning has undergone rapid growth in diversification and practicality, and repertoire of these techniques has evolved and expanded. The aim of this book is to provide a broad overview of the available machine learning techniques which can be utilized for solving civil engineering problems. Fundamentals of both theoretical and practical aspects are discussed in the domains of Water Resources/Hydrological Modelling, Geotechnical Engineering, Construction Engineering and Management, Coastal/Marine Engineering. Complex civil engineering problems such as drought forecasting, river flow forecasting, precipitation forecasting, and significant wave height forecasting are included.

Table of Contents

Chapter 1 INTRODUCTION

    1. Machine Learning
    2. Learning from data
    3. Research in Machine Learning: Recent progress
    4. Artificial Neural Network
    5. Fuzzy Logic
    6. Genetic Algorithm
    7. Support Vector Machine
    8. Hybrid Approach

Chapter 2 ARTIFICIAL NEURAL NETWORKS

2.1 Fundamental concept and Terminologies

2.2 Evolution of Neural Networks

2.3 Models of ANN

2.4 McCulloch-Pitts Neuron

2.5 Hebb network

2.6 Summary

2.7 Supervised Learning Network

2.8 Unsupervised learning networks

2.9 Special networks

2.10 Working principle of ANN

Chapter 3 FUZZY LOGIC

3.1 Introduction to classical sets and fuzzy sets

3.2 classical relations and fuzzy relations

3.3 Membership functions

3.4 Defuzzification

3.5 Fuzzy arithmetic and fuzzy measures

3.6 Fuzzy rule base and approximate reasoning

3.7 Fuzzy decision making

3.8 Fuzzy logic control systems

3.9 Merits and demerits of fuzzy logic

3.10 Fuzzy rule-based or inference system

Chapter 4 SUPPORT VECTOR MACHINE

4.1 Introduction to Statistical learning theory

4.2 Support vector classification

4.3Multiclass SVM

4.4 Various SVM

4.5 Kernal based methods

4.6Feature selection and extraction

4.7Function approximation

Chapter 5 GENETIC ALGORITHM

5.1Introduction

5.2 Classification of GA

5.3Genetic Programming

Chapter 6 HYBRID SYSTEMS

6.1 Introduction

6.2 Neuro-fuzzy hybrid systems

6.3 Neuro-genetic hybrid systems

6.4 Fuzzy genetic hybrid systems

6.5Summary

Chapter 7 DATA STATISTICS AND ANALYTICS

7.1Introduction

7.2Data analysis-spatial and temporal

7.3Data preprocessing

7.4Presentation of data

Chapter 8 APPLICATIONS IN CIVIL ENGINEERING DOMAIN

8.1Introduction

8.2 In the field of Water Resources and Hydrological Modelling

8.3 In the field of Geotechnical Engineering

8.4 In the field of Construction Engineering and Management

8.5 In the field of Coastal and Marine Engineering

8.6 In the field of Environmental Engineering

8.7 In the field of Structural Engineering

8.8 In the field of Transportation Engineering

8.9 Other Applications

Chapter 9 CONCLUSIONS AND FUTURE WORK

Appendices-Sample code in MATLAB

About the Author

Paresh Deka earned Ph.D. from Indian Institute of Technology, Guwahati, in the year 2004 specializing in Hydrological modelling. Dr Deka worked as faculty at Arbaminch University, Ethiopia and visiting faculty at Asian Institute of Technology, Bangkok. So far, he has supervised eight Phd scholars and five are continuing. Also, he has supervised forty Master thesis and four are continuing. His research area is Soft computing applications in Water Resources Engg. and Management .Dr. Deka has already published four books and five book chapters. He has credit of publishing more than thirty international journal papers of high repute. Dr. Deka is a visiting faculty engaging in short term research interaction at Purdue University, USA. Currently, Dr. Deka is working as Associate Professor in the Deptt. Of Applied Mechanics and Hydraulics at National Institute of Technology, Surathkal, Karnataka.

Subject Categories

BISAC Subject Codes/Headings:
COM037000
COMPUTERS / Machine Theory
MAT004000
MATHEMATICS / Arithmetic
TEC009020
TECHNOLOGY & ENGINEERING / Civil / General