Data Science for Civil Engineering

Data Science for Civil Engineering

by Rakesh K. Jain, Prashant Shantaram Dhotre, Deepak Tatyasaheb Mane, Parikshit Narendra Mahalle
Released May 2023
Publisher(s): CRC Press
ISBN: 9781000873481

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Book description

This book explains use of data science-based techniques for modelling and providing optimal solutions to complex problems in civil engineering. It deals with the basics of data science and essential mathematics and covers pertinent applications in structural and environmental engineering, construction management, and transportation.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Contents
  6. About the Authors
  7. Preface
  8. Chapter 1 Introduction
    1. 1.1 Introduction
      1. 1.1.1 Purpose of Data Science
      2. 1.1.2 BI and Data Science: What's the Difference
      3. 1.1.3 Components of Data Science
    2. 1.2 Data Science: An Overview
      1. 1.2.1 Need for Data Science
      2. 1.2.2 Role of a Data Scientist
      3. 1.2.3 Problems and Solutions Using Data Science
    3. 1.3 Benefits, Challenges, and Applications of Data Science
      1. 1.3.1 Benefits of Data Science
      2. 1.3.2 Challenges of Data Science
      3. 1.3.3 Data Science Life Cycle
      4. 1.3.4 Data Science and Its Applications
    4. 1.4 Data Science and Civil Engineering: Opportunities
    5. 1.5 Summary of the Book
    6. References
  9. Chapter 2 Mathematical Foundation for Data Science
    1. 2.1 Linear Algebra
      1. 2.1.1 Vector Space
      2. 2.1.2 Vector Norm
      3. 2.1.3 Linearly Dependency and Independency of Vectors
      4. 2.1.4 Basis and Dimensions of Vectors Spaces
      5. 2.1.5 Linear Transformation (Linear Mapping)
      6. 2.1.6 Eigenvalue and Eigenvector of a Linear Transformation
      7. 2.1.7 Matrix Factorization
    2. 2.2 Calculus and Optimization Techniques
      1. 2.2.1 Introduction to Multivariate Calculus
      2. 2.2.2 Constrained and Unconstrained Optimization Techniques
    3. 2.3 Regression Analysis
      1. Introduction
      2. 2.3.1 Simple Linear Regression
      3. 2.3.2 Multiple Linear Regression
      4. 2.3.3 Polynomial Regression
      5. 2.3.4 Logistic Regression
      6. 2.3.5 LASSO Regression
    4. Reference
  10. Chapter 3 Data Analytics for Environmental Engineering
    1. 3.1 Introduction to Environmental Engineering
      1. 3.1.1 Role of Data Analytics in Environmental Engineering
    2. 3.2 Data Analysis in Environmental Engineering
      1. 3.2.1 Types of Sample Collection
      2. 3.2.2 Determination of Sample Size
    3. 3.3 Applications of Soft Computing Tools
      1. 3.3.1 Artificial Neural Network
      2. 3.3.2 Genetic Algorithm
      3. 3.3.3 Fuzzy Logic
    4. 3.4 Multiple Criteria Decision-Making (MCDM) Model
      1. 3.4.1 MCDM Model: An Overview of Procedures
      2. 3.4.2 Fuzzy Multiple Criteria Decision-Making (FMCDM) Model
    5. References
  11. Chapter 4 Structural Engineering: Trends, Applications, and Advances
    1. 4.1 Overview of Structural Engineering
    2. 4.2 Need of Data Science in Structural Engineering
    3. 4.3 Current Trends and Applications of Data Science in Structural Engineering
      1. 4.3.1 Genetic Algorithm
      2. 4.3.2 Swarm Intelligence
      3. 4.3.3 Artificial Neural Networks
      4. 4.3.4 Big Data
    4. 4.4 Application of AI in Concrete Technology
      1. 4.4.1 Concrete
      2. 4.4.2 Artificial Intelligence (AI)
      3. 4.4.3 Application of AI for Prediction of Mechanical Properties of Concrete, Especially Compressive Strength
      4. 4.4.4 Application of AI Techniques in Mix Design of Concrete
    5. 4.5 Conclusion and Future Scope
    6. References
  12. Chapter 5 Application of Data Science in Transportation Systems
    1. 5.1 Introduction to Transportation Engineering
      1. 5.1.1 Subdivision of Transportation Engineering
      2. 5.1.2 Aspects of Transportation Development
    2. 5.2 Data Analytics in Transportation Industry
      1. 5.2.1 Significance of Data Analytics in Transportation
      2. 5.2.2 Data Collection Tools and Methods
      3. 5.2.3 Process of Data Collection (Surveys)
      4. 5.2.4 Data Collection Techniques
      5. 5.2.5 Advantages of Predictive Analytics for Public Transportation Planning
    3. 5.3 Applications of Data Analytics in Transportation Planning and Management
      1. 5.3.1 Data Analytics for Planning the Multimodal Transportation
      2. 5.3.2 Forecasting the Traffic Congestion
      3. 5.3.3 Self-Driving Car
      4. 5.3.4 Finding Parking Slots
      5. 5.3.5 The Connected Vehicle
    4. 5.4 Boom Bike-Sharing Demand Case Study
      1. 5.4.1 Problem Statement
      2. 5.4.2 Additional Questions
      3. 5.4.3 Understanding the Data Set and the Data Dictionary
      4. 5.4.4 Solution Approach
    5. References
    6. Appendix –
  13. Chapter 6 Data Analytics for Water Resource Engineering
    1. 6.1 Introduction to Water Resource Engineering
      1. 6.1.1 Role of Data Analytics in Water Resource Engineering
      2. 6.1.2 Sustainable Water Resource Engineering
      3. 6.1.3 Types of Data Analytics in Water Resource Engineering Perspective
      4. 6.1.4 Data Analytics Challenges in Water Resource Engineering
    2. 6.2 Role of Big Data in Water Resources
      1. 6.2.1 Big Data Characteristics
      2. 6.2.2 Big Data Analytical Techniques
      3. 6.2.3 Challenges of Big Data Analytical Techniques in Water Resource Engineering
    3. 6.3 Advanced Computational Intelligence Techniques in Water Resource Management
      1. 6.3.1 Artificial Intelligence Techniques
      2. 6.3.2 Machine Learning
      3. 6.3.3 Deep Learning (DL)
      4. 6.3.4 Fuzzy Logic
    4. 6.4 Predictive Models
      1. 6.4.1 Water Quality (Example 1)
      2. 6.4.2 Hydrologic Prediction and Forecasting
    5. 6.5 Applications of Data Analytics in Water Resource Engineering
      1. 6.5.1 Modeling for Flood Management
      2. 6.5.2 Remote Sensing and GIS for Identification of Groundwater Recharge
      3. 6.5.3 Mitigation Measures Through Water Conservation Works
      4. 6.5.4 Watershed Management
    6. 6.6 Case Study on Identification of Potential Groundwater Recharge Zones and Suitable Locations for Appropriate Artificial Recharge Structures Using Remote Sensing and GIS Technology
    7. References
  14. Chapter 7 Data Analysis in Geomatics
    1. 7.1 Introduction
    2. 7.2 Adjustment of Survey Measurement
      1. 7.2.1 Introduction
      2. 7.2.2 Definitions
      3. 7.2.3 Adjustment of Direct and Indirect Observations
      4. 7.2.4 Adjustment of Closed Traverse Networks
    3. 7.3 Data Analysis in Satellite-Based Positioning System
      1. 7.3.1 Introduction
      2. 7.3.2 Features of GPS Data Processing
      3. 7.3.3 GPS Data Processing
    4. 7.4 Geospatial Analysis
      1. 7.4.1 Remote Sensing
      2. 7.4.2 Geographic Information System (GIS)
      3. 7.4.3 Digital Image Processing
    5. 7.5 Conclusion
    6. References
  15. Chapter 8 Conclusions
    1. 8.1 Summary
    2. 8.2 Business Intelligence
    3. 8.3 Research Openings and Future Outlook
  16. Index

Product information

  • Title: Data Science for Civil Engineering
  • Author(s): Rakesh K. Jain, Prashant Shantaram Dhotre, Deepak Tatyasaheb Mane, Parikshit Narendra Mahalle
  • Release date: May 2023
  • Publisher(s): CRC Press
  • ISBN: 9781000873481