Data Science for Civil Engineering: A Beginner's Guide

✍ Scribed by Jain, Rakesh K.; Dhotre, Prashant Shantaram; Mane, Deepak Tatyasaheb; Mahalle, Parikshit Narendra

• Publisher: Taylor & Francis Group
• Year: 2023
• Tongue: English
• Leaves: 279
• Category: Library

✦ Synopsis

This book explains the use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: • Details information on essential mathematics required to implement civil engineering applications using data science techniques.; • Discusses broad background of data science and its fundamentals.; • Focuses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering.; • Includes Python programming libraries to solve complex problems.; • Addresses various real-world applications of data science–based civil engineering use cases. This book is aimed at senior undergraduate students in civil engineering and applied data science.

✦ Table of Contents

Chapter 1 Introduction Deepak Mane and Prashant Dhotre
1.1 Introduction
1.2 Data Science: An Overview
1.3 Benefits, Challenges, and Applications of Data Science
1.4 Data Science and Civil Engineering: Opportunities
1.5 Summary of the Book
References
Chapter 2 Mathematical Foundation for Data Science Radhika Menon, Neha Shaikh, and Ambika Biradar
2.1 Linear Algebra
2.2 Calculus and Optimization Techniques
2.3 Regression Analysis
Reference
Chapter 3 Data Analytics for Environmental Engineering Ravindra K. Lad and R. A. Christian
3.1 Introduction to Environmental Engineering
3.2 Data Analysis in Environmental Engineering
3.3 Applications of Soft Computing Tools
3.4 Multiple Criteria Decision-Making (MCDM) Model
References
Chapter 4 Structural Engineering: Trends, Applications, and Advances Deepa A. Joshi and Manisha Shewale
4.1 Overview of Structural Engineering
4.2 Need of Data Science in Structural Engineering
4.3 Current Trends and Applications of Data Science in Structural Engineering
4.4 Application of AI in Concrete Technology
4.5 Conclusion and Future Scope
References
Chapter 5 Application of Data Science in Transportation Systems Rakesh K. Jain, Ashish R. Joshi, and Bharati H. Gavhane
5.1 Introduction to Transportation Engineering
5.2 Data Analytics in Transportation Industry
5.3 Applications of Data Analytics in Transportation Planning and Management
5.4 Boom Bike-Sharing Demand Case Study
References
Chapter 6 Data Analytics for Water Resource Engineering Shivaji Govind Patil and Sivakumar V.
6.1 Introduction to Water Resource Engineering
6.2 Role of Big Data in Water Resources
6.3 Advanced Computational Intelligence Techniques in Water Resource Management
6.4 Predictive Models
6.5 Applications of Data Analytics in Water Resource Engineering
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
References
Chapter 7 Data Analysis in Geomatics S S Shahapure
7.1 Introduction
7.2 Adjustment of Survey Measurement
7.3 Data Analysis in Satellite-Based Positioning System
7.4 Geospatial Analysis
7.5 Conclusion
References
Chapter 8 Conclusions Parikshit N. Mahalle
8.1 Summary
8.2 Business Intelligence
8.3 Research Openings and Future Outlook