book

Data Science for Civil Engineering

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

May 2023

Intermediate to advanced

248 pages

7h 10m

English

CRC Press

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1.1 Introduction1.1.1 Purpose of Data Science1.1.2 BI and Data Science: What's the Difference1.1.3 Components of Data Science1.2 Data Science: An Overview1.2.1 Need for Data Science1.2.2 Role of a Data Scientist1.2.3 Problems and Solutions Using Data Science1.3 Benefits, Challenges, and Applications of Data Science1.3.1 Benefits of Data Science1.3.2 Challenges of Data Science1.3.3 Data Science Life Cycle1.3.4 Data Science and Its Applications1.4 Data Science and Civil Engineering: Opportunities1.5 Summary of the BookReferences
2.1 Linear Algebra2.1.1 Vector Space2.1.2 Vector Norm2.1.3 Linearly Dependency and Independency of Vectors2.1.4 Basis and Dimensions of Vectors Spaces2.1.5 Linear Transformation (Linear Mapping)2.1.6 Eigenvalue and Eigenvector of a Linear Transformation2.1.7 Matrix Factorization2.2 Calculus and Optimization Techniques2.2.1 Introduction to Multivariate Calculus2.2.2 Constrained and Unconstrained Optimization Techniques2.3 Regression AnalysisIntroduction2.3.1 Simple Linear Regression2.3.2 Multiple Linear Regression2.3.3 Polynomial Regression2.3.4 Logistic Regression2.3.5 LASSO RegressionReference
3.1 Introduction to Environmental Engineering3.1.1 Role of Data Analytics in Environmental Engineering3.2 Data Analysis in Environmental Engineering3.2.1 Types of Sample Collection3.2.2 Determination of Sample Size3.3 Applications of Soft Computing Tools3.3.1 Artificial Neural Network3.3.2 Genetic Algorithm3.3.3 Fuzzy Logic3.4 Multiple Criteria Decision-Making (MCDM) Model3.4.1 MCDM Model: An Overview of Procedures3.4.2 Fuzzy Multiple Criteria Decision-Making (FMCDM) ModelReferences

4.1 Overview of Structural Engineering4.2 Need of Data Science in Structural Engineering4.3 Current Trends and Applications of Data Science in Structural Engineering4.3.1 Genetic Algorithm4.3.2 Swarm Intelligence4.3.3 Artificial Neural Networks4.3.4 Big Data4.4 Application of AI in Concrete Technology4.4.1 Concrete4.4.2 Artificial Intelligence (AI)4.4.3 Application of AI for Prediction of Mechanical Properties of Concrete, Especially Compressive Strength4.4.4 Application of AI Techniques in Mix Design of Concrete4.5 Conclusion and Future ScopeReferences
5.1 Introduction to Transportation Engineering5.1.1 Subdivision of Transportation Engineering5.1.2 Aspects of Transportation Development5.2 Data Analytics in Transportation Industry5.2.1 Significance of Data Analytics in Transportation5.2.2 Data Collection Tools and Methods5.2.3 Process of Data Collection (Surveys)5.2.4 Data Collection Techniques5.2.5 Advantages of Predictive Analytics for Public Transportation Planning5.3 Applications of Data Analytics in Transportation Planning and Management5.3.1 Data Analytics for Planning the Multimodal Transportation5.3.2 Forecasting the Traffic Congestion5.3.3 Self-Driving Car5.3.4 Finding Parking Slots5.3.5 The Connected Vehicle5.4 Boom Bike-Sharing Demand Case Study5.4.1 Problem Statement5.4.2 Additional Questions5.4.3 Understanding the Data Set and the Data Dictionary5.4.4 Solution ApproachReferencesAppendix –
6.1 Introduction to Water Resource Engineering6.1.1 Role of Data Analytics in Water Resource Engineering6.1.2 Sustainable Water Resource Engineering6.1.3 Types of Data Analytics in Water Resource Engineering Perspective6.1.4 Data Analytics Challenges in Water Resource Engineering6.2 Role of Big Data in Water Resources6.2.1 Big Data Characteristics6.2.2 Big Data Analytical Techniques6.2.3 Challenges of Big Data Analytical Techniques in Water Resource Engineering6.3 Advanced Computational Intelligence Techniques in Water Resource Management6.3.1 Artificial Intelligence Techniques6.3.2 Machine Learning6.3.3 Deep Learning (DL)6.3.4 Fuzzy Logic6.4 Predictive Models6.4.1 Water Quality (Example 1)6.4.2 Hydrologic Prediction and Forecasting6.5 Applications of Data Analytics in Water Resource Engineering6.5.1 Modeling for Flood Management6.5.2 Remote Sensing and GIS for Identification of Groundwater Recharge6.5.3 Mitigation Measures Through Water Conservation Works6.5.4 Watershed Management6.6 Case Study on Identification of Potential Groundwater Recharge Zones and Suitable Locations for Appropriate Artificial Recharge Structures Using Remote Sensing and GIS TechnologyReferences
7.1 Introduction7.2 Adjustment of Survey Measurement7.2.1 Introduction7.2.2 Definitions7.2.3 Adjustment of Direct and Indirect Observations7.2.4 Adjustment of Closed Traverse Networks7.3 Data Analysis in Satellite-Based Positioning System7.3.1 Introduction7.3.2 Features of GPS Data Processing7.3.3 GPS Data Processing7.4 Geospatial Analysis7.4.1 Remote Sensing7.4.2 Geographic Information System (GIS)7.4.3 Digital Image Processing7.5 ConclusionReferences
8.1 Summary8.2 Business Intelligence8.3 Research Openings and Future Outlook

Content preview from Data Science for Civil Engineering

2 Mathematical Foundation for Data Science

Radhika Menon, Neha Shaikh, and Ambika Biradar

DOI: 10.1201/9781003316671-2

2.1 Linear Algebra

Data science has applications in almost all branches of civil engineering. A strong mathematical theory forms the foundation of all data science models. Mathematical concepts like linear algebra, gradient-based optimization techniques and regression techniques are foundations of data analysis models. Therefore, in order to extract knowledge and hidden patterns from the raw data, it is essential to have understanding of all such core mathematical concepts. These mathematical concepts, together with some illustrations are covered in Sections 1 and 2.

Data, which may be an observation, feature or an attribute, ...

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Introduction to Statistical and Machine Learning Methods for Data Science

Publisher Resources

ISBN: 9781000873481

Data Science for Civil Engineering

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

2 Mathematical Foundation for Data Science

2.1 Linear Algebra

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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2.1 Linear Algebra

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You might also like

Introduction to Statistical and Machine Learning Methods for Data Science

Effective Data Science Infrastructure

Training Data for Machine Learning

Data Science for Business

Publisher Resources

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.