Data Science for Civil Engineering: A Beginner's Guide

Cover
Half Title
Title Page
Copyright Page
Contents
About the Authors
Preface
1. Introduction
	1.1 Introduction
		1.1.1 Purpose of Data Science
		1.1.2 BI and Data Science: What's the Difference
		1.1.3 Components of Data Science
	1.2 Data Science: An Overview
		1.2.1 Need for Data Science
		1.2.2 Role of a Data Scientist
		1.2.3 Problems and Solutions Using Data Science
	1.3 Benefits, Challenges, and Applications of Data Science
		1.3.1 Benefits of Data Science
		1.3.2 Challenges of Data Science
		1.3.3 Data Science Life Cycle
		1.3.4 Data Science and Its Applications
	1.4 Data Science and Civil Engineering: Opportunities
	1.5 Summary of the Book
	References
2. Mathematical Foundation for Data Science
	2.1 Linear Algebra
		2.1.1 Vector Space
			2.1.1.1 Subspaces
			2.1.1.2 Affine Subspaces
		2.1.2 Vector Norm
		2.1.3 Linearly Dependency and Independency of Vectors
		2.1.4 Basis and Dimensions of Vectors Spaces
		2.1.5 Linear Transformation (Linear Mapping)
		2.1.6 Eigenvalue and Eigenvector of a Linear Transformation
		2.1.7 Matrix Factorization
			2.1.7.1 LU Decomposition
			2.1.7.2 Cholesky's Decomposition
			2.1.7.3 QR Decomposition
	2.2 Calculus and Optimization Techniques
		2.2.1 Introduction to Multivariate Calculus
		2.2.2 Constrained and Unconstrained Optimization Techniques
	2.3 Regression Analysis
		Introduction
		2.3.1 Simple Linear Regression
		2.3.2 Multiple Linear Regression
		2.3.3 Polynomial Regression
		2.3.4 Logistic Regression
		2.3.5 LASSO Regression
	Reference
3. Data Analytics for Environmental Engineering
	3.1 Introduction to Environmental Engineering
		3.1.1 Role of Data Analytics in Environmental Engineering
	3.2 Data Analysis in Environmental Engineering
		3.2.1 Types of Sample Collection
			3.2.1.1 Grab Sampling
			3.2.1.2 Composite Sampling
		3.2.2 Determination of Sample Size
			3.2.2.1 Confidence Interval and Confidence Limits
			3.2.2.2 Confidence Interval for Means
			3.2.2.3 Sample Size
			3.2.2.4 Illustrative Example of Determination of Sample Size
	3.3 Applications of Soft Computing Tools
		3.3.1 Artificial Neural Network
		3.3.2 Genetic Algorithm
		3.3.3 Fuzzy Logic
	3.4 Multiple Criteria Decision-Making (MCDM) Model
		3.4.1 MCDM Model: An Overview of Procedures
		3.4.2 Fuzzy Multiple Criteria Decision-Making (FMCDM) Model
			3.4.2.1 Fuzzy Normalized Weightage Method
			3.4.2.2 Fuzzy Evidence Theory Weightage Method
			3.4.2.3 Illustrative Example
	References
4. Structural Engineering: Trends, Applications, and Advances
	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
		4.3.1 Genetic Algorithm
		4.3.2 Swarm Intelligence
			4.3.2.1 Particle Swarm Optimization
			4.3.2.2 Bee Colony Algorithm
			4.3.2.3 Cuckoo Search
		4.3.3 Artificial Neural Networks
		4.3.4 Big Data
	4.4 Application of AI in Concrete Technology
		4.4.1 Concrete
		4.4.2 Artificial Intelligence (AI)
		4.4.3 Application of AI for Prediction of Mechanical Properties of Concrete, Especially Compressive Strength
			4.4.3.1 Examples of Application of AI Techniques for Prediction of Concrete Properties
		4.4.4 Application of AI Techniques in Mix Design of Concrete
			4.4.4.1 Mix Design of Concrete
			4.4.4.2 Methods of Mix Design of Concrete
			4.4.4.3 Examples of Application of AI Techniques for Mix Design of Concrete
	4.5 Conclusion and Future Scope
	References
5. Application of Data Science in Transportation Systems
	5.1 Introduction to Transportation Engineering
		5.1.1 Subdivision of Transportation Engineering
			5.1.1.1 Highway Engineering
			5.1.1.2 Railway Engineering
			5.1.1.3 Water Transportation: Port and Harbor
			5.1.1.4 Airport Engineering
			5.1.1.5 Pipeline Engineering
			5.1.1.6 Telecommunications
		5.1.2 Aspects of Transportation Development
	5.2 Data Analytics in Transportation Industry
		5.2.1 Significance of Data Analytics in Transportation
		5.2.2 Data Collection Tools and Methods
		5.2.3 Process of Data Collection (Surveys)
		5.2.4 Data Collection Techniques
			5.2.4.1 Traditional Data Collection Techniques
			5.2.4.2 Modern Data Collection Techniques
		5.2.5 Advantages of Predictive Analytics for Public Transportation Planning
	5.3 Applications of Data Analytics in Transportation Planning and Management
		5.3.1 Data Analytics for Planning the Multimodal Transportation
			5.3.1.1 Examples of Cities with MTS
		5.3.2 Forecasting the Traffic Congestion
		5.3.3 Self-Driving Car
		5.3.4 Finding Parking Slots
		5.3.5 The Connected Vehicle
			5.3.5.1 Improved Traffic Flow Using Technology
			5.3.5.2 Smart Transportation for Intelligent City Planning
	5.4 Boom Bike-Sharing Demand Case Study
		5.4.1 Problem Statement
		5.4.2 Additional Questions
		5.4.3 Understanding the Data Set and the Data Dictionary
		5.4.4 Solution Approach
	References
6. Data Analytics for Water Resource Engineering
	6.1 Introduction to Water Resource Engineering
		6.1.1 Role of Data Analytics in Water Resource Engineering
		6.1.2 Sustainable Water Resource Engineering
		6.1.3 Types of Data Analytics in Water Resource Engineering Perspective
			6.1.3.1 Descriptive Analytics
			6.1.3.2 Diagnostic Analytics
			6.1.3.3 Predictive Analytics
			6.1.3.4 Prescriptive Analytics
		6.1.4 Data Analytics Challenges in Water Resource Engineering
	6.2 Role of Big Data in Water Resources
		6.2.1 Big Data Characteristics
			6.2.1.1 Sources of Big Data for Water Resource Engineering Applications
				6.2.1.1.1 Geospatial Data
				6.2.1.1.2 Internet of Things (IoT)
				6.2.1.1.3 Crowed Source
				6.2.1.1.4 Field Data
				6.2.1.1.5 Social Media and the Web
				6.2.1.1.6 Historical
		6.2.2 Big Data Analytical Techniques
			6.2.2.1 Statistical
			6.2.2.2 Data Mining
			6.2.2.3 Machine Learning
			6.2.2.4 Visualization
		6.2.3 Challenges of Big Data Analytical Techniques in Water Resource Engineering
	6.3 Advanced Computational Intelligence Techniques in Water Resource Management
		6.3.1 Artificial Intelligence Techniques
			6.3.1.1 Artificial Intelligence in Water Supply (Example 1)
		6.3.2 Machine Learning
			6.3.2.1 Water Quality Management (Example 1)
			6.3.2.2 Reservoir Management (Example 2)
		6.3.3 Deep Learning (DL)
			6.3.3.1 Water Distribution Systems (Example 1)
		6.3.4 Fuzzy Logic
			6.3.4.1 Groundwater Mapping (Example 1)
	6.4 Predictive Models
		6.4.1 Water Quality (Example 1)
		6.4.2 Hydrologic Prediction and Forecasting
			6.4.2.1 Groundwater (Example 1)
			6.4.2.2 Drought Risk Assessment (Example 2)
			6.4.2.3 Sediment Transport (Example 3)
	6.5 Applications of Data Analytics in Water Resource Engineering
		6.5.1 Modeling for Flood Management
		6.5.2 Remote Sensing and GIS for Identification of Groundwater Recharge
		6.5.3 Mitigation Measures Through Water Conservation Works
		6.5.4 Watershed Management
	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
7. Data Analysis in Geomatics
	7.1 Introduction
	7.2 Adjustment of Survey Measurement
		7.2.1 Introduction
		7.2.2 Definitions
		7.2.3 Adjustment of Direct and Indirect Observations
			7.2.3.1 Weight of Observation (w)
			7.2.3.2 Laws of Weight
			7.2.3.3 Direct Observation of Equal Weight
			7.2.3.4 Direct Observations of Unequal Weights
			7.2.3.5 Indirect Observed Quantities Involving Unknown of Equal Weight
			7.2.3.6 Indirectly Observed Quantities Involving Unknowns of Unequal Weights
		7.2.4 Adjustment of Closed Traverse Networks
			7.2.4.1 Illustrative Example
	7.3 Data Analysis in Satellite-Based Positioning System
		7.3.1 Introduction
		7.3.2 Features of GPS Data Processing
		7.3.3 GPS Data Processing
			7.3.3.1 Applications
	7.4 Geospatial Analysis
		7.4.1 Remote Sensing
		7.4.2 Geographic Information System (GIS)
		7.4.3 Digital Image Processing
			7.4.3.1 Application of Geospatial Analysis
			7.4.3.2 Case Study
	7.5 Conclusion
	References
8. Conclusions
	8.1 Summary
	8.2 Business Intelligence
	8.3 Research Openings and Future Outlook
Index