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Machine Learning for Civil and Environmental Engineers: A Practical Approach to Data-Driven Analysis, Explainability, and Causality

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Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers

This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain.

Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers.

The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with.

Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on:

  • The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective
  • Supervised vs. unsupervised learning for regression, classification, and clustering problems
  • Explainable and causal methods for practical engineering problems
  • Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis
  • A framework for machine learning adoption and application, covering key questions commonly faced by practitioners

This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.

Author(s): M. Z. Naser
Publisher: Wiley
Year: 2023

Language: English
Pages: 608
City: Hoboken

Machine Learning for Civil & Environmental Engineers
Contents
Preface
About the Companion Website
1 Teaching Methods for This Textbook
Synopsis
1.1 Education in Civil and Environmental Engineering
1.2 Machine Learning as an Educational Material
1.3 Possible Pathways for Course/Material Delivery
1.3.1 Undergraduate Students
1.3.2 Graduate Students and Post-docs
1.3.3 Engineers and Practitioners
1.3.4 A Note
1.4 Typical Outline for Possible Means of Delivery
Chapter Blueprint
Questions and Problems
References
2 Introduction to Machine Learning
Synopsis
2.1 A Brief History of Machine Learning
2.2 Types of Learning
2.3 A Look into ML from the Lens of Civil and Environmental Engineering
2.4 Let Us Talk a Bit More about ML
2.5 ML Pipeline
2.5.1 Formulating a Hypothesis
2.5.2 Database Development
2.5.3 Processing Observations
2.5.4 Model Development
2.5.5 Model Evaluation
2.5.6 Model Optimization
2.5.7 Model Deployment
2.5.8 Model Management (Monitoring, Updating, Etc.)
2.6 Conclusions
Definitions
Chapter Blueprint
Questions and Problems
References
3 Data and Statistics
Synopsis
3.1 Data and Data Science
3.2 Types of Data
3.2.1 Numerical Data
3.2.2 Categorical Data
3.2.3 Footage
3.2.4 Time Series Data*
3.2.5 Text Data*
3.3 Dataset Development
3.4 Diagnosing and Handling Data
3.5 Visualizing Data
3.6 Exploring Data
3.6.1 Correlation-based and Information-based Methods
3.6.2 Feature Selection and Extraction Methods
3.6.3 Dimensionality Reduction
3.7 Manipulating Data
3.7.1 Manipulating Numerical Data
3.7.2 Manipulating Categorical Data
3.7.3 General Manipulation
3.8 Manipulation for Computer Vision
3.9 A Brief Review of Statistics
3.9.1 Statistical Concepts
3.9.2 Regression
3.10 Conclusions
Definitions
Chapter Blueprint
Questions and Problems
References
4 Machine Learning Algorithms
Synopsis
4.1 An Overview of Algorithms
4.1.1 Supervised Learning
4.1.2 Unsupervised Learning
4.2 Conclusions
Definitions
Chapter Blueprint
Questions and Problems
References
5 Performance Fitness Indicators and Error Metrics
Synopsis
5.1 Introduction
5.2 The Need for Metrics and Indicators
5.3 Regression Metrics and Indicators
5.4 Classification Metrics and Indicators
5.5 Clustering Metrics and Indicators
5.6 Functional Metrics and Indicators*
5.6.1 Energy-based Indicators
5.6.2 Domain-specific Metrics and Indicators
5.6.3 Other Functional Metrics and Indicators
5.7 Other Techniques (Beyond Metrics and Indicators)
5.7.1 Spot Analysis
5.7.2 Case-by-Case Examination
5.7.3 Drawing and Stacking
5.7.4 Rational Vetting*
5.7.5 Confidence Intervals*
5.8 Conclusions
Definitions
Chapter Blueprint
Questions and Problems
Suggested Metrics and Packages
References
6 Coding-free and Coding-based Approaches to Machine Learning
Synopsis
6.1 Coding-free Approach to ML
6.1.1 BigML
6.1.2 DataRobot
6.1.3 Dataiku
6.1.4 Exploratory
6.1.5 Clarifai
6.2 Coding-based Approach to ML
6.2.1 Python
6.2.2 R
6.3 Conclusions
Definitions
Chapter Blueprint
Questions and Problems
References
7 Explainability and Interpretability
Synopsis
7.1 The Need for Explainability
7.1.1 Explainability and Interpretability
7.2 Explainability from a Philosophical Engineering Perspective*
7.3 Methods for Explainability and Interpretability
7.3.1 Supervised Machine Learning
7.3.2 Unsupervised Machine Learning
7.4 Examples
7.4.1 Surrogates*
7.4.2 Global Explainability
7.4.3 Local Explainability
7.5 Conclusions
Definitions
Questions and Problems
Chapter Blueprint
References
8 Causal Discovery and Causal Inference
Synopsis
8.1 Big Ideas Behind This Chapter
8.2 Re-visiting Experiments
8.3 Re-visiting Statistics and ML
8.4 Causality
8.4.1 Definition and a Brief History
8.4.2 Correlation and Causation
8.4.3 The Causal Rungs
8.4.4 Regression and Causation
8.4.5 Causal Discovery and Causal Inference
8.4.6 Assumptions Required to Establish Causality
8.4.7 Causal Graphs and Graphical Methods
8.4.8 Causal Search Methods and ML Packages
8.4.9 Causal Inference and ML Packages
8.4.10 Causal Approach
8.5 Examples
8.5.1 Causal Discovery
8.5.2 Causal Inference
8.5.3 DAG from CausalNex
8.5.4 Modifying CausalNex’s DAG with Domain Knowledge
8.5.5 A DAG Similar to a Regression Model
8.6 A Note on Causality and ML
8.7 Conclusions
Definitions
Questions and Problems
Chapter Blueprint
References
9 Advanced Topics (Synthetic and Augmented Data, Green ML, Symbolic Regression, Mapping Functions, Ensembles, and AutoML)
Synopsis
9.1 Synthetic and Augmented Data
9.1.1 Big Ideas
9.1.2 Conservative Interpolation
9.1.3 Synthetic Minority Over-sampling Technique (SMOTE)
9.1.4 Generative Adversarial Networks (GANs) and Triplet-based Variational Autoencoder (TVAE)
9.1.5 Augmented Data
9.1.6 A Note
9.2 Green ML
9.2.1 Big Ideas
9.2.2 Example
9.2.3 Energy Perspective
9.2.4 A Note
9.3 Symbolic Regression
9.3.1 Big Ideas
9.3.2 Examples
9.3.3 Eureqa
9.3.4 TurningBot
9.3.5 HeuristicLab
9.3.6 GeneXproTools⁎
9.3.7 Online Interface by MetaDemoLab
9.3.8 Python
9.3.9 Eureqa
9.3.10 MetaDemoLab
9.3.11 Python
9.3.12 GeneXproTools*
9.3.13 Eureqa
9.3.14 MetaDemoLab
9.3.15 HeuristicLab
9.3.16 A Note
9.4 Mapping Functions
9.4.1 Big Ideas
9.4.2 Concept of Mapping Functions
9.4.3 Approach to Mapping Functions
9.4.4 Example
9.4.5 A Note
9.5 Ensembles
9.5.1 Big Ideas
9.5.2 Examples
9.6 AutoML
9.6.1 Big Ideas
9.6.2 The Rationale and Anatomy of CLEMSON
9.6.3 Example
9.6.4 A Note
9.7 Conclusions
Definitions
Questions and Problems
Chapter Blueprint
References
10 Recommendations, Suggestions, and Best Practices
Synopsis
10.1 Recommendations
10.1.1 Continue to Learn
10.1.2 Understand the Difference between Statistics and ML
10.1.3 Know the Difference between Prediction via ML and Carrying Out Tests and Numerical Simulations
10.1.4 Ask if You Need ML to Address the Phenomenon on Hand
10.1.5 Establish a Crystal Clear Understanding of Model Assumptions, Outcomes, and Limitations
10.1.6 Remember that an Explainable Model Is Not a Causal Model
10.1.7 Master Performance Metrics and Avoid the Perception of False Goodness
10.1.8 Acknowledge that Your Model Is Likely to Be Biased
10.1.9 Consult with Experts and Re-visit Domain Knowledge to Identify Suitable Features
10.1.10 Carefully Navigate the Trade-offs
10.1.11 Share Your Data and Codes
10.2 Suggestions
10.2.1 Start Your Analysis with Simple Algorithms
10.2.2 Explore Algorithms and Metrics
10.2.3 Be Conscious of Data Origin
10.2.4 Emphasize Model Testing
10.2.5 Think Beyond Training and Validation
10.2.6 Trace Your Model Beyond Deployment
10.2.7 Convert Your ML Models into Web and Downloadable Applications
10.2.8 Whenever Possible, Include Physics Principles in ML Models
10.3 Best Practices
10.3.1 Avoid the Use of “Small” and Low Quality Data
10.3.2 Be Aware of the Most Commonly Favored ML Algorithms
10.3.3 Follow the Most Favored Model Development Procedures
10.3.4 Report Statistics on Your Dataset
10.3.5 Avoid Blackbox Models in Favor of Explainable and Causal Models (Unless the Goal Is to Create a Blackbox Model)
10.3.6 Integrate ML into Your Future Works
Definitions
Questions and Problems
References
11 Final Thoughts and Future Directions
Synopsis
11.1 Now
11.2 Tomorrow
11.2.1 Big, Small, and Imbalanced Data
11.2.2 Learning ML
11.2.3 Benchmarking ML
11.2.4 Standardizing ML
11.2.5 Unboxing ML
11.2.6 Popularizing ML
11.2.7 Engineering ML
11.3 Possible Ideas to Tackle
11.4 Conclusions
References
Index
EULA