Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality
Preface xiii About the Companion Website xix 1 Teaching Methods for This Textbook 1 Synopsis 1 1.1 Education in Civil and Environmental Engineering 1 1.2 Machine Learning as an Educational Material 2 1.3 Possible Pathways for Course/Material Delivery 3 1.4 Typical Outline for Possible Means of Deliv...
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| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
2023
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| Schlagworte: |
Machine learning
> Civil engineering
> Data processing
> Environmental engineering
> Bauingenieur-, Vermessungs- und Bauwesen
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Database Management / General
> Civil engineering, surveying & building
> Künstliche Intelligenz
> TECHNOLOGY & ENGINEERING / Civil / General
> Lehrbuch
> Bautechnik
> Umwelttechnik
> Maschinelles Lernen
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| Online Zugang: | Inhaltsverzeichnis Cover |
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| Zusammenfassung: | Preface xiii About the Companion Website xix 1 Teaching Methods for This Textbook 1 Synopsis 1 1.1 Education in Civil and Environmental Engineering 1 1.2 Machine Learning as an Educational Material 2 1.3 Possible Pathways for Course/Material Delivery 3 1.4 Typical Outline for Possible Means of Delivery 7 Chapter Blueprint 8 Questions and Problems 8 References 8 2 Introduction to Machine Learning 11 Synopsis 11 2.1 A Brief History of Machine Learning 11 2.2 Types of Learning 12 2.3 A Look into ML from the Lens of Civil and Environmental Engineering 15 2.4 Let Us Talk a Bit More about ML 17 2.5 ML Pipeline 18 2.6 Conclusions 27 Definitions 27 Chapter Blueprint 29 Questions and Problems 29 References 30 3 Data and Statistics 33 Synopsis 33 3.1 Data and Data Science 33 3.2 Types of Data 34 3.3 Dataset Development 37 3.4 Diagnosing and Handling Data 37 3.5 Visualizing Data 38 3.6 Exploring Data 59 3.7 Manipulating Data 66 3.8 Manipulation for Computer Vision 68 3.9 A Brief Review of Statistics 68 3.10 Conclusions 76 4 Machine Learning Algorithms 81 Synopsis 81 4.1 An Overview of Algorithms 81 4.2 Conclusions 127 5 Performance Fitness Indicators and Error Metrics 133 Synopsis 133 5.1 Introduction 133 5.2 The Need for Metrics and Indicators 134 5.3 Regression Metrics and Indicators 135 5.4 Classification Metrics and Indicators 142 5.5 Clustering Metrics and Indicators 142 5.6 Functional Metrics and Indicators* 151 5.7 Other Techniques (Beyond Metrics and Indicators) 154 5.8 Conclusions 159 6 Coding-free and Coding-based Approaches to Machine Learning 169 Synopsis 169 6.1 Coding-free Approach to ML 169 6.2 Coding-based Approach to ML 280 6.3 Conclusions 322 7 Explainability and Interpretability 327 7 Synopsis 327 7.1 The Need for Explainability 327 7.2 Explainability from a Philosophical Engineering Perspective* 329 7.3 Methods for Explainability and Interpretability 331 7.4 Examples 335 7.5 Conclusions 428 8 Causal Discovery and Causal Inference 433 Synopsis 433 8.1 Big Ideas Behind This Chapter 433 8.2 Re-visiting Experiments 434 8.3 Re-visiting Statistics and ML 435 8.4 Causality 436 8.5 Examples 451 8.6 A Note on Causality and ML 475 8.7 Conclusions 475 9 Advanced Topics (Synthetic and Augmented Data, Green ML, Symbolic Regression, Mapping Functions, Ensembles, and AutoML) 481 Synopsis 481 9.1 Synthetic and Augmented Data 481 9.2 Green ML 488 9.3 Symbolic Regression 498 9.4 Mapping Functions 529 9.5 Ensembles 539 9.6 AutoML 548 9.7 Conclusions 552 10 Recommendations, Suggestions, and Best Practices 559 Synopsis 559 10.1 Recommendations 559 10.2 Suggestions 564 10.3 Best Practices 566 11 Final Thoughts and Future Directions 573 Synopsis 573 11.1 Now 573 11.2 Tomorrow 573 11.3 Possible Ideas to Tackle 575 11.4 Conclusions 576 References 576 Index 577 "Synopsis: The theme of this textbook revolves around how machine learning (ML) can help civil and environmental engineers transform their domain. This textbook hopes to deliver the knowledge and information necessary to educate engineering students and practitioners on the principles of ML and how to integrate these into our field. This textbook is about navigating the realm of data-driven ML, explainable ML, and causal ML from the context of education, research, and practice. In hindsight, this textbook augments ML into the heart of engineering. Together, we will go over the big ideas behind ML. We will ask and answer questions such as, what is ML? Why is ML needed? How does ML differ from statistics, physical testing, and numerical simulation? Can we trust ML? And how can we benefit from ML, adapt to it, adopt it, wield it, and leverage it to overcome many, many of the problems that we may face? This book is also about showing you, my dear reader, how to amplify your engineering knowledge with a new tool. A tool that is yet to be formally taught in our curriculum. A tool that many civil and environmental engineering departments and schools may not fully appreciate ; yet are eager to know more about!"-- Accessible and practical framework for machine learning applications and solutions for civil and environmental engineersThis 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 practitionersThis 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 |
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| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | xix, 588 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119897606 978-1-119-89760-6 |