Machine learning for civil & environmental engineers : a practical approach to data-driven analysis, explainability, and causality: Fid-Bau Portal

Machine learning for civil & environmental engineers : a practical approach to data-driven analysis, explainability, and causality

Naser, M. Z. / John Wiley and Sons

Table of contents

Abstract

"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