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

This textbook provides a comprehensive and practical framework for machine learning applications and solutions in civil and environmental engineering, covering state-of-the-art methodologies and techniques for developing and implementing algorithms. It offers in-depth case studies with solved problems, ranging from simplified to advanced methods, and includes valuable information on supervised vs. unsupervised learning, explainable and causal methods, database development, and a framework for machine learning adoption and application. It is a valuable resource for undergraduate/graduate students, scientists/researchers, and design/engineering professionals.

Format: Hardback
Length: 608 pages
Publication date: 26 October 2023
Publisher: John Wiley & Sons Inc

This comprehensive textbook serves as an accessible and practical framework for machine learning applications and solutions in the field of civil and environmental engineering. It introduces engineers and engineering students to the realm of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI), highlighting their significance in addressing civil and environmental engineering projects and problems. The book presents state-of-the-art methodologies and techniques for developing and implementing algorithms within the engineering domain, enabling readers to apply cutting-edge solutions to real-world challenges.

Through a series of real-world projects, this textbook offers readers in-depth case studies accompanied by solved problems commonly faced by civil and environmental engineers. These projects encompass diverse areas such as the 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 predicting water quality and energy expenditure in buildings. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques, making the content accessible to professionals and students alike.

Written by a highly qualified professional with extensive experience in the field, Machine Learning provides valuable insights into various aspects of machine learning and causality in civil and environmental engineering. It includes a comprehensive scientometrics analysis, offering a historical perspective on the development of machine learning in this domain. The book also delves into supervised and unsupervised learning for regression, classification, and clustering problems, emphasizing their practical applications in engineering. Additionally, it discusses explainable and causal methods, which are crucial for addressing practical engineering problems with confidence.

Furthermore, the textbook provides comprehensive guidance on database development, outlining how engineers can effectively collect and verify appropriate data for machine learning applications. It emphasizes the importance of data quality and integrity in ensuring accurate and reliable results. By presenting comprehensive and practical knowledge, Machine Learning serves as a valuable resource for professional engineers and engineering students seeking to enhance their skills in machine learning and apply them to real-world engineering challenges.

Weight: 1406g
Dimension: 279 x 221 x 38 (mm)
ISBN-13: 9781119897606