000			03072 a2200205 4500
005			20250415155955.0
008			241214b2023|||||||| |||| 00| 0 eng d
020			_a9781119897606
041			_aEnglish
082			_a006.31 N37
100			_aNaser, M. Z. _eAuthor _93501
245			_aMachine learning for civil and environmental engineers: A practical approach to data-driven analysis explainability, and causality
260			_a New Jersey: _bJohn Wiley & Sons, _c2023.
300			_axix, 588p.; 24cms.
500			_aAccessible 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.
650			_aMachine learning _xCivil engineering -- data processing _94860
650			_aMI design, simulation, and optimization for infrastructure _96218
650			_aEnvironmental engineering -- data processing _94861
942			_cBK
999			_c1496 _d1496