{"product_id":"machine-learning-for-civil-and-environmental-engineers-a-practical-approach-to-data-driven-analysis-explainability-and-causality-9781119897606","title":"Machine Learning for Civil and Environmental Engineers: A Practical Approach to Data-Driven Analysis, Explainability, and Causality","description":"\u003cp\u003e\u003cb\u003eAccessible and practical framework for machine learning applications and solutions for civil and environmental engineers\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eThis textbook \u003c\/i\u003eintroduces 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. \u003c\/p\u003e\u003cp\u003eThrough 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. \u003c\/p\u003e\u003cp\u003eThe 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. \u003c\/p\u003e\u003cp\u003eWritten by a highly qualified professional with significant experience in the field, \u003ci\u003eMachine Learning\u003c\/i\u003e includes valuable information on: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eThe current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective \u003c\/li\u003e \u003cli\u003eSupervised vs. unsupervised learning for regression, classification, and clustering problems\u003c\/li\u003e \u003cli\u003eDetails explainable and causal methods for practical engineering problems\u003c\/li\u003e \u003cli\u003eDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis\u003c\/li\u003e \u003cli\u003eA framework for machine learning adoption and application, covering key questions commonly faced by practitioners\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003ci\u003eThis textbook \u003c\/i\u003eis a must-have reference for undergraduate\/graduate students to learn concepts on the use of \u003ci\u003emachine learning\u003c\/i\u003e, for scientists\/researchers to learn how to integrate \u003ci\u003emachine learning\u003c\/i\u003e into civil and environmental engineering, and for design\/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eM. Z. Naser\u003c\/b\u003e is a tenure-track faculty member at the School of Civil and Environmental Engineering \u0026amp; Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) at Clemson University, USA. Dr. Naser has co-authored over 100 publications and has 10 years of experience in structural engineering and AI. His research interest spans causal \u0026amp; explainable AI methodologies to discover new knowledge hidden within the domains of structural \u0026amp; fire engineering and materials science to realize functional, sustainable, and resilient infrastructure. He is a registered professional engineer and a member of various international editorial boards and building committees.\u003cbr\u003e\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":50390429368594,"sku":"9781119897606","price":68.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_5a6a3a0d-3f62-4cce-8496-5f02a711face.jpg?v=1728947735","url":"https:\/\/surprise-castle.myshopify.com\/products\/machine-learning-for-civil-and-environmental-engineers-a-practical-approach-to-data-driven-analysis-explainability-and-causality-9781119897606","provider":"Surprise Castle","version":"1.0","type":"link"}