Isbn: 9781119897606 - machine learning for civil and environmental engineers: a practical approach to data-driven analysis, explainability, and causality (22 resultados)

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Hardcover. Condición: new. Hardcover. Accessible 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 perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Hardcover. Condición: new. Hardcover. Accessible 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 perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Condición: New. 2023. 1st Edition. hardcover. . . . . .

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Hardback. Condición: New. Accessible 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 perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA 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.…

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Hardcover. Condición: Brand New. 656 pages. 11.14x8.82x1.50 inches. In Stock.

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Buch. Condición: Neu. Neuware - 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.…

Gebunden. Condición: New. M. Z. Naser is a tenure-track faculty member at the School of Civil and Environmental Engineering & Earth Sciences and a member of the Artificial Intelligence Research Institute for Science and Engineering (AIRISE) at Clemson University, USA. Dr. Naser has .

Buch. Condición: Neu. Machine Learning for Civil and Environmental Engineers | A Practical Approach to Data-Driven Analysis, Explainability, and Causality | M Z Naser | Buch | 608 S. | Englisch | 2023 | Wiley | EAN 9781119897606 | Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, product-safety[at]wiley[dot]com | Anbieter: preigu. …

Hardback. Condición: New. Accessible 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 perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA 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.…

Hardcover. Condición: new. Hardcover. Accessible 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 perspectiveSupervised vs. unsupervised learning for regression, classification, and clustering problemsExplainable and causal methods for practical engineering problemsDatabase development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysisA 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…