Machine learning for civil & environmental engineers: a practical approach to data-driven analysis, explainability, and causality
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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, NJ
Wiley
[2023]
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| Schlagworte: | |
| Zusammenfassung: | 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,- |
| Beschreibung: | xix, 588 Seiten Illustrationen, Diagramme |
| ISBN: | 9781119897606 |
Internformat
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Datensatz im Suchindex
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| id | DE-604.BV049647911 |
| illustrated | Illustrated |
| index_date | 2024-07-03T23:40:04Z |
| indexdate | 2024-07-10T10:13:07Z |
| institution | BVB |
| isbn | 9781119897606 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034991405 |
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| physical | xix, 588 Seiten Illustrationen, Diagramme |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | Wiley |
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| spelling | Naser, M. Z. Verfasser aut Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality Machine learning for civil and enviromental engineers Hoboken, NJ Wiley [2023] xix, 588 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier 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,- Bautechnik (DE-588)4004955-3 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Bauingenieurwesen Künstliche Intelligenz Mathematische Statistik Datenanalyse, Datenverarbeitung Bautechnik, Umwelttechnik AI Artificial Intelligence Bauingenieur- u. Bauwesen Civil Engineering & Construction Civil Engineering & Construction Special Topics Computer Science Data Analysis Datenanalyse Informatik KI Maschinelles Lernen Spezialthemen Bauingenieur- u. Bauwesen Statistics Statistik Maschinelles Lernen (DE-588)4193754-5 s Bautechnik (DE-588)4004955-3 s DE-604 |
| spellingShingle | Naser, M. Z. Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality Bautechnik (DE-588)4004955-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4004955-3 (DE-588)4193754-5 |
| title | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_alt | Machine learning for civil and enviromental engineers |
| title_auth | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_exact_search | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_exact_search_txtP | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_full | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_fullStr | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_full_unstemmed | Machine learning for civil & environmental engineers a practical approach to data-driven analysis, explainability, and causality |
| title_short | Machine learning for civil & environmental engineers |
| title_sort | machine learning for civil environmental engineers a practical approach to data driven analysis explainability and causality |
| title_sub | a practical approach to data-driven analysis, explainability, and causality |
| topic | Bautechnik (DE-588)4004955-3 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Bautechnik Maschinelles Lernen |
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