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Artificial intelligence and machine learning techniques for civil engineering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence and machine learning techniques for civil engineeringVagelis Plevris, Afaq Ahmad, and Nikos Lagaros, editors.
其他作者:	Plevris, Vagelis,
出版者:	Hershey, Pennsylvania :IGI Global,2023.
面頁冊數:	1 online resource (385 p.)
標題:	Civil engineeringData processing.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-6684-5643-9
ISBN:	9781668456446 (electronic bk.)

Artificial intelligence and machine learning techniques for civil engineering
Artificial intelligence and machine learning techniques for civil engineering[electronic resource] /Vagelis Plevris, Afaq Ahmad, and Nikos Lagaros, editors. - Hershey, Pennsylvania :IGI Global,2023. - 1 online resource (385 p.)

Includes bibliographical references and index.

Chapter 1. Artificial intelligence-assisted building information modelling -- Chapter 2. Deep learning-based damage inspection for concrete structures -- Chapter 3. Machine learning applications for vibration-based structural healthmonitoring -- Chapter 4. Use of AI and ML algorithms in developing closed-form formulae for structural engineering design -- Chapter 5. A predictive regression model for the shear strength of RC knee joint subjected to cyclic load -- Chapter 6. Predicting the fundamental period of light-frame wooden buildings by employing bat algorithm-based artificial neural network -- Chapter 7. Shear capacity of RC elements with transverse reinforcement through a variable-angle truss modelwith machine-learning-calibrated coefficients -- Chapter 8. Groundwater modelling of the saq aquifer using artificial intelligence and hydraulic simulations -- Chapter 9. Reliability analysis of RC code for predicting load-carrying capacityof RCC walls through ANN -- Chapter 10. The value proposition of machine learning in construction management: exploring the trends in construction 4.0 and beyond -- Chapter 11. Explainable safety risk management in construction with unsupervised learning -- Chapter 12. Enhanced stochastic paint optimizer for nonlinear Design of fuzzy logic controllers in steel building structures for the near-fault earthquakes.

"This reference book offers state-of-the-art contributions in the area of AI and its applications in the field of civil engineering presenting methods and implementation of AI and machine learning in multiple facets of civil engineering"--

ISBN: 9781668456446 (electronic bk.)Subjects--Topical Terms:

199279
Civil engineering
--Data processing.Index Terms--Genre/Form:

214472
Electronic books.

LC Class. No.: TA347.A78 / A7935 2023e

Dewey Class. No.: 624.0285

Artificial intelligence and machine learning techniques for civil engineering
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505 0 $a Chapter 1. Artificial intelligence-assisted building information modelling -- Chapter 2. Deep learning-based damage inspection for concrete structures -- Chapter 3. Machine learning applications for vibration-based structural healthmonitoring -- Chapter 4. Use of AI and ML algorithms in developing closed-form formulae for structural engineering design -- Chapter 5. A predictive regression model for the shear strength of RC knee joint subjected to cyclic load -- Chapter 6. Predicting the fundamental period of light-frame wooden buildings by employing bat algorithm-based artificial neural network -- Chapter 7. Shear capacity of RC elements with transverse reinforcement through a variable-angle truss modelwith machine-learning-calibrated coefficients -- Chapter 8. Groundwater modelling of the saq aquifer using artificial intelligence and hydraulic simulations -- Chapter 9. Reliability analysis of RC code for predicting load-carrying capacityof RCC walls through ANN -- Chapter 10. The value proposition of machine learning in construction management: exploring the trends in construction 4.0 and beyond -- Chapter 11. Explainable safety risk management in construction with unsupervised learning -- Chapter 12. Enhanced stochastic paint optimizer for nonlinear Design of fuzzy logic controllers in steel building structures for the near-fault earthquakes.
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