Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls

Reinforced concrete (RC) shear wall is one of the most widely adopted earthquake-resisting structural elements. Accurate prediction of capacity curves of RC shear walls has been of significant importance since it can convey important information about progressive damage states, the degree of energy...

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Autores principales:	Yicheng Yang, In Ho Cho
Formato:	article
Lenguaje:	EN
Publicado:	Pouyan Press 2021
Materias:	machine learning for capacity curve prediction multiple-target regression model clus shear wall database uncertainty quantification Technology T
Acceso en línea:	https://doaj.org/article/806db179ccc047abaab1d7cbf44f2e4c
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id	oai:doaj.org-article:806db179ccc047abaab1d7cbf44f2e4c
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spelling	oai:doaj.org-article:806db179ccc047abaab1d7cbf44f2e4c2021-12-03T15:12:29ZMultiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls2588-287210.22115/scce.2021.314998.1381https://doaj.org/article/806db179ccc047abaab1d7cbf44f2e4c2021-10-01T00:00:00Zhttp://www.jsoftcivil.com/article_140632_12d83a90d537259e72b061507cf7e96a.pdfhttps://doaj.org/toc/2588-2872Reinforced concrete (RC) shear wall is one of the most widely adopted earthquake-resisting structural elements. Accurate prediction of capacity curves of RC shear walls has been of significant importance since it can convey important information about progressive damage states, the degree of energy absorption, and the maximum strength. Decades-long experimental efforts of the research community established a systematic database of capacity curves, but it is still in its infancy to productively utilize the accumulated data. In the hope of adding a new dimension to earthquake engineering, this study provides a machine learning (ML) approach to predict capacity curves of the RC shear wall based on a multi-target prediction model and fundamental statistics. This paper harnesses bootstrapping for uncertainty quantification and affirms the robustness of the proposed method against erroneous data. Results and validations using more than 200 rectangular RC shear walls show a promising performance and suggest future research directions toward data- and ML-driven earthquake engineering.Yicheng YangIn Ho ChoPouyan Pressarticlemachine learning for capacity curve predictionmultiple-target regression modelclusshear wall databaseuncertainty quantificationTechnologyTENJournal of Soft Computing in Civil Engineering, Vol 5, Iss 4, Pp 90-113 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning for capacity curve prediction multiple-target regression model clus shear wall database uncertainty quantification Technology T
spellingShingle	machine learning for capacity curve prediction multiple-target regression model clus shear wall database uncertainty quantification Technology T Yicheng Yang In Ho Cho Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
description	Reinforced concrete (RC) shear wall is one of the most widely adopted earthquake-resisting structural elements. Accurate prediction of capacity curves of RC shear walls has been of significant importance since it can convey important information about progressive damage states, the degree of energy absorption, and the maximum strength. Decades-long experimental efforts of the research community established a systematic database of capacity curves, but it is still in its infancy to productively utilize the accumulated data. In the hope of adding a new dimension to earthquake engineering, this study provides a machine learning (ML) approach to predict capacity curves of the RC shear wall based on a multi-target prediction model and fundamental statistics. This paper harnesses bootstrapping for uncertainty quantification and affirms the robustness of the proposed method against erroneous data. Results and validations using more than 200 rectangular RC shear walls show a promising performance and suggest future research directions toward data- and ML-driven earthquake engineering.
format	article
author	Yicheng Yang In Ho Cho
author_facet	Yicheng Yang In Ho Cho
author_sort	Yicheng Yang
title	Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
title_short	Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
title_full	Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
title_fullStr	Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
title_full_unstemmed	Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls
title_sort	multiple target machine learning prediction of capacity curves of reinforced concrete shear walls
publisher	Pouyan Press
publishDate	2021
url	https://doaj.org/article/806db179ccc047abaab1d7cbf44f2e4c
work_keys_str_mv	AT yichengyang multipletargetmachinelearningpredictionofcapacitycurvesofreinforcedconcreteshearwalls AT inhocho multipletargetmachinelearningpredictionofcapacitycurvesofreinforcedconcreteshearwalls
_version_	1718373157742575616

Multiple Target Machine Learning Prediction of Capacity Curves of Reinforced Concrete Shear Walls

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