Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques

Structural health monitoring of beam–column joints is paramount, as they are critical load-carrying components of reinforced concrete buildings. Evaluating the ultimate joint shear capacity and failure modes of beam–columns, especially in seismic events, is a crucial task, especially in view of life...

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Autores principales:	Giuseppe Santarsiero, Mayank Mishra, Manav Kumar Singh, Angelo Masi
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Reinforced concrete structures Beam–column joint Machine learning ANN Artificial intelligence Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/7983881e561a4ef4b52b5abfe5320c77
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spelling	oai:doaj.org-article:7983881e561a4ef4b52b5abfe5320c772021-11-04T04:43:15ZStructural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques2666-827010.1016/j.mlwa.2021.100190https://doaj.org/article/7983881e561a4ef4b52b5abfe5320c772021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021000955https://doaj.org/toc/2666-8270Structural health monitoring of beam–column joints is paramount, as they are critical load-carrying components of reinforced concrete buildings. Evaluating the ultimate joint shear capacity and failure modes of beam–columns, especially in seismic events, is a crucial task, especially in view of life safety concerns. Traditional methods used to determine the joint shear capacity of beam–column joints are often inaccurate and cumbersome owing to improper accounting of governing parameters that influence beam–column joints’ behaviour. In this study, the performance of machine learning-based structural health monitoring techniques are evaluated in predicting the joint shear capacity and the mode of failure for the exterior beam–column joint taking into account their complex structural behaviour through both numerical modelling and various machine learning techniques. The data used to train and test the model was collected from laboratory experiments and other test data available in the literature. The results indicated the superiority of the proposed particle swarm optimized artificial neural network (PSO-ANN) and XGboost over previously used approaches. Hence, the proposed techniques can be efficiently used for monitoring of structural performance by making informed decision regarding condition assessment of RC buildings.Giuseppe SantarsieroMayank MishraManav Kumar SinghAngelo MasiElsevierarticleReinforced concrete structuresBeam–column jointMachine learningANNArtificial intelligenceCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 6, Iss , Pp 100190- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Reinforced concrete structures Beam–column joint Machine learning ANN Artificial intelligence Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Reinforced concrete structures Beam–column joint Machine learning ANN Artificial intelligence Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Giuseppe Santarsiero Mayank Mishra Manav Kumar Singh Angelo Masi Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
description	Structural health monitoring of beam–column joints is paramount, as they are critical load-carrying components of reinforced concrete buildings. Evaluating the ultimate joint shear capacity and failure modes of beam–columns, especially in seismic events, is a crucial task, especially in view of life safety concerns. Traditional methods used to determine the joint shear capacity of beam–column joints are often inaccurate and cumbersome owing to improper accounting of governing parameters that influence beam–column joints’ behaviour. In this study, the performance of machine learning-based structural health monitoring techniques are evaluated in predicting the joint shear capacity and the mode of failure for the exterior beam–column joint taking into account their complex structural behaviour through both numerical modelling and various machine learning techniques. The data used to train and test the model was collected from laboratory experiments and other test data available in the literature. The results indicated the superiority of the proposed particle swarm optimized artificial neural network (PSO-ANN) and XGboost over previously used approaches. Hence, the proposed techniques can be efficiently used for monitoring of structural performance by making informed decision regarding condition assessment of RC buildings.
format	article
author	Giuseppe Santarsiero Mayank Mishra Manav Kumar Singh Angelo Masi
author_facet	Giuseppe Santarsiero Mayank Mishra Manav Kumar Singh Angelo Masi
author_sort	Giuseppe Santarsiero
title	Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
title_short	Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
title_full	Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
title_fullStr	Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
title_full_unstemmed	Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
title_sort	structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/7983881e561a4ef4b52b5abfe5320c77
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Structural health monitoring of exterior beam–column subassemblies through detailed numerical modelling and using various machine learning techniques

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