Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network

Collapse performance evaluation of structures has been a concern for researchers due to its complexity and uncertainty in modeling and simulation. Concentrate plastic hinges are best candidates for modeling collapse behavior of structures. Collapse fragility curves are affected by various sources of...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mohammad Amin Bayari, Naser Shabakhty, Esmaeel Izadi Zaman Abadi
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2021
Materias:	collapse fragility curves modeling uncertainties lhs simulation artificial neural network response surface method Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/62a979978acc452daf15178160503bfb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:62a979978acc452daf15178160503bfb
record_format	dspace
spelling	oai:doaj.org-article:62a979978acc452daf15178160503bfb2021-11-08T15:55:01ZCollapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network2476-39772538-261610.22065/jsce.2020.187419.1871https://doaj.org/article/62a979978acc452daf15178160503bfb2021-08-01T00:00:00Zhttps://www.jsce.ir/article_102738_f49d87612dafe0337ecbf9b9dd34d05c.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616Collapse performance evaluation of structures has been a concern for researchers due to its complexity and uncertainty in modeling and simulation. Concentrate plastic hinges are best candidates for modeling collapse behavior of structures. Collapse fragility curves are affected by various sources of uncertainty. Existing uncertainties in modified Ibarra and Krawinkler moment-rotation model for concrete moment frame buildings were investigated in this paper. LHS simulation method was used to generate random variables considering the correlation among modeling uncertainties in one component and two structural components. Collapse responses including mean collapse capacity and standard deviation were obtained for each simulation by generating random samples for uncertainties using incremental dynamic analysis (IDA). As much effort is needed for implementation of IDA, MLP artificial neural networks, GMDH artificial neural network and response surface method were used to estimate and anticipate the collapse behavior of the structure. Results show that using above methods will lead to high accuracy anticipations with an error of less than 10% for GMDH neural network and an error of less than 7% for MLP and response surface methods.Mohammad Amin BayariNaser ShabakhtyEsmaeel Izadi Zaman AbadiIranian Society of Structrual Engineering (ISSE)articlecollapse fragility curvesmodeling uncertaintieslhs simulationartificial neural networkresponse surface methodBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 8, Iss 6, Pp 59-80 (2021)
institution	DOAJ
collection	DOAJ
language	FA
topic	collapse fragility curves modeling uncertainties lhs simulation artificial neural network response surface method Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	collapse fragility curves modeling uncertainties lhs simulation artificial neural network response surface method Bridge engineering TG1-470 Building construction TH1-9745 Mohammad Amin Bayari Naser Shabakhty Esmaeel Izadi Zaman Abadi Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
description	Collapse performance evaluation of structures has been a concern for researchers due to its complexity and uncertainty in modeling and simulation. Concentrate plastic hinges are best candidates for modeling collapse behavior of structures. Collapse fragility curves are affected by various sources of uncertainty. Existing uncertainties in modified Ibarra and Krawinkler moment-rotation model for concrete moment frame buildings were investigated in this paper. LHS simulation method was used to generate random variables considering the correlation among modeling uncertainties in one component and two structural components. Collapse responses including mean collapse capacity and standard deviation were obtained for each simulation by generating random samples for uncertainties using incremental dynamic analysis (IDA). As much effort is needed for implementation of IDA, MLP artificial neural networks, GMDH artificial neural network and response surface method were used to estimate and anticipate the collapse behavior of the structure. Results show that using above methods will lead to high accuracy anticipations with an error of less than 10% for GMDH neural network and an error of less than 7% for MLP and response surface methods.
format	article
author	Mohammad Amin Bayari Naser Shabakhty Esmaeel Izadi Zaman Abadi
author_facet	Mohammad Amin Bayari Naser Shabakhty Esmaeel Izadi Zaman Abadi
author_sort	Mohammad Amin Bayari
title	Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
title_short	Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
title_full	Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
title_fullStr	Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
title_full_unstemmed	Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network
title_sort	collapse fragility curves development with considering of modeling uncertainties using lhs simulation and artificial neural network
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2021
url	https://doaj.org/article/62a979978acc452daf15178160503bfb
work_keys_str_mv	AT mohammadaminbayari collapsefragilitycurvesdevelopmentwithconsideringofmodelinguncertaintiesusinglhssimulationandartificialneuralnetwork AT nasershabakhty collapsefragilitycurvesdevelopmentwithconsideringofmodelinguncertaintiesusinglhssimulationandartificialneuralnetwork AT esmaeelizadizamanabadi collapsefragilitycurvesdevelopmentwithconsideringofmodelinguncertaintiesusinglhssimulationandartificialneuralnetwork
_version_	1718441569126711296

Collapse Fragility Curves Development with Considering of Modeling Uncertainties Using LHS Simulation and Artificial Neural Network

Ejemplares similares