Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations

In recent years, numerous experimental tests were done on the concrete beams reinforced with the fiber-reinforced polymer (FRP). In this way, some equations were proposed to estimate the shear strength of the beams reinforced with FRP. The aim of this study is to explore the feasibility of using a f...

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Autores principales:	Mahmood Akbari, Vahid Jafari Deligani, Hamid Nezaminia
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2017
Materias:	concrete beam fiber reinforced composite shear strength artificial neural network publication no. 345 aci 440 Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/a223a2c7865b4339b8ac86ba6108b3a7
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spelling	oai:doaj.org-article:a223a2c7865b4339b8ac86ba6108b3a72021-11-08T15:46:54ZShear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations2476-39772538-261610.22065/jsce.2017.80891.1141https://doaj.org/article/a223a2c7865b4339b8ac86ba6108b3a72017-12-01T00:00:00Zhttps://www.jsce.ir/article_46011_8734b9b6df26cf55dd2c339fc58ae399.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616In recent years, numerous experimental tests were done on the concrete beams reinforced with the fiber-reinforced polymer (FRP). In this way, some equations were proposed to estimate the shear strength of the beams reinforced with FRP. The aim of this study is to explore the feasibility of using a feed-forward artificial neural network (ANN) model to predict the ultimate shear strength of the beams strengthened with FRP composites. For this purpose, a database consists of 304 reinforced FRP concrete beams have been collected from the available articles on the analysis of shear behavior of these beams. The inputs to the ANN model consists of the 11 variables including the geometric dimensions of the section, steel reinforcement amount, FRP amount and the properties of the concrete, steel reinforcement and FRP materials while the output variable is the shear strength of the FRP beam. To assess the performance of the ANN model for estimating the shear strength of the reinforced beams, the outputs of the ANN are compared to those of equations of the Iranian code (Publication No. 345) and the American code (ACI 440). The comparisons between the outputs of Iran and American regulations with those of the proposed model indicates that the predictive power of this model is much better than the experimental codes. Specifically, for under study data, mean absolute relative error (MARE) criteria is 13%, 34% and 39% for the ANN model, the American and the Iranian codes, respectively.Mahmood AkbariVahid Jafari DeliganiHamid NezaminiaIranian Society of Structrual Engineering (ISSE)articleconcrete beamfiber reinforced compositeshear strengthartificial neural networkpublication no. 345aci 440Bridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 4, Iss 4, Pp 79-97 (2017)
institution	DOAJ
collection	DOAJ
language	FA
topic	concrete beam fiber reinforced composite shear strength artificial neural network publication no. 345 aci 440 Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	concrete beam fiber reinforced composite shear strength artificial neural network publication no. 345 aci 440 Bridge engineering TG1-470 Building construction TH1-9745 Mahmood Akbari Vahid Jafari Deligani Hamid Nezaminia Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
description	In recent years, numerous experimental tests were done on the concrete beams reinforced with the fiber-reinforced polymer (FRP). In this way, some equations were proposed to estimate the shear strength of the beams reinforced with FRP. The aim of this study is to explore the feasibility of using a feed-forward artificial neural network (ANN) model to predict the ultimate shear strength of the beams strengthened with FRP composites. For this purpose, a database consists of 304 reinforced FRP concrete beams have been collected from the available articles on the analysis of shear behavior of these beams. The inputs to the ANN model consists of the 11 variables including the geometric dimensions of the section, steel reinforcement amount, FRP amount and the properties of the concrete, steel reinforcement and FRP materials while the output variable is the shear strength of the FRP beam. To assess the performance of the ANN model for estimating the shear strength of the reinforced beams, the outputs of the ANN are compared to those of equations of the Iranian code (Publication No. 345) and the American code (ACI 440). The comparisons between the outputs of Iran and American regulations with those of the proposed model indicates that the predictive power of this model is much better than the experimental codes. Specifically, for under study data, mean absolute relative error (MARE) criteria is 13%, 34% and 39% for the ANN model, the American and the Iranian codes, respectively.
format	article
author	Mahmood Akbari Vahid Jafari Deligani Hamid Nezaminia
author_facet	Mahmood Akbari Vahid Jafari Deligani Hamid Nezaminia
author_sort	Mahmood Akbari
title	Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
title_short	Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
title_full	Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
title_fullStr	Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
title_full_unstemmed	Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations
title_sort	shear strength estimation of the concrete beams reinforced with frp; comparison of artificial neural network and equations of regulations
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2017
url	https://doaj.org/article/a223a2c7865b4339b8ac86ba6108b3a7
work_keys_str_mv	AT mahmoodakbari shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations AT vahidjafarideligani shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations AT hamidnezaminia shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations
_version_	1718441691749285888

Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulations

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