Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks

Reinforced concrete (RC) structural elements such as peripheral beams, beams supporting the canopy, ring beams of circular slabs are common members which could encounter torsional moments. One of the up-to-date and modern techniques for strengthening such beams is using FRP sheets. The increase of s...

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Autores principales:	Hosein Naderpour, Pouyan Fakharian
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2018
Materias:	rc beam torsional strength frp ann torsion Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/a7f06ef806544e4aa632df25740fd328
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spelling	oai:doaj.org-article:a7f06ef806544e4aa632df25740fd3282021-11-08T15:47:59ZPredicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks2476-39772538-261610.22065/jsce.2017.70668.1023https://doaj.org/article/a7f06ef806544e4aa632df25740fd3282018-05-01T00:00:00Zhttps://www.jsce.ir/article_44714_957d4126f7b7536c4a6c06ecdfcf076d.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616Reinforced concrete (RC) structural elements such as peripheral beams, beams supporting the canopy, ring beams of circular slabs are common members which could encounter torsional moments. One of the up-to-date and modern techniques for strengthening such beams is using FRP sheets. The increase of service loads, degradation of mechanical properties and updates in code regulations would cause to the need for retrofit. Using FRP sheets as a strengthening technique would increase the flexure, shear and torsion capacity. It would also change the failure mode and/or plane. In this article, torsional strength prediction of RC beams strengthened by FRP using artificial neural networks has been investigated. Input parameters of ANNs are RC beam width, height, FRP sheet thickness, modulus of elasticity of FRP sheet, yielding stress of longitudinal and transverse steels, the compressive strength of concrete, the effective width of shear strips along beam length, center-to-center space of FRP strips, the angle of wrapping and number of FRP layers. The target parameter is the capacity of the beam in bearing the torsional moment. The results indicate that the idealized neural network having definite number of neurons in hidden layer, can predict the torsional strength of RC beams externally bonded with FRP with a high degree of precision. Considering the mentioned precision, the method could be an efficient alternative for time-consuming and highly cost experimental programs.Hosein NaderpourPouyan FakharianIranian Society of Structrual Engineering (ISSE)articlerc beamtorsional strengthfrpanntorsionBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 5, Iss 1, Pp 20-35 (2018)
institution	DOAJ
collection	DOAJ
language	FA
topic	rc beam torsional strength frp ann torsion Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	rc beam torsional strength frp ann torsion Bridge engineering TG1-470 Building construction TH1-9745 Hosein Naderpour Pouyan Fakharian Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
description	Reinforced concrete (RC) structural elements such as peripheral beams, beams supporting the canopy, ring beams of circular slabs are common members which could encounter torsional moments. One of the up-to-date and modern techniques for strengthening such beams is using FRP sheets. The increase of service loads, degradation of mechanical properties and updates in code regulations would cause to the need for retrofit. Using FRP sheets as a strengthening technique would increase the flexure, shear and torsion capacity. It would also change the failure mode and/or plane. In this article, torsional strength prediction of RC beams strengthened by FRP using artificial neural networks has been investigated. Input parameters of ANNs are RC beam width, height, FRP sheet thickness, modulus of elasticity of FRP sheet, yielding stress of longitudinal and transverse steels, the compressive strength of concrete, the effective width of shear strips along beam length, center-to-center space of FRP strips, the angle of wrapping and number of FRP layers. The target parameter is the capacity of the beam in bearing the torsional moment. The results indicate that the idealized neural network having definite number of neurons in hidden layer, can predict the torsional strength of RC beams externally bonded with FRP with a high degree of precision. Considering the mentioned precision, the method could be an efficient alternative for time-consuming and highly cost experimental programs.
format	article
author	Hosein Naderpour Pouyan Fakharian
author_facet	Hosein Naderpour Pouyan Fakharian
author_sort	Hosein Naderpour
title	Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
title_short	Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
title_full	Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
title_fullStr	Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
title_full_unstemmed	Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks
title_sort	predicting the torsional strength of reinforced concrete beams strengthened with frp sheets in terms of artificial neural networks
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2018
url	https://doaj.org/article/a7f06ef806544e4aa632df25740fd328
work_keys_str_mv	AT hoseinnaderpour predictingthetorsionalstrengthofreinforcedconcretebeamsstrengthenedwithfrpsheetsintermsofartificialneuralnetworks AT pouyanfakharian predictingthetorsionalstrengthofreinforcedconcretebeamsstrengthenedwithfrpsheetsintermsofartificialneuralnetworks
_version_	1718441692531523584

Predicting the Torsional Strength of Reinforced Concrete Beams Strengthened with FRP Sheets in terms of Artificial Neural Networks

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