Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP) materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM) FR...

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Autores principales:	Seyed Rohollah Hosseini Vaez, Hosein Naderpour, Mohammad Barati
Formato:	article
Lenguaje:	FA
Publicado:	Iranian Society of Structrual Engineering (ISSE) 2017
Materias:	strengthening fiber reinforced polymer nsm-frp flexural strength artificial neural network Bridge engineering TG1-470 Building construction TH1-9745
Acceso en línea:	https://doaj.org/article/6805d54c00f14fad99032372979b04f9
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spelling	oai:doaj.org-article:6805d54c00f14fad99032372979b04f92021-11-08T15:46:54ZEstimating the behavior of RC beams strengthened with NSM system using artificial neural networks2476-39772538-261610.22065/jsce.2017.44332https://doaj.org/article/6805d54c00f14fad99032372979b04f92017-12-01T00:00:00Zhttps://www.jsce.ir/article_44332_f2537fe81c6a8de661a957d8f2a962e1.pdfhttps://doaj.org/toc/2476-3977https://doaj.org/toc/2538-2616In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP) materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM) FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain) process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.Seyed Rohollah Hosseini VaezHosein NaderpourMohammad BaratiIranian Society of Structrual Engineering (ISSE)articlestrengtheningfiber reinforced polymernsm-frpflexural strengthartificial neural networkBridge engineeringTG1-470Building constructionTH1-9745FAJournal of Structural and Construction Engineering, Vol 4, Iss 4, Pp 16-28 (2017)
institution	DOAJ
collection	DOAJ
language	FA
topic	strengthening fiber reinforced polymer nsm-frp flexural strength artificial neural network Bridge engineering TG1-470 Building construction TH1-9745
spellingShingle	strengthening fiber reinforced polymer nsm-frp flexural strength artificial neural network Bridge engineering TG1-470 Building construction TH1-9745 Seyed Rohollah Hosseini Vaez Hosein Naderpour Mohammad Barati Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
description	In the last decade, conventional materials such as steel and concrete are being replaced by fiber reinforced polymer (FRP) materials for the strengthening of concrete structures. Among the strengthening techniques based on Fiber Reinforced Polymer composites, the use of near-surface mounted (NSM) FRP rods is emerging as a promising technology for increasing flexural and shear strength of deficient concrete, masonry and timber members. An artificial neural network is an information processing tool that is inspired by the way biological nervous systems (such as the brain) process the information. The key element of this tool is the novel structure of the information processing system. In engineering applications, a neural network can be a vector mapper which maps an input vector to an output one. In the present study, a new approach is developed to predict the behavior of strengthened concrete beam using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as elastic modulus of the FRP reinforcement, the ratio of the steel longitudinal reinforcement, dimensions of the beam section, the ratio of the NSM-FRP reinforcement and characteristics of concrete, the output node was the flexural strength of beams. The idealized neural network was employed to generate empirical charts and equations to be used in design. The aim of this study is to investigate the behavior of strengthened RC beam using artificial neural networks.
format	article
author	Seyed Rohollah Hosseini Vaez Hosein Naderpour Mohammad Barati
author_facet	Seyed Rohollah Hosseini Vaez Hosein Naderpour Mohammad Barati
author_sort	Seyed Rohollah Hosseini Vaez
title	Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
title_short	Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
title_full	Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
title_fullStr	Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
title_full_unstemmed	Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks
title_sort	estimating the behavior of rc beams strengthened with nsm system using artificial neural networks
publisher	Iranian Society of Structrual Engineering (ISSE)
publishDate	2017
url	https://doaj.org/article/6805d54c00f14fad99032372979b04f9
work_keys_str_mv	AT seyedrohollahhosseinivaez estimatingthebehaviorofrcbeamsstrengthenedwithnsmsystemusingartificialneuralnetworks AT hoseinnaderpour estimatingthebehaviorofrcbeamsstrengthenedwithnsmsystemusingartificialneuralnetworks AT mohammadbarati estimatingthebehaviorofrcbeamsstrengthenedwithnsmsystemusingartificialneuralnetworks
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Estimating the behavior of RC beams strengthened with NSM system using artificial neural networks

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