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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Main Authors: Mahmood Akbari, Vahid Jafari Deligani, Hamid Nezaminia
Format: Article
Language:fas
Published: Iranian Society of Structrual Engineering (ISSE) 2017-12-01
Series:Journal of Structural and Construction Engineering
Subjects:
Online Access:http://www.jsce.ir/article_46011_8734b9b6df26cf55dd2c339fc58ae399.pdf
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spelling doaj-a223a2c7865b4339b8ac86ba6108b3a72020-11-24T22:55:16ZfasIranian Society of Structrual Engineering (ISSE)Journal of Structural and Construction Engineering2476-39772538-26162017-12-0144799710.22065/jsce.2017.80891.114146011Shear strength estimation of the concrete beams reinforced with FRP; comparison of artificial neural network and equations of regulationsMahmood Akbari0Vahid Jafari Deligani1Hamid Nezaminia2Assistant Professor, Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan, IranMSc student, Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan, IranMSc of Structure Engineering, Department of Civil Engineering, Faculty of Engineering, University of Kashan, Kashan, IranIn 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.http://www.jsce.ir/article_46011_8734b9b6df26cf55dd2c339fc58ae399.pdfConcrete beamFiber reinforced compositeShear StrengthArtificial neural networkPublication No. 345ACI 440
collection DOAJ
language fas
format Article
sources DOAJ
author Mahmood Akbari
Vahid Jafari Deligani
Hamid Nezaminia
spellingShingle 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
Journal of Structural and Construction Engineering
Concrete beam
Fiber reinforced composite
Shear Strength
Artificial neural network
Publication No. 345
ACI 440
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)
series Journal of Structural and Construction Engineering
issn 2476-3977
2538-2616
publishDate 2017-12-01
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.
topic Concrete beam
Fiber reinforced composite
Shear Strength
Artificial neural network
Publication No. 345
ACI 440
url http://www.jsce.ir/article_46011_8734b9b6df26cf55dd2c339fc58ae399.pdf
work_keys_str_mv AT mahmoodakbari shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations
AT vahidjafarideligani shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations
AT hamidnezaminia shearstrengthestimationoftheconcretebeamsreinforcedwithfrpcomparisonofartificialneuralnetworkandequationsofregulations
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