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dc.contributor.authorBashir, Rizwan*
dc.contributor.authorAshour, Ashraf F.*
dc.date.accessioned2015-12-09T11:51:37Z
dc.date.available2015-12-09T11:51:37Z
dc.date.issued2012-12
dc.identifier.citationBashir R and Ashour AF (2012) Neural network modelling for shear strength of concrete members reinforced with FRP bars. Composites: Part B: Engineering, 43 (8): 3198-3207.en_US
dc.identifier.urihttp://hdl.handle.net/10454/7515
dc.descriptionyesen_US
dc.description.abstractThis paper investigates the feasibility of using artificial neural networks (NNs) to predict the shear capacity of concrete members reinforced longitudinally with fibre reinforced polymer (FRP) bars, and without any shear reinforcement. An experimental database of 138 test specimens failed in shear is created and used to train and test NNs as well as to assess the accuracy of three existing shear design methods. The created NN predicted to a high level of accuracy the shear capacity of FRP reinforced concrete members. Garson index was employed to identify the relative importance of the influencing parameters on the shear capacity based on the trained NNs weightings. A parametric analysis was also conducted using the trained NN to establish the trend of the main influencing variables on the shear capacity. Many of the assumptions made by the shear design methods are predicted by the NN developed; however, few are inconsistent with the NN predictions.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/j.compositesb.2012.04.011en_US
dc.rights© 2012 Elsevier. Reproduced in accordance with the publisher's self-archiving policy.en_US
dc.subjectConcrete; Fibre reinforced polymer (FRP) bars; Shear; Strength; Prediction; Computational modelling; Neural network modelling; Statistical properties; Statistical methodsen_US
dc.titleNeural network modelling for shear strength of concrete members reinforced with FRP barsen_US
dc.status.refereedyesen_US
dc.date.Accepted2012-04-10
dc.typeArticleen_US
dc.type.versionAccepted Manuscripten_US
refterms.dateFOA2018-07-25T14:43:45Z


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