Shear strength predicting of FRP-strengthened RC beams by using artificial neural networks

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dc.contributor.authorYavuz, Gunnur
dc.contributor.authorArslan, Musa Hakan
dc.contributor.authorBaykan, Omer Kaan
dc.date.accessioned2020-03-26T18:58:29Z
dc.date.available2020-03-26T18:58:29Z
dc.date.issued2014
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, the efficiency of artificial neural networks (ANN) in predicting the shear strength of reinforced concrete (RC) beams, strengthened by means of externally bonded fiber-reinforced polymers (FRP), is explored. Experimental data of 96 rectangular RC beams from an existing database in the literature were used to develop the ANN model. Eight different input parameters affecting the shear strength were selected for creating the ANN structure. Each parameter was arranged in an input vector and a corresponding output vector that includes the shear strength of the RC beam. For all outputs, the ANN model was trained and tested using a three-layered back-propagation method. The initial performance of back-propagation was evaluated and discussed. In addition, the accuracy of well-known building codes in predicting the shear strength of FRP-strengthened RC beams was also examined, in a comparable way, using same test data. The study shows that the ANN model gives reasonable predictions of the ultimate shear strength of RC beams (R-2 approximate to 0.93). Moreover, the study concludes that the ANN model predicts the shear strength of FRP-strengthened RC beams better than existing building code approaches.en_US
dc.identifier.doi10.1515/secm-2013-0002en_US
dc.identifier.endpage255en_US
dc.identifier.issn0792-1233en_US
dc.identifier.issn2191-0359en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage239en_US
dc.identifier.urihttps://dx.doi.org/10.1515/secm-2013-0002
dc.identifier.urihttps://hdl.handle.net/20.500.12395/31129
dc.identifier.volume21en_US
dc.identifier.wosWOS:000332226400012en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWALTER DE GRUYTER GMBHen_US
dc.relation.ispartofSCIENCE AND ENGINEERING OF COMPOSITE MATERIALSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectartificial neural networken_US
dc.subjectbeamen_US
dc.subjectexternally bonded FRPen_US
dc.subjectshear strengthen_US
dc.subjectstrengtheningen_US
dc.titleShear strength predicting of FRP-strengthened RC beams by using artificial neural networksen_US
dc.typeArticleen_US

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