Predicting of Torsional Strength of RC Beams by Using Different Artificial Neural Network Algorithms and Building Codes

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dc.contributor.authorArslan, M. Hakan
dc.date.accessioned2020-03-26T18:04:49Z
dc.date.available2020-03-26T18:04:49Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIn this study, the efficiency of different artificial neural networks (ANNs) in predicting the torsional strength of reinforced concrete (RC) beams is firstly explored. Experimental data of 76 rectangular RC beams from an existing database in the literature were used to develop ANN model. The input parameters affecting the torsional strength were selected as cross-sectional area of beams, dimensions of closed stirrups, spacing of stirrups, cross-sectional area of one-leg of closed stirrup, yield strength of stirrup and longitudinal reinforcement, steel ratio of stirrups, steel ratio of longitudinal reinforcement and concrete compressive strength. Each parameter was arranged in an input vector and a corresponding output vector that includes the torsional strength of RC beam. For all outputs, the ANN models were trained and tested using three layered 11 back-propagation methods. The initial performance evaluation of 11 different back propagations was compared with each other. In addition to these, the paper presents a short review of the well-known building codes provisions for the design of RC beams under pure torsion. The accuracy of the codes in predicting the torsional strength of RC beams was also examined with comparable way by using same test data. The study shows that the ANN models give reasonable predictions of the ultimate torsional strength of RC beams (R-2 approximate to 0.988). Moreover, the study concludes that all ANN models predict the torsional strength of RC beams better than existing building code equations for torsion.en_US
dc.identifier.citationArslan, M. H., (2010). Predicting of Torsional Strength of RC Beams by Using Different Artificial Neural Network Algorithms and Building Codes. Advances in Engineering Software, (41), 946-955. Doi: 10.1016/j.advengsoft.2010.05.009
dc.identifier.doi10.1016/j.advengsoft.2010.05.009en_US
dc.identifier.endpage955en_US
dc.identifier.issn0965-9978en_US
dc.identifier.issn1873-5339en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage946en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.advengsoft.2010.05.009
dc.identifier.urihttps://hdl.handle.net/20.500.12395/25169
dc.identifier.volume41en_US
dc.identifier.wosWOS:000281267100007en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorArslan, M. Hakan
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofAdvances in Engineering Softwareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectReinforced concrete beamen_US
dc.subjectArtificial neural networken_US
dc.subjectTorsional strengthen_US
dc.subjectBuilding codeen_US
dc.subjectTheoretical modelen_US
dc.subjectBack-propagation algorithmen_US
dc.titlePredicting of Torsional Strength of RC Beams by Using Different Artificial Neural Network Algorithms and Building Codesen_US
dc.typeArticleen_US

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