Estimation of curvature and displacement ductility in reinforced concrete buildings

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dc.contributor.authorArslan, M. Hakan
dc.date.accessioned2020-03-26T18:25:33Z
dc.date.available2020-03-26T18:25:33Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractEnsuring sufficient ductility in building load bearing systems and elements of the load bearing system is quite important for their seismic performance. The Seismic Codes stipulate that certain requirements must be met to maintain ductility values above a certain level. The purpose of this study is to determine how ductility values of both elements and load bearing systems vary as parameters related to the conditions specified in the codes change and as estimates of these values are used. With this aim in mind, the curvature ductility in columns and beams of a four-storey Reinforced Concrete (RC) building differs depending on parameters that include the axial load level, longitudinal reinforcement, transverse reinforcement, compression bar ratio and concrete strength. The value of the curvature ductility was found to vary according to the number of parameters and variance range, which was found to be 60 and 135 in the beam section and column section, respectively. Later, a pushover analysis was applied to 540 different statuses of the sample RC system for the same parameters, and the ratio variations and respective displacement (global) ductility of the frames were calculated. The relationship between obtained ductility values with the parameters, as well as the accuracy of the established model, were estimated using regression analyses (Multi-linear and Nonlinear Regression (MLR, NLR)) and 11 various Artificial Neural Networks (ANN) methods. According to the estimation methods, it was found that the test parameters that significantly affect curvature ductility values are not sufficient to explain the displacement ductility values. On the other hand, it was seen that the estimation strength of ANNs proved to be greater than MLR in both curvature ductility and displacement ductility. Outcomes also indicated that the NLR model exhibits superior performance for estimating displacement ductility.en_US
dc.identifier.doi10.1007/s12205-012-0958-1en_US
dc.identifier.endpage770en_US
dc.identifier.issn1226-7988en_US
dc.identifier.issn1976-3808en_US
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage759en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s12205-012-0958-1
dc.identifier.urihttps://hdl.handle.net/20.500.12395/28023
dc.identifier.volume16en_US
dc.identifier.wosWOS:000305833600009en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherKOREAN SOCIETY OF CIVIL ENGINEERS-KSCEen_US
dc.relation.ispartofKSCE JOURNAL OF CIVIL ENGINEERINGen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectearthquakeen_US
dc.subjectductilityen_US
dc.subjectpushover analysisen_US
dc.subjectneural networksen_US
dc.subjectregression analysesen_US
dc.titleEstimation of curvature and displacement ductility in reinforced concrete buildingsen_US
dc.typeArticleen_US

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