Prediction of force reduction factor (R) of prefabricated industrial buildings using neural networks

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dc.contributor.authorArslan, M. Hakan
dc.contributor.authorCeylan, Murat
dc.contributor.authorKaltakci, M. Yasar
dc.contributor.authorOzbay, Yueksel
dc.date.accessioned2020-03-26T17:17:56Z
dc.date.available2020-03-26T17:17:56Z
dc.date.issued2007
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe force (load) reduction factor, R, which is one of the most important parameters in earthquake load calculation, is independent of the dimensions of the structure but is defined on the basis of the load bearing system of the structure as defined in earthquake codes. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last three major earthquakes in Turkey (Adana 1998, Kocaeli 1999, Duzce 1999) and the experts are still discussing the main reasons of those failures. Most of them agreed that they resulted mainly from the earthquake force reduction factor, R that is incorrectly selected during design processes, in addition to all other detailing errors. Thus this wide spread damages caused by the earthquake to prefabricated structures aroused suspicion about the correctness of the R coefficient recommended in the current Turkish Earthquake Codes (TEC - 98). In this study, an attempt was made for an approximate determination of R coefficient for widely utilized prefabricated structure types (single-floor single-span) with variable dimensions. According to the selecting variable dimensions, 140 sample frames were computed using pushover analysis. The force reduction factor R was calculated by load-displacement curves obtained pushover analysis for each frame. Then, formulated artificial neural network method was trained by using 107 of the 140 sample frames. For the training various algorithms were used. The method was applied and used for the prediction of the R rest 33 frames with about 92% accuracy. The paper also aims at proposing the authorities to change the R coefficient values predicted in TEC - 98 for prefabricated concrete structures.en_US
dc.identifier.doi10.12989/sem.2007.27.2.117en_US
dc.identifier.endpage134en_US
dc.identifier.issn1225-4568en_US
dc.identifier.issn1598-6217en_US
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage117en_US
dc.identifier.urihttps://dx.doi.org/10.12989/sem.2007.27.2.117
dc.identifier.urihttps://hdl.handle.net/20.500.12395/21542
dc.identifier.volume27en_US
dc.identifier.wosWOS:000249580500001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTECHNO-PRESSen_US
dc.relation.ispartofSTRUCTURAL ENGINEERING AND MECHANICSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectneural networken_US
dc.subjectforce reduction factoren_US
dc.subjectprefabricated industrial buildingsen_US
dc.titlePrediction of force reduction factor (R) of prefabricated industrial buildings using neural networksen_US
dc.typeArticleen_US

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