A New Application Area of ANN and ANFIS: Determination of Earthquake Load Reduction Factor of Prefabricated Industrial Buildings

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dc.contributor.authorCeylan, M.
dc.contributor.authorArslan, M. H.
dc.contributor.authorCeylan, R.
dc.contributor.authorKaltakcı, M. Y.
dc.contributor.authorÖzbay, Y.
dc.date.accessioned2020-03-26T17:46:42Z
dc.date.available2020-03-26T17:46:42Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe earthquake load reduction factor, R, is one of the most important parameters in the design stage of a building. Significant damages and failures were experienced on prefabricated reinforced concrete structures during the last earthquakes in Turkey and the experts agreed that they resulted mainly from the incorrectly selected earthquake load reduction factor, R. In this study, an attempt was made to estimate the R coefficient for prefabricated industrial structures having a single storey, one and two bays, which are commonly constructed for manufacturing andwarehouse operation with variable dimensions. According to the selected variable dimensions, 280 sample (140 samples for one bay (S-1) and 140 samples for two bays (S-2)) frames' load-displacement relations were computed using pushover analysis and the earthquake load reduction factor, R, was calculated for each frame. Then, formulated three-layered artificial neural network methods (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were trained by using 214 of the 280 sample frames. Then, the methods were tested with the other 66 sample frames. Accuracy rates were found to be about 94% and 96% for ANN and ANFIS, respectively. The use of ANN and ANFIS provided an alternative way for estimating the R and it also showed that ANFIS estimated R more successfully than ANN.en_US
dc.description.sponsorshipCoordinatorship of Selcuk University's Scientific Research ProjectsSelcuk Universityen_US
dc.description.sponsorshipThe authors thank Professor Dr Semih Tezcan (Bogazici University) and the engineers at YESA Company (Istanbul) for their help. The project is supported by the Coordinatorship of Selcuk University's Scientific Research Projects.en_US
dc.identifier.citationCeylan, M., Arslan, M. H., Ceylan, R., Kaltakcı, M. Y., Özbay, Y., (2010). A New Application Area of ANN and ANFIS: Determination of Earthquake Load Reduction Factor of Prefabricated Industrial Buildings. Civil Engineering and Environmental Systems, 27(1), 53-69. Doi: 10.1080/10286600802506726
dc.identifier.doi10.1080/10286600802506726en_US
dc.identifier.endpage69en_US
dc.identifier.issn1028-6608en_US
dc.identifier.issn1029-0249en_US
dc.identifier.issue1en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage53en_US
dc.identifier.urihttps://dx.doi.org/10.1080/10286600802506726
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24522
dc.identifier.volume27en_US
dc.identifier.wosWOS:000274531800004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorCeylan, M.
dc.institutionauthorArslan, M. H.
dc.institutionauthorCeylan, R.
dc.institutionauthorKaltakcı, M. Y.
dc.institutionauthorÖzbay, Y.
dc.language.isoenen_US
dc.publisherTaylor & Francis Ltden_US
dc.relation.ispartofCivil Engineering and Environmental Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectAdaptive neuro-fuzzy inference systemen_US
dc.subjectEarthquake load reduction factoren_US
dc.subjectPrefabricated industrial buildingsen_US
dc.subjectDamageen_US
dc.titleA New Application Area of ANN and ANFIS: Determination of Earthquake Load Reduction Factor of Prefabricated Industrial Buildingsen_US
dc.typeArticleen_US

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