Design forces for groups of six cylindrical silos by artificial neural network modelling

Basit Öğe Kaydı
dc.contributor.authorYuksel, Suleyman B.
dc.contributor.authorArslan, Musa H.
dc.date.accessioned2020-03-26T18:24:39Z
dc.date.available2020-03-26T18:24:39Z
dc.date.issued2012
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractA huge amount of various granular materials can be stored in and between the cells of grouped reinforced concrete cylindrical silos. The determination of design forces for reinforced concrete groups of six cylindrical silos requires significant computational effort owing to structural continuity and force transfer between adjacent silos. In this study, the efficiency of artificial neural network models in predicting the design forces and moments of groups of six cylindrical silos due to interstice loadings was investigated. Previously obtained finite-element analysis results in the literature were used to train and test the artificial neural network models. Each parameter (silo wall thickness, intersection wall thickness and the central angle spanning the intersection walls of the groups of six cylindrical silos) affecting design forces and moments was included in the input vector. The outputs of the artificial neural network models are the bending moments, hoop forces and shear forces at the supports and crowns of the interstice walls owing to interstice loadings. All artificial neural network models were trained and tested using 11 different three-layered back-propagation methods widely used in the literature. The results obtained demonstrated that all the back-propagation methods are capable of predicting the design forces and design moments at the interstice walls of the groups of six cylindrical silos.en_US
dc.identifier.doi10.1680/stbu.10.00049en_US
dc.identifier.endpage580en_US
dc.identifier.issn0965-0911en_US
dc.identifier.issue10en_US
dc.identifier.startpage567en_US
dc.identifier.urihttps://dx.doi.org/10.1680/stbu.10.00049
dc.identifier.urihttps://hdl.handle.net/20.500.12395/27884
dc.identifier.volume165en_US
dc.identifier.wosWOS:000311676400004en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherICE PUBLISHINGen_US
dc.relation.ispartofPROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-STRUCTURES AND BUILDINGSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectcodes of practice & standardsen_US
dc.subjectconcrete structuresen_US
dc.subjectsilosen_US
dc.titleDesign forces for groups of six cylindrical silos by artificial neural network modellingen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu