Design Force Estimation Using Artificial Neural Network for Groups of Four Cylindrical Silos

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dc.contributor.authorYüksel, S. Bahadır
dc.contributor.authorArslan, M. Hakan
dc.date.accessioned2020-03-26T17:47:45Z
dc.date.available2020-03-26T17:47:45Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe computation of design forces for the reinforced concrete groups of four cylindrical silos (GFCS) is fairly difficult because of the continuity of the walls between the adjacent silos. In this study, the efficiency of the artificial neural network (ANN) in predicting the design forces and the design moments of the GFCS due to interstice and internal loadings was investigated. Previously obtained finite element (FE) analyses results in the literature were used to train and test the ANN models. Each parameter (silo wall thickness, intersection wall thickness and the central angle spanning the intersection walls of the GFCS) affecting design forces and moments was set to be an input vector. The outputs of the ANN models would be the bending moments, hoop forces and shear forces at the supports and crowns of the interstice walls due to interstice loadings; the maximum axial forces and maximum bending moments at the external walls due to internal loadings. All the outputs of the ANN models were trained and tested by three-layered 11 back-propagation methods widely used in the literature. The obtained results presented that these 11 different methods were capable of predicting the design forces and the design moments at the interstice and external walls of the GFCS used in the training and testing phases of the study.en_US
dc.identifier.citationYüksel, S. B., Arslan, M. H., (2010). Design Force Estimation Using Artificial Neural Network for Groups of Four Cylindrical Silos. Advances in Structural Engineering, 13(4), 681-693. Doi: 10.1260/1369-4332.13.4.681
dc.identifier.doi10.1260/1369-4332.13.4.681en_US
dc.identifier.endpage693en_US
dc.identifier.issn1369-4332en_US
dc.identifier.issn2048-4011en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage681en_US
dc.identifier.urihttps://dx.doi.org/10.1260/1369-4332.13.4.681
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24760
dc.identifier.volume13en_US
dc.identifier.wosWOS:000280465600011en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorYüksel, S. Bahadır
dc.institutionauthorArslan, M. Hakan
dc.language.isoenen_US
dc.publisherSage Publications Incen_US
dc.relation.ispartofAdvances in Structural Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectgrouped silosen_US
dc.subjectinterstice loadingen_US
dc.subjectinternal loadingen_US
dc.subjectinterstice wallsen_US
dc.subjectintersection wallsen_US
dc.subjectartificial neural networksen_US
dc.subjectback-propagation methodsen_US
dc.titleDesign Force Estimation Using Artificial Neural Network for Groups of Four Cylindrical Silosen_US
dc.typeArticleen_US

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