Structural Engineering Applications of Artificial Neural Networks

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dc.authoridTR115089en_US
dc.authoridTR188416en_US
dc.contributor.authorBagci, Muhiddin
dc.contributor.authorAltintas, Gokhan
dc.date.accessioned2015-04-01T13:11:43Z
dc.date.available2015-04-01T13:11:43Z
dc.date.issued2007
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractIn this study, infilled planar frames and confined reinforced concrete section have been analysed using Artificial Neural Network (ANN). ANN architecture was chosen in which multi layer, feed forward, and back propagation algorithm was used. The training data of infill frame used were provided by a finite element model in which non-linearity of materials and the structural interface were taken into account under increasing lateral load. Using the proposed analytical model (layered model) were generated the training data for confined reinforced concrete section. Analytical technique uses realistic material models for confined and unconfined concrete. After completing the training phase, verification of the performance of the network was carried out using old (included in training phase) and new (not included in training phase) patterns. The controls conducted in the test phase. The findings of this exercise show that the ANN algorithm can be successfully and easily used within reasonable accuracy in order to decrease computational time in finding infill frame and the moment-curvature relationships of reinforced concrete sections.en_US
dc.identifier.citationJournal of Science and Technology 1 (1), 2007, 43-57tr_TR
dc.identifier.issn1307-3818
dc.language.isoenen_US
dc.publisherBeykent Üniversitesitr_TR
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectArtificial Neural Networktr_TR
dc.subjectFinite Elements Methodtr_TR
dc.subjectInfilled Frametr_TR
dc.subjectConfined Reinforced Concrete Sectiontr_TR
dc.subjectMoment-Curvaturetr_TR
dc.titleStructural Engineering Applications of Artificial Neural Networksen_US
dc.typeArticleen_US

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