Optimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEM

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dc.contributor.authorMadenci, Emrah
dc.contributor.authorGulcu, Saban
dc.date.accessioned2024-02-23T14:31:25Z
dc.date.available2024-02-23T14:31:25Z
dc.date.issued2020
dc.departmentNEÜen_US
dc.description.abstractArtificial neural networks (ANNs) are known as intelligent methods for modeling the behavior of physical phenomena because of it is a soft computing technique and takes data samples rather than entire data sets to arrive at solutions, which saves both time and money. ANN is successfully used in the civil engineering applications which are suitable examining the complicated relations between variables. Functionally graded materials (FGMs) are advanced composites that successfully used in various engineering design. The FGMs are nonhomogeneous materials and made of two different type of materials. In the present study, the bending analysis of functionally graded material (FGM) beams presents on theoretical based on combination of mixed-finite element method, Gateaux differential and Timoshenko beam theory. The main idea in this study is to build a model using ANN with four parameters that are: Young's modulus ratio (E-t/E-b), a shear correction factor (k(s)), power-law exponent (n) and length to thickness ratio (L/h). The output data is the maximum displacement (w). In the experiments: 252 different data are used. The proposed ANN model is evaluated by the correlation of the coefficient (R), MAE and MSE statistical methods. The ANN model is very good and the maximum displacement can be predicted in ANN without attempting any experiments.en_US
dc.description.sponsorshipNecmettin Erbakan University, Natural Scientific Research Project (BAP) [181219012]en_US
dc.description.sponsorshipThe research described in this paper was financially supported by the Necmettin Erbakan University, Natural Scientific Research Project (BAP) Project No: 181219012en_US
dc.identifier.doi10.12989/sem.2020.75.5.633
dc.identifier.endpage642en_US
dc.identifier.issn1225-4568
dc.identifier.issn1598-6217
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85092101225en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage633en_US
dc.identifier.urihttps://doi.org/10.12989/sem.2020.75.5.633
dc.identifier.urihttps://hdl.handle.net/20.500.12452/15173
dc.identifier.volume75en_US
dc.identifier.wosWOS:000577107300009en_US
dc.identifier.wosqualityQ2en_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.subjectFunctionally Graded Material Beamen_US
dc.subjectArtificial Neural Networksen_US
dc.subjectMixed Finite Element Methoden_US
dc.subjectDisplacement Dataen_US
dc.titleOptimization of flexure stiffness of FGM beams via artificial neural networks by mixed FEMen_US
dc.typeArticleen_US

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