Using group method of data handling (GMDH) neural network to predict the maximum stress on elastomeric layers in spherical elastomeric bearings

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dc.authoridMakaraci, Murat/0000-0002-7952-1989
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorMakaraci, Murat
dc.contributor.authorBayraklilar, Said
dc.contributor.authorKuncan, Melih
dc.date.accessioned2024-12-24T19:30:25Z
dc.date.available2024-12-24T19:30:25Z
dc.date.issued2021
dc.departmentSiirt Üniversitesi
dc.description.abstractWhile many studies on planar elastomeric bearings have attracted attention in the international literature, there are very few studies on spherical elastomeric bearings due to their multi-layered and design difficulties. While elastomeric bearings are rigid against loads perpendicular to the layers, they are flexible against the loads parallel to the layers. In this way, spherical elastomeric bearings provide rigid against the central force caused by the rotation of the helicopter propeller, and flexibility against the blade's flapping and rotational movement. In this study, the Group Method of Data Handling (GMDH) model is used to estimate the maximum stresses in elastomeric layers in a spherical elastomeric bearing under compression and angular displacement loading. theta (angular displacement loading), P (pressure loading), a (radius of axis), beta 0 (first joint angle), cos (beta 0) (first joint angle cosine value), beta 1 (second joint angle), beta 2 (third joint angle), phi t (cone angle), phi p (angle between the direction of the pressure loading and the plane perpendicular to the elastomer layer), cos (phi p), D (elastomer layer outer diameter), ne (elastomer layer number), d (elastomer layer hole diameter) and H (elastomer layer thickness) were used as input features for GMDH model. The results obtained with GMDH were also compared with different machine learning methods such as ANN, SVM, RF. According to the results obtained, GMDH model was found to be more successful than other models in estimating spherical elastomeric bearing.
dc.identifier.doi10.17341/gazimmfd.722514
dc.identifier.endpage1345
dc.identifier.issn1300-1884
dc.identifier.issn1304-4915
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85107818299
dc.identifier.scopusqualityQ2
dc.identifier.startpage1332
dc.identifier.trdizinid1138855
dc.identifier.urihttps://doi.org/10.17341/gazimmfd.722514
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1138855
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7523
dc.identifier.volume36
dc.identifier.wosWOS:000655278700012
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isotr
dc.publisherGazi Univ, Fac Engineering Architecture
dc.relation.ispartofJournal of The Faculty of Engineering and Architecture of Gazi University
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectABAQUS
dc.subjectnonlinear finite element analysis
dc.subjectair vehicles
dc.subjecthyper-elasticity
dc.subjectspherical elastomeric bearing
dc.subjectgroup method of data handling
dc.titleUsing group method of data handling (GMDH) neural network to predict the maximum stress on elastomeric layers in spherical elastomeric bearings
dc.title.alternativeVeri işleme grup yöntemi (VİGY) sinir ağı kullanılarak küresel elastomerik yataklarda elastomer tabakalar üzerindeki maksimum gerilmenin tahmini
dc.typeArticle

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