An integrated machine learning and optimization approach for enhanced strength prediction in riveted joints

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dc.authorid0000-0002-1723-4108
dc.authorid0000-0002-7672-1846
dc.contributor.authorTanrıver, Kürşat
dc.contributor.authorAy, Mustafa
dc.date.accessioned2026-04-22T06:41:23Z
dc.date.available2026-04-22T06:41:23Z
dc.date.issued2026
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Mekatronik Mühendisliği Bölümü
dc.description.abstractIn this study, experimental tests, finite element analysis, and machine learning techniques were integrated to predict the maximum shear stress of riveted joints. First, ten tensile tests were conducted, yielding an average stress of 76.0383 MPa on the plates, and the regression analysis of the stress–strain data demonstrated a 95-99% level of agreement. A finite element analysis performed in Ansys under conditions similar to the experiments, produced a result of 78.875 MPa, which falls within an error margin consistent with the literature. Subsequently, a dataset of 20 samples containing various rivet diameters, plate thicknesses, hole coordinates and tensile loads was generated and used, to train a Regression Decision Tree model in MATLAB. For a new design case, the model predicted 7.480 MPa instead of 7.291 MPa, corresponding to an error of approximately 2.50%. When the dataset was expanded to 50 samples, this deviation decreased to 0.82%, indicating a significant improvement in accuracy. Overall, the results demonstrate that the machine learning model rapidly improves, as additional data become available and can provide reliable, fast predictions, including the effects of bending moments, offering a promising approach that may reduce the need for extensive experimental and numerical analyses.
dc.identifier.citationTanrıver, K., & Ay, M. (2026). An integrated machine learning and optimization approach for enhanced strength prediction in riveted joints. Acta Polytechnica Hungarica, 23(3), pp. 51-75. https://doi.org/10.12700/APH.23.3.2026.3.4
dc.identifier.doi10.12700/APH.23.3.2026.3.4
dc.identifier.endpage75
dc.identifier.issn1785-8860
dc.identifier.issue3
dc.identifier.scopusqualityQ1
dc.identifier.startpage51
dc.identifier.urihttps://doi.org/10.12700/APH.23.3.2026.3.4
dc.identifier.urihttps://hdl.handle.net/20.500.13055/1443
dc.identifier.volume23
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynak.otherSCI-E - Science Citation Index Expanded
dc.institutionauthorTanrıver, Kürşat
dc.institutionauthorid0000-0002-1723-4108
dc.language.isoen
dc.publisherObuda University
dc.relation.ispartofActa Polytechnica Hungarica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectANSYS
dc.subjectExperimental Work
dc.subjectFinite Element Analysis
dc.subjectMachine Learning
dc.subjectMATLAB
dc.subjectRiveted Joints
dc.titleAn integrated machine learning and optimization approach for enhanced strength prediction in riveted joints
dc.typeArticle
dspace.entity.typePublication

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