Large Deflection Analysis of Functionally Graded Reinforced Sandwich Beams With Auxetic Core Using Physics-Informed Neural Network

dc.authorscopusid 57222963792
dc.authorscopusid 55196291500
dc.authorscopusid 6603250102
dc.contributor.author Nopour, Reza
dc.contributor.author Fallah, Ali
dc.contributor.author Aghdam, Mohammad Mohammadi
dc.date.accessioned 2025-04-07T18:54:18Z
dc.date.available 2025-04-07T18:54:18Z
dc.date.issued 2025
dc.department Atılım University en_US
dc.department-temp [Nopour, Reza; Aghdam, Mohammad Mohammadi] Amirkabir Univ Technol, Mech Engn Dept, Tehran 158754413, Iran; [Fallah, Ali] Sabanci Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Fallah, Ali] Atılım Univ, Dept Automot Engn, Ankara, Turkiye en_US
dc.description.abstract This paper aims to investigate the large deflection behavior of a sandwich beam reinforced with functionally graded (FG) graphene platelets (GPL) together with an auxetic core, rested on a nonlinear elastic foundation. The nonlinear governing equations of the problem are derived using Hamilton's principle based on the Euler-Bernoulli beam theory for large deflections. Five different distributions are considered to describe the dispersion of GPL in the top and bottom faces of the sandwich beam. The Physics-Informed Neural Network (PINN) method is employed to model the nonlinear deflection of the beam under various boundary conditions. This study highlights the effectiveness of PINN in handling the complexities of nonlinear structural analyses. The findings underscore the impact of the core auxeticity, GPL amount and distribution, and elastic foundation coefficient on the nonlinear deflection of the sandwich beam under different loading scenarios. For instance, using Type I configuration can reduce the deflection of the beam by nearly half compared to using Type IV. Furthermore, a nonlinear foundation with a unit coefficient results in a 48% reduction in deflection compared to the scenario without an elastic foundation. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.doi 10.1080/15397734.2025.2462674
dc.identifier.issn 1539-7734
dc.identifier.issn 1539-7742
dc.identifier.scopus 2-s2.0-85219682171
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1080/15397734.2025.2462674
dc.identifier.wos WOS:001468012000001
dc.identifier.wosquality Q1
dc.institutionauthor Fallah, Ali
dc.language.iso en en_US
dc.publisher Taylor & Francis inc en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 0
dc.subject Nonlinear Bending en_US
dc.subject Auxetic Composites en_US
dc.subject Physics-Informed Neural Network en_US
dc.subject Sandwich Beam en_US
dc.title Large Deflection Analysis of Functionally Graded Reinforced Sandwich Beams With Auxetic Core Using Physics-Informed Neural Network en_US
dc.type Article en_US
dc.wos.citedbyCount 0
dspace.entity.type Publication
relation.isAuthorOfPublication 92163e16-11d7-4e19-8ec0-ba1a59c34089
relation.isAuthorOfPublication.latestForDiscovery 92163e16-11d7-4e19-8ec0-ba1a59c34089

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