A Machine-Learning-Based Model for Buckling Analysis of Thermally Affected Covalently Functionalized Graphene/Epoxy Nanocomposite Beams

In this paper, a machine-learning model is utilized to estimate the temperature-dependent moduli of neat, thermally reduced graphene and covalently functionalized graphene/epoxy nanocomposites. addition, governed mathematical expressions have been used solve buckling problem beams fabricated from such nanocomposites in presence thermal gradient. order do so, an energy-based method including shear deformable beam hypothesis used. The structure rested on Winkler–Pasternak substrate. reported verifications demonstrate impressive precision presented ML model, as well response under-study structures. Finally, framework some numerical case studies, impact several parameters nanocomposite depicted. results study delineate that temperature has vital role determination critical load structures can endure.