Data-driven modeling for thermo-elastic properties of vacancy-defective graphene reinforced nanocomposites with its application to functionally graded beams

The presence of unavoidable defects in the form of atom vacancies in graphene sheets considerably deteriorates the thermo-elastic properties of graphene-reinforced nanocomposites. Since none of the existing micromechanics models is capable of capturing the effect of vacancy defect, accurate prediction of the mechanical properties of these nanocomposites poses a great challenge. Based on molecular dynamics (MD) databases and genetic programming (GP) algorithm, this paper addresses this key issue by developing a data-driven modeling approach which is then used to modify the existing Halpin-Tsai model and rule of mixtures by taking vacancy defects into account. The data-driven micromechanics models can provide accurate and efficient predictions of thermo-elastic properties of defective graphene-reinforced Cu nanocomposites at various temperatures with high coefficients of determination (R-2 > 0.9). Furthermore, these well-trained data-driven micromechanics models are employed in the thermal buckling, elastic buckling, free vibration, and static bending analyses of functionally graded defective graphene reinforced composite beams, followed by a detailed parametric study with a particular focus on the effects of defect percentage, content, and distribution pattern of graphene as well as temperature on the structural behaviors.