Stochastic static analysis of functionally graded sandwich nanoplates based on a novel stochastic meshfree computational framework

In this study, the spatial variability of materials is incorporated into the static analysis of functionally graded sandwich nanoplates to achieve higher accuracy. Utilising a modified point estimation method and the radial point interpolation method, we develop a novel stochastic meshfree computational framework to deal with the material uncertainty. Higher-order shear deformation theory is employed to establish the displacement field of the plates. The elastic modulus of ceramic and metal (E_c and E_m) are treated as separate random fields and discretized through the Karhunen-Loève expansion (KLE) method. To improve the performance of procedure, the Wavelet-Galerkin method is introduced to solve the second type of Fredholm integral equation. Subsequently, substituting the random variables obtained by KLE into the stochastic computational framework, a high accuracy stochastic response of structures can be acquired. By comparing computed findings with those of Monte Carlo simulation, the accuracy and efficiency of developed framework are verified. Moreover, the results indicate that the plate's deflection exhibits varying sensitivities to the random fields E_c and E_m. Also, the sandwich configuration as well as power-law exponents affect the stochastic response of structures. These findings offer valuable insights for the optimized design of functionally graded sandwich nanoplates.