A study on static bending behavior of partially elastically supported functionally graded plate with porous voids and prediction of deformation through deep learning

Abstract

This research explores the static bending behaviour of functionally graded rectangular plates with porous voids. It addresses maximum deformation and static bending factors under uniform-pressure, considering variables such as porous-void distributions, full and partial elastic foundations, and edge constraints. The Rayleigh-Ritz method combined with algebraic polynomials is employed to obtain the numerical solutions. The convergence test shows computing efficiency, while the validation tests against public data and ANSYS findings verify the accuracy of the present numerical model. Additionally, this research presents a deep learning-based Artificial-Neural-Network model for deformation prediction to enhance the depth of the analysis without extensive numerical simulations.

Keywords:

• EFGRP
• porosity
• static bending
• partial foundations
• artificial neural network

Disclosure statement

No potential conflict of interest was reported by the author(s)