An efficient stress recovery technique in adaptive finite element method using artificial neural network

Abstract

In this paper, an efficient stress recovery technique is presented to estimate the recovered stress field at the nodal points. The feed–forward back–propagation multilayer perceptron (MLP) neural network approach is employed to improve the accuracy of the stress recovery method. An automatic adaptive mesh refinement is performed based on a–posteriori Zienkiewicz–Zhu error estimation method. The proposed technique is employed to recover the stress field accurately in the regions with a high stress gradient where the conventional recovery techniques are not able to improve the stress fields efficiently due to the singular behavior of problem. Finally, several numerical examples are solved to demonstrate the efficiency and accuracy of the proposed computational algorithm. The results are compared with the conventional methods, including the averaging method, superconvergent patch recovery (SPR) technique, and weighted superconvergent patch recovery (WSPR) method that illustrates how the artificial neural network can be used accurately to recover the stress field.