Analysis of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using artificial neural networks

Abstract

We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.

Long short-term memory for nonlinear static analysis of functionally graded plates

“…LSTM will be presented in the subsequent section. For the sake of brevity, readers can refer to the detailed solving process of the supervised learning problem in some studies such as [31][32][33].…”