Ensemble learning methods for the mechanical behavior prediction of tri-directional functionally graded plates

Abstract

This paper aims to enhance computational performance for behavior prediction of tri-directional functionally
graded plates using ensemble learning methods such as random forest, extreme gradient boosting, and light
gradient boosting machine. Furthermore, the effectiveness of these methods is verified by comparing their
results with those of artificial neural networks. The present investigation focuses on the buckling problem of tridirectional functionally graded plates. In this study, data pairs consisting of input and output data are generated using a combination of isogeometric analysis and generalized shear deformation theory to ensure the accuracy of the dataset. The input data in this case are eighteen control points used to characterize material distribution; the output data are total ceramic volume fraction and non-dimensional buckling load. Based on this dataset, the effect of hyperparameters in machine learning models on accuracy and computational cost is investigated to determine models with optimal hyperparameters, referred to as optimal models. The performance of the optimal models in predicting plate behavior is compared to each other. Furthermore, in terms of computational time and accuracy, the light gradient boosting machine model gives the best results compared to the others.