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

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.