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.

Light Gradient Boosting Machine is a new machine learning technique developed by Microsoft corporation which has been proposed in the present study to determine the CBR of stabilized expansive soils. Model performance of the ML model are evaluated by different criteria such as correlation coefficient R, root mean square error RMSE and mean absolute error MAE.