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.

This study focuses on optimizing the hyperparameters of the XGB model by a grid search to find an optimal XGB predictive model. In addition, the effect of input parameters on the SCCRF's CS is studied using Shapley Additive exExplanations (SHAP) values technique.

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures.