Data-driven axial bearing capacity analysis of steel tubes infilled with rubberized alkali-activated concrete

This study aims to employ machine learning algorithms to analyze the axial bearing capacity of rubberized alkali-activated concrete filled steel tubes. A dataset encompassing 327 synthesized instances and seven input features is adopted for training and testing six machine learning models, including Decision Tree, Random Forest, Extremely Randomized Trees, Adaptive Boosting, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting Trees (XGBoost). The SHapley Additive exPlanation algorithm is employed to elucidate the prediction process of machine learning models and to analyze the influence of each parameter on axial bearing capacity. Comparison of evaluating metrics shows that GBDT and XGBoost models achieve highest accuracy and generalization capabilities when their Coefficient of Determination values surpassing 0.98 and Mean Absolute Percent Error remaining below 3%. Moreover, the explanation analysis of machine learning models reveals that diameter/width of the cross section, rubber content, yielding strength and thickness of steel tubes are critical variables that affect the axial bearing capacity, while compressive strength of alkali-activated concrete, specimen height, and shape of cross section show negligible impact. Besides, GBDT model overemphasizes the effect of specimen height and might lead a conservative prediction for specimens with smaller heights. Finally, compressive strength of alkali-activated concrete and diameter/width, thickness, and yielding strength of steel tubes are positively correlated with axial bearing capacity, and the increase of rubber content in alkali-activated concrete leads to the decrease of capacity.