Axial load and strain prediction of square FRP–concrete–steel double-skin tubular columns using machine learning and nonlinear finite element analysis
This study investigates the axial behavior of square fibre reinforced polymer–concrete–steel double-skin tubular columns (hybrid DSTCs) by integrating experimental data, nonlinear finite element analysis (NLFEA), and machine learning (ML). A hybrid dataset comprising 174 specimens was developed, including 24 experimental results from the literature and 150 additional specimens generated through validated NLFEA models. The models demonstrated strong agreement with experimental results, achieving R² values of 0.98 for axial load and 0.92 for axial strain. The expanded dataset facilitated parametric studies on cross-sectional dimension, outer FRP tube thickness, inner steel tube thickness, compressive strength of concrete, and FRP modulus of elasticity. Four ML models, including Multi-linear Regression (MLR), Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed to predict axial load (Pu) and axial strain (εcu). Model performance was evaluated using R², RMSE, MAE, and MAPE. The DT model achieved the highest accuracy for axial load (Pu) prediction (R² = 0.97, MAPE = 2.97 %), while XGBoost provided the best overall performance for both axial load (R² = 0.97, MAPE = 3.60 %) and axial strain (R² = 0.83, MAPE = 9.63 %). Feature importance analysis revealed that axial load (Pu) is primarily governed by FRP stiffness, steel yield strength, and concrete strength, whereas ultimate axial strain (εcu) is dominated by steel tube geometry and strength, confirming that axial load and strain are controlled by distinct governing mechanisms. The findings highlight the potential of FEM–ML integration to develop robust predictive tools for square hybrid DSTCs.