Machine learning-driven prediction of axial capacity of concrete-filled steel tubes columns with built-in bamboo or timber cores

Studies on concrete-filled steel tube columns with built-in bamboo or timber cores (CFST-BTC) not only optimize the mechanical properties of conventional concrete-filled steel tube (CFST) columns but also reduce concrete usage and carbon emissions, enhancing structural sustainability. This study establishes a CFST-BTC database of 271 specimens under axial compression and evaluates various machine learning (ML) models for predicting their ultimate bearing capacity (N_cu). Six mainstream ML algorithms such as ANN, LightGBM, Gradient Boosting Regressor, CatBoost, XGBoost and AdaBoost were assessed for training and predicting N_cu of CFST-BTC. Gradient Boosting Regressor and ANN effectively predicted the experimental results, achieving R² values of 0.9988/0.9985 for the training set and 0.9964/0.9959 for the test set, significantly outperforming traditional analytical models. Based on SHAP analysis, key parameters were identified, nonlinear relationships were revealed, and the user-friendly analytical equation was formulated based on ANN. The proposed equation demonstrated strong predictive performance, achieving an R² value of 0.9609 and an RMSE of 156.35. Although these results are slightly inferior to those of the six ML surrogate models, they surpass the explicit equation constructed using multiplication (R² = 0.9297) and other traditional analytical models. The user-friendly equation suggests that the optimal substitution ratio (w) of bamboo or timber core replacing the core concrete in CFST-BTC is 36.5 %, providing guidance for the practical application of CFST-BTC columns in engineering. Meanwhile, the suggested explicit equation effectively satisfies the boundary conditions at w = 0 and w = 1, ensuring reliable predictions for ultimate bearing capacity of both CFST columns and bamboo or timber-filled steel tube (BTFST) columns without computational inconsistencies at the boundary conditions.