Prediction model of axial compressive bearing capacity of concrete-encased steel composite columns based on SHAP and machine learning

The ongoing construction of large-span buildings and high-rise buildings in recent years has raised the performance requirements for structural elements. Concrete-encased steel (CES) composite structure has been widely used in modern building structures due to its excellent seismic performance, stable mechanical properties and good fire resistance. However, under the axial load, the interaction of various materials in the CES columns is highly nonlinear, and the prediction accuracy of the traditional calculation formula is limited. Therefore, this paper establishes a database standard and collects 116 sets of effective test data from early research at home and abroad. The axial compression bearing capacity of CES columns is predicted using a model constructed with three artificial intelligence algorithms: ANN, RF and XGBoost. To enhance the model's interpretability, the SHAP method is employed. SHAP employs global explanatory analysis, individual sample interpretation, and feature influence analysis to elucidate the impact mechanisms of various characteristic factors on the ultimate bearing capacity of composite columns under axial pressure. This clarification aids in comprehending the predictive mechanisms for the bearing capacity of steel and concrete composite columns as well as the significance of influential parameters, offering valuable guidance for the design and application of such composite columns.