Machine Learning-Based Prediction of Axial Load Bearing Capacity for CFRST Columns

Abstract

As a primary load-bearing component, accurately predicting the bearing capacity of concrete-filled rectangular steel tube (CFRST) members is an essential prerequisite for ensuring structural safety. Machine learning methods are employed to model and predict the axial load bearing capacity of CFRST columns. A test database containing 1119 members is established, and the input parameters of the machine learning model are determined using a combination of data preprocessing and correlation analysis. Four machine learning algorithms, namely Lasso, ANN, RF, and XGBoost, are selected to build the prediction models for axial load bearing capacity, and a comparative analysis of their predictive performance is conducted. The feature importance analysis is performed using the SHAP method. The results indicate that the model based on the XGBoost algorithm achieves the highest prediction accuracy. Through comparison with six existing calculation methods in domestic and international codes, the reliability of its predictive performance is verified.