Prediction of the axial compression capacity of stub CFST columns using machine learning techniques

Concrete-filled steel tubular (CFST) columns have extensive applications in structural engineering due to their exceptional load-bearing capability and ductility. However, existing design code standards often yield different design capacities for the same column properties, introducing uncertainty for engineering designers. Moreover, conventional regression analysis fails to accurately predict the intricate relationship between column properties and compressive strength. To address these issues, this study proposes the use of two machine learning (ML) models – Gaussian process regression (GPR) and symbolic regression (SR). These models accept a variety of input variables, encompassing geometric and material properties of stub CFST columns, to estimate their strength. An experimental database of 1041 datasets was compiled from various research papers, including circular, rectangular, and double-skin stub CFST columns. In addition, a dimensionless output variable, referred to as the strength index, is introduced to enhance model performance. Through performance metrics, the GPR model emerges as the most accurate and reliable from the evaluation results. In addition, simple and practical design equations for the different types of CFST columns have been proposed based on the SR model. To validate the efficiency of the introduced models, predictions from these models are compared with those from two established standard codes and various ML studies. The developed ML models and proposed equations can predict the compressive strength of stub CFST columns with reliable and accurate results, making them valuable tools for structural engineering. Furthermore, the Shapley additive interpretation (SHAP) technique is employed for feature analysis. The results of the feature analysis reveal that column slenderness ratio and concrete strength parameters negatively impact the compressive strength index.