Development of effective XGB model to predict the Axial Load Capacity of circular CFST columns

Abstract

The Axial Load Capacity (ALC) of Concrete-Filled Steel Tubular (CFST) structural members is regarded as one of the most crucial technical factors for the design of these composite structures. This work proposes the development and application of the Extreme Gradient Boosting (XGB) model to forecast the ALC of circular CFST structural components using the affecting input parameters, namely column diameter, steel tube thickness, column length, steel yield strength, and concrete compressive strength. A dataset of 2073 experimental results from the literature was used for the model development. The performance of the XGB model was evaluated using statistical criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Determination (R2), and Mean Absolute Percentage Error (MAPE). The five-fold cross-validation technique and Monte Carlo simulation method were used to evaluate the model's performance. The results show good performance of the XGB model (R2 = 0.999, RMSE = 242.757 kN, MAE = 157.045 kN, and MAPE = 0.057) in predicting the circular CFST’s ALC.