Prediction of the Axial Bearing Compressive Capacities of CFST Columns Based on Machine Learning Methods

Abstract

Concrete-filled steel tubes (CFSTs) are widely used in engineering structures due to their excellent mechanical properties and economic benefits. This study focused on the construction of artificial neural network (ANN) models with high prediction capabilities and prediction accuracies that could predict the axial compression load capacities of short CFST columns using machine learning methods. A database was created by searching literature published over the past 40 years regarding circular-CFST bearing-capacity testing. Three ANN models with different input parameters were developed, and used the Whale Optimization Algorithm to optimize the network weights and thresholds, the core idea of which comes from the humpback whale's special bubble net attack method. Then, the predictions of the proposed machine learning models were also compared with the theoretical values produced by the formulas proposed in existing codes. The results show that the ANN models had higher accuracies and a wider application range than the existing code models. Based on the Garson's algorithm, we perform parameter sensitivity analysis on the network model to enhance the interpretability of the neural network model. Finally, a graphical user tool is built to make the strength of CFST can be predicted quickly.