A machine learning based interaction model to predict robustness of concrete-filled double skin steel tubular columns under fire condition

Abstract

Concrete-filled double skin steel tubular (CFDST) column is a hollow composite structure component, which shows better performance than traditional reinforced concrete and steel columns due to the favorable composite action between steel and concrete. In the current study, a machine learning based interaction model combine with the extended Rankine method is developed to predict fire resistance of eccentrically loaded CFDST cylinder columns. The prediction of the reliable shear bond parameter was conducted by back propagation artificial neural network (BP-ANN) and Extreme Gradient Boosting Tree (XGBoost). To perform a reliable production, the architecture and the parametric setting of both models were constructed. Furthermore, the results of the prediction were verified by experimental results and finite element analysis. The results show that the proposed method can predict the behavior of the eccentrically load CFDST columns under fire attack with reasonable accuracy.