Ultimate axial strength prediction of concrete-filled double-skin steel tube columns using soft computing methods

Abstract

In this study, two hybrid models, grey wolf optimizer (GWO) combined with group method of data handling (GMDH) and random forest (RF) optimized by particle swarm optimization (PSO), are proposed to predict the ultimate axial strength of concrete-filled double-skin steel tube (CFDST) columns. 139 sets of data collected from the literature were used to build the proposed models. The proposed hybrid models were compared with other machine learning models as well as empirical equations and design specifications. The results show that for all datasets, the integral absolute error (IAE), root relative squared error (RRSE), mean absolute percentage error (MAPE) and coefficient of determination (R2) predicted by PSO-RF and GWO-GMDH models are 0.057, 0.092, 0.068 and 0.992; 0.068, 0.094, 0.110 and 0.989, respectively. Compared with the single RF and GDMH models, the IAE, RRSE and MAPE values of the proposed PSO-RF and GWO-GMDH models decreased by 3.91%, 13.66% and 6.34%; 37.81%, 48.78% and 39.24%, respectively, and the R2 value increased by 1.24% and 1.67%, respectively. In addition, the factors affecting the failure of CFDST columns are analyzed through the parameter sensitivity analysis.