Efficiency of three advanced data-driven models for predicting axial compression capacity of CFDST columns. (July 2020) - Tran, Viet-Linh; Kim, Seung-Eock
Abstract: This study aims to investigate the performance of three advanced data-driven models, namely multivariate adaptive regression spline (MARS), artificial neural network (ANN), and adaptive neural fuzzy inference system (ANFIS), for predicting the axial compression capacity of circular concrete-filled double-skin steel tube (CFDST) columns. For this purpose, 125 experimental data sets collected from the literature were used to develop the MARS, ANN, and ANFIS models. In this regard, the column length, the outer diameter, the outer thickness and yield strength of the outer steel tube, the inner diameter, the inner thickness and yield strength of the inner steel tube, and the compressive strength of concrete were considered as input variables, meanwhile, axial compression capacity was considered as the output variable. The performance of the three data-driven models was compared with six equations proposed by design codes and other authors. The comparisons showed that three data-driven models achieved more accuracy than previous equations, of which, the ANN model has an advantage over the ANFIS and MARS models. Finally, a graphical user-friendly interface (GUI) was developed to make the MARS, ANN, and ANFIS models become more attractive for practical use. Highlights: Three data-driven models, namely MARS, ANN, and ANFIS effectively predict the axial compression capacity of CFDST columns. The ANN model outperforms the ANFIS and MARS models. The equations obtained from three data-driven models perform better than six previous equations in the literature.