GS-MARS method for predicting the ultimate load-carrying capacity of rectangular CFST columns under eccentric loading

This study presents applications of the multivariate adaptive regression splines (MARS) method for predicting the ultimate loading carrying capacity (Nu) of rectangular concrete-filled steel tubular (CFST) columns subjected to eccentric loading. A database containing 141 experimental data was collected from available literature to develop the MARS model with a total of seven variables that covered various geometrical and material properties including the width of rectangular steel tube (B), the depth of rectangular steel tube (H), the wall thickness of steel tube (t), the length of column (L), cylinder compressive strength of concrete (f\'c), yield strength of steel (fy), and the load eccentricity (e). The proposed model is a combination of the MARS algorithm and the grid search cross-validation technique (abbreviated here as GS-MARS) in order to determine MARS\' parameters. A new explicit formulation was derived from MARS for the mentioned input variables. The GS-MARS estimation accuracy was compared with four available mathematical methods presented in the current design codes, including AISC, ACI-318, AS, and Eurocode 4. The results in terms of criteria indices indicated that the MARS model was much better than the available formulae.