Strength prediction of concrete-filled steel tubular columns using Categorical Gradient Boosting algorithm

Abstract

Due to complexities from the interaction between steel tube and concrete filling of concrete-filled steel tubular (CFST) columns, their strengths are very complicated, which is a highly nonlinear relation with material strengths and geometry. Categorical gradient Boosting (CatBoost), which is advanced boosting machine, is presented to solve the problems. A total of 3103 tests, which is divided in four datasets, is trained and tested the learners to determine the ultimate axial strength as the output variable while the strength of materials (concrete and steel) and geometry (e.g., diameters/width/heights, thickness, effective length, eccentricities) are the input ones. The comparison of the p..