Journal article

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

Seunghye Lee, Thuc P Vo, Huu-Tai Thai, Jaehong Lee, Vipulkumar Patel



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..

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University of Melbourne Researchers


Awarded by NRF (National Research Foundation of Korea) - MEST (Ministry of Education and Science Technology) of Korean government

Funding Acknowledgements

This research was supported by a grant (NRF-2020R1A4A2002855) from NRF (National Research Foundation of Korea) funded by MEST (Ministry of Education and Science Technology) of Korean government.