Jing-Pu Tang

Strength prediction of circular and square CFDST columns using generalised machine learning models

Three ML algorithms, including Categorical Boosting (CatBoost), Multilayer Perceptron (MLP) and Kolmogorov‐Arnold Networks (KAN), were tested and the results show that the KAN model provides the most accurate predictions of CFDST strengths, with an R2 of 0.9903, compared with 0.9084 for Eurocode 4.