Prediction of ultimate bearing capacity of concrete filled steel tube stub columns via machine learning

In this study, three artificial intelligence models, namely group method of data handling, gene expression programming and random forest, are proposed to predict the ultimate bearing capacity of concrete filled steel tube stub columns. A total of 220 data samples collected from the literature was used to construct the three models. Five statistical indices were used to evaluate the performance of the three models and the other five existing design codes and two reference models. Compared with the optimal model among the seven existing models, the coefficient of variation, mean absolute percentage error, root relative squared error and integral of absolute error values of all datasets of the three models (i.e., group method of data handling, gene expression programming and random forest) were decreased by 46.47%, 56.49%, 71.35% and 65.78%; 49.82%, 59.49%, 66.88% and 64.51%; 79.27%, 84.83%, 78.64% and 83.71%, respectively; while the determination coefficient values of all datasets of the three models were increased by 7.36%, 6.79% and 8.22%, respectively. The results show that the predicted results of the three models agree well with the experimental results, and the random forest model has the best comprehensive performance.