Prediction of the axial compressive strength of circular concrete-filled steel tube columns using sine cosine algorithm-support vector regression

In this study, support vector regression optimized by a sine cosine algorithm was used to predict the axial bearing capacities of circular concrete-infilled steel tubular columns. A database consisting of 478 test specimens was constructed to train and validate the proposed model, and the effects of the data composition and the sine cosine optimization were investigated. The accuracy of the proposed method was verified by comparing the proposed model's performance to those of other widely used machine learning methods and design formulae. The stability of the proposed method was verified by evaluating the average model performance of 100 random numerical experiments. For computer aided intelligent design, a backward prediction analysis using machine learning was performed, and the results indicate that the combination of support vector regression and the sine cosine algorithm can provide preliminary design assistance, whereas the empirical formulae cannot.