查看文章

Concrete-filled steel tube (CFST) columns are widely used in the construction industry. Prediction of the ultimate bearing capacity of CFST columns is complicated because it is influenced nonlinearly by many factors such as steel tube length, steel tube thickness, ratio length and column diameter, and concrete compressive strength. This study proposes an artificial intelligence (AI) model to predict the ultimate bearing capacity of CFST columns. The AI model was developed based on support vector regression (SVR) and grey wolf optimization (GWO). The GWO optimized the SVR configuration that produces highly accurate prediction results. A large experimental dataset with normal, high, and ultimate strength concretes was used to validate the model’s effectiveness through the learning and test phases. A k-fold cross-validation method was adopted to ensure the generalizability. The column diameter (D …