Prediction of Axial Capacity of Concrete Filled Steel Tubes Using Gene Expression Programming

The safety and economy of an infrastructure project depends on the material and design equations used to simulate the performance of a particular member. A variety of materials can be used in conjunction to achieve a composite action, such as a hollow steel section filled with concrete, which can be successfully utilized in the form of an axially loaded member. This study aims to model the ultimate compressive strength (P_u) of concrete-filled hollow steel sections (CFSS) by formulating a mathematical expression using gene expression programming (GEP). A total of 149 datapoints were obtained from the literature, considering ten input parameters, including the outer diameter of steel tube (D), wall thickness of steel tube, compressive strength of concrete (f_c'), elastic modulus of concrete (E_c), yield strength of steel (f_v), elastic modulus of steel (E_s), length of the column (L), confinement factor (ζ), ratio of D to thickness of column, and the ratio of length to D of column. The performance of the developed models was assessed using coefficient of regression R, root mean squared error RMSE, mean absolute error MAE and comparison of regression slopes. It was found that the optimal GEP Model T3, having number of chromosomes N_c = 100, head size H_s = 8 and number of genes N_g = 3, outperformed all the other models. For this particular model, R²_overall equaled 0.99, RMSE values were 133.4 and 162.2, and MAE = 92.4 and 108.7, for training (TR) and testing (TS) phases, respectively. Similarly, the comparison of regression slopes analysis revealed that the Model T3 exhibited the highest R of 0.99 with m = 1, in both the TR and TS stages, respectively. Finally, parametric analysis showed that the P_u of composite steel columns increased linearly with the value of D, t and f_y.