Modeling the nonlinear behavior of ACC for SCFST columns using experimental-data and a novel evolutionary-algorithm
Axial compression capacity (ACC) is an important parameter for the concrete-filled steel tubular columns to measure the resistance of these fundamental elements, which used in the construction of several structures types. For this purpose, the Gene Expression Programing (GEP) is proposed in this paper as a new framework for the development of novel models with closed-form equations to describe the behavior of the axial compression capacity (ACC) for Square Concrete-Filled Steel Tubular (SCFST) columns. For an accurate ACC modeling, six novel predictive formulas based on the GEP-approach were proposed by incorporating different combinations of the input variables. These latter were obtained from a large dataset that includes 300 experimental tests with different ranges and varieties. Besides, the most known codes and empirical correlations for modeling the behavior of ACC for SCFST columns were reviewed, whereas the performance, accuracy, and efficiency of the proposed models and the excited codes and correlations were investigated and compared using several statistical criteria and graphical illustration. Results show that the best explicit closed-form correlation extracted based on the GEP-approach exhibit an overall coefficient of determination (R2) value of 0.9943. Furthermore, the outcome results indicate that the efficiency of the proposed GEP-based formulations outperform the excited codes and correlations, which proves that the GEP is a powerful technique to derive a new model for modeling the complex behavior of the ACC for SCFST columns.