Bearing Capacity Prediction of Cold-Formed Steel Columns with Gene Expression Programming

In recent years, there has been a growing use of cold-formed steel (CFS) structures in the field of civil engineering. The objective of this study is to utilize gene expression programming (GEP) in order to forecast the ultimate bearing capacity of cold-formed steel columns. The buckling resistance of built-up back-to-back cold-formed (BCF) thin-walled tube columns under axial compression, and of cold-formed thick-walled steel columns under combined axial compression and bending, is examined in this paper. The data were collected from various studies to develop and verify the proposed model, with training and testing sets of 160 and 14, and 2000 and 500, respectively. The performance of the genetically developed GEP models was evaluated and compared with that of the mechanical models specified in American and Chinese specifications. The GEP models demonstrated significantly better performance compared with that of the code-specified models. The results generated by the GEP models demonstrate stronger alignment with both experimental data and analytical predictions. This study also demonstrates the capability of the GEP models to calculate the ultimate bearing capacity, with the proposed mechanical models being used as a reference for calculations.