Data‐driven moment‐rotation formulation of slender cold‐formed steel beams subjected to local buckling

Accurately predicting the structural response of Cold-Formed Steel (CFS) members under extreme load events is essential for reliable seismic performance assessment and design of multi-storey CFS structures. However, this remains challenging due to the complex behaviour of thin-walled CFS elements influenced by interacting buckling modes. Current seismic performance-based design guidelines, such as ASCE 41-23, primarily rely on local slenderness and do not account for other influential parameters. To address this limitation, the present study proposes a novel data-driven formulation to predict the moment–rotation behaviour of CFS beams. A comprehensive series of finite element (FE) models was developed and validated against experimental data to generate a robust dataset representing the moment–rotation response of CFS beams governed by local buckling. Following a comparative analysis with the ASCE 41-23 model, Multi-Gene Genetic Programming (MGGP) was employed as a machine learning (ML) technique to predict the moment–rotation response of CFS beams. The input parameters include key geometric and material properties, such as local slenderness, web-to-thickness ratio, flange-to-thickness ratio, and yield and ultimate steel strengths for C- and Z-shaped cross-sections. The results demonstrate that the proposed MGGP model achieves significantly higher predictive accuracy than ASCE 41-23, reducing average errors by 70% for peak moment, 78% for rotation at peak moment, and 87% for ultimate rotation. Finally, a web-based application was developed to enable rapid ML-based prediction of the output parameters.