Advanced Finite Element Modeling of Cold‐Formed Steel Profiles in Bending: A Detailed Study on Flexural Strength and Structural Performance

ABSTRACT Lattice beams with cold‐formed steel include buckling under heavy loads and suboptimal structural performance. The issues include potential buckling under heavy loads, difficulties in achieving optimal structural performance, and challenges in ensuring compliance with safety standards. In this manuscript, a novel reinforcement approach combining finite element (FE) analysis with an explicit feature interaction aware graph neural network (EFIAGNN) and a hiking optimization algorithm (HOA), termed EFIAGNN‐HOA, is proposed. The proposed EFIAGNN technique is used for the normalized ultimate load forecast, and HOA is used to find the best objective function. Using the MATLAB platform, EFIAGNN‐HOA is evaluated against existing techniques like the Direct Strength Method (DSM), AS/NZS specifications, the method of moving asymptotes (MMA), gradient‐based algorithms (GBA), and particle swarm optimization (PSO). The proposed model achieves superior accuracy 99.01%, computational efficiency 98.99%, and reduced errors MAE: 0.02%, RMSE: 0.01%. It also significantly lowers the computational costs proposed: 850% vs. 1100% to 1500% for existing methods while improving convergence and prediction reliability. Additionally, EFIAGNN‐HOA provides more precise deflection and moment capacity estimations, with closer agreement to experimental results than DSM and AS/NZS standards. These improvements translate into cost savings, optimized material use, and enhanced structural efficiency, making EFIAGNN‐HOA a highly effective solution for CFS lattice beam design.