Deep learning-based procedure for structural design of cold-formed steel channel sections with edge-stiffened and un-stiffened holes under axial compression

This paper proposes a framework of deep belief network (DBN) for studying the structural performance of cold-formed steel (CFS) channel sections with edge-stiffened/un-stiffened web holes, under axial compression. A total of 50,000 data points for training the DBN are generated from elasto plastic finite element analysis, which incorporates both initial geometric imperfections and residual stresses. A comparison against 23 experimental results...