Optimal design of cold-formed steel face-to-face built-up columns through deep belief network and genetic algorithm

Abstract

In this paper, a machine-learning optimisation framework for cold-formed steel (CFS) face-to-face built-up columns was proposed using Deep Belief Network (DBN) and Genetic Algorithm (GA). Firstly, an elasto-plastic non-linear finite element (FE) model was developed and validated against the available test results in the literature. Based on the validated FE model, a total of 1,100 datasets were generated, which were then used to train the DBN model. As the DBN model was proven accurate at predicting the axial strength of CFS face-to-face built-up columns, the trained DBN model was therefore used in the optimisation procedure. Through the optimisation procedure, considerable increases of up to 184.7% in axial strength were acquired for the optimised sections, when compared to the axial strength of initial CFS box section, which is commonly used in the industry. Furthermore, the optimised results obtained from the trained DBN model were compared with the results predicted by the Direct Strength Method (DSM). It was found that the DBN-GA optimised results acquired using FE datasets outperformed the results predicted by the DSM.