To consider the highly nonlinear and complex buckling behaviour of various sections of cold-formed steel (CFS) built-up columns, experimental and finite element (FE) methods are commonly used for calculating their axial compressive capacity, although these methods are time-consuming and costly. This paper proposes machine learning (ML) methods to overcome the issues of traditional methods for predicting the maximum axial load capacity (MALC) of CFS built-up columns. A total of 3839 samples from more than 33 different types of sections were collected from 43 published papers, including 817 expe...
