Automated Geometric Imperfection Detection and Quantification of CFS Members from Point Clouds

Abstract

Cold-formed steel (CFS) has become widely used as a construction material in the last decade. Many studies have been performed to investigate the behaviour of CFS members as individual structural components. The results obtained from these studies showed that CFS members' behaviour under certain loading cases is significantly affected by the geometric imperfections present on these members. To fully understand an individual CFS member's behaviour, the effects of geometric imperfection should be taken into account. However, locating and quantifying these geometric imperfections on any CFS member is not straightforward. In this research, geometric imperfections are- detected automatically using a novel methodology. This method relies on the usage of a three-dimensional optical scanner for collecting texture-mapped point clouds of various C- and omega-sectioned CFS members. CFS members' local and global geometric imperfections are then extracted from the captured texture-mapped point clouds. The obtained results are then compared with the literature. It is observed that the geometric imperfection distributions at both local and global levels on CFS members differ significantly along each member. This differentiation is observed even for the identically dimensioned members manufactured in succession using the same machine.