Accurate geometric imperfection detection and quantification of cold-formed steel members from point clouds

In recent years, the use of cold-formed steel (CFS) in low and medium-rise buildings has become widespread. CFS members have a high strength-to-weight ratio, and since the construction of the structures performed using these elements takes a short time, it offers an effective solution in terms of meeting the requirements of rapid construction. CFS construction has advantages as well as disadvantages, and one of these disadvantages is that the geometric imperfections that occur in the member during the manufacturing, transportation, and installation processes affect the element's behavior. This research focuses on accurately detecting and quantifying the geometric imperfections found in C-sectioned CFS members. Figure A presents the geometric imperfection detection results for the twist, delta theta, obtained using both old and improved new methods on a 1000 mm long C-sectioned CFS member, Pmid6. Figure A. Geometric imperfection detection results for the twist, delta theta, obtained using both old and improved new methods for the cross-section cuts along the member axisPurpose: This research aims to accurately detect and quantify the geometric imperfections found in C-sectioned CFS members by using the improved geometric imperfection detection and quantification method.Theory and Methods: Local and global imperfections in CFS members are determined using the improved automatic geometric imperfection detection and quantification method. The improved geometric imperfection detection and quantification method performs an advanced reference geometric model registration that improves the global imperfection detection results. Furthermore, the local imperfections are investigated by projecting the reference key-points detected on the point cloud section cuts on the ideal geometric model representation. The results obtained were compared with a previously developed, literature-based geometric imperfection detection and quantification method.Results: The obtained results showed that the maximum and average geometric imperfection values calculated by the improved geometric imperfection detection and quantification method for all elements decreased by 50% or more, except for the geometric imperfections whose formulation remain same, and which are not directly affected by the initial ideal geometric model placement process.Conclusion: The developed improved geometric imperfection detection and quantification method is capable of both the precise registration of the ideal geometric model used as a reference and the accurate detection and quantification of both local and global geometric imperfections. Obtained results have showed that the improved method can capture the actual distributions of both local and global geometric imperfections.