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Buckling Mode Identification for a Cold-Formed Steel Column Experiment with 3d Image-Based Reconstr

Abraham Lama-Salomon , Fannie Tao , Junle Cai and Cristopher D. Moen Virginia Polytechnic Institute and State University (Virginia Tech) Blacksburg, VA


ntly developed thin-walled modal decomposition algorithms are merged with 3d image based reconstruction (Figure 1) to document and quantify buckling deformation throughout a cold-formed steel column experiment. The buckling deformation is recorded with strategically located high-definition video cameras. The video footage is decomposed into individual frames, and a gradient-based optimization algorithm, available in low cost commercial software packages, is applied that finds the 3d image coordinates and camera position by maximizing the number of matching (overlapping) features from frame to frame. Once the 3d coordinate system and camera locations are established, a dense point cloud is generated resulting in the 3d column representation throughout the experiment. The 3d point cloud is analyzed with a buckling mode identification tool that employs cross-sectional deformation modes from generalized beam theory. Local, distortional, and global buckling participation are documented, including contributions just prior to col-umn failure which can be useful for the development of future strength prediction design approaches, especially where buckling modes mix near an ultimate limit state.

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