Multi-Scale Applications of Point Cloud Technology: Historic Structure Documentation, Geometric Imperfection Analysis and Buckling Detection, and Direct Integration into Finite Element Models

Abstract

This thesis explores the application of point cloud technology for structural documentation, visualization, and analysis across a range of scales—from large heritage sites and infras- tructure to component-scale structural components. The research investigates the use of multiple scanning tools, including the Artec Leo and iPad Pro (Scaniverse), and evaluates their effectiveness in capturing geometric data for engineering and preservation purposes. Two distinct scanning scenerios were examined: the Au Sable Light Station, a historic lighthouse located along Lake Superior, and the Newville Bridge, an active transportation structure in Wisconsin. These case studies demonstrate the use of high-resolution LiDAR and photogrammetry to generate detailed three-dimensional models suitable for archival, virtual reality, and structural analysis applications. In addition, high-precision scanning of thin-walled steel decks in a laboratory setting was conducted to evaluate the detection of geometric imperfections and assess the fidelity of data used for finite element modeling. The results show that high-resolution scanners such as the Artec Leo produce highly accurate point clouds that closely align with manual measurements, especially in capturing critical features like flange angles and bend radius. Photogrammetry methods using the iPad Pro offered greater accessibility and faster workflows, but with reduced accuracy, particularly in capturing complex geometries and reflective surfaces. Limitations encountered during the scanning process included environmental lighting variability, surface reflectivity, and obstructions from moving elements in active sites. The study concludes that point cloud technology offers a reliable, non-invasive, and scal- able approach for structural documentation and geometric data acquisition. Furthermore, by integrating processed point clouds into simulation workflows, the research demonstrates a path toward more accurate and realistic structural modeling. The point cloud data were post-processed into mesh formats and converted to geometry suitable for import into finite element analysis. This allowed real-world imperfections to be reflected in simulation models, enhancing the predictive capability of structural assessments. This work establishes a foun- dational framework for applying 3D scanning in both civil engineering and cultural heritage contexts, and highlights its growing potential in digital preservation, condition assessment, and performance-based design.