Accurate condition assessment of in-service infrastructure systems is critical for system-wide prioritization decisions. Current protocols require lengthy inspections and expensive equipment to examine large infrastructure systems. Furthermore, changes in inspection protocols over time can create discontinuities in recording and understanding the time history of a structure. To address these challenges, a systematic and adaptive technique for converting two-dimensional (2D) digital images into three-dimensional (3D) models has been developed, with the goal of creating high-resolution and scale-accurate inspection records. The developed reconstruction technique utilizes multiscale imaging to reconstruct a structure with varying levels of details and geometric complexity. The captured images are then converted into photorealistic, accurate, and dense 3D scene reconstructions by utilizing a hierarchical adaptation of a dense structure-from-motion (DSfM) algorithm. The result of this approach is a vir...

Accurate and rapid condition assessment of in-service structural components is critical to ensure safety and serviceability. One major assessment consideration is the detection and quantification of structural section loss due to deterioration, for instance, from corrosion. Modern three-dimensional (3D) imaging techniques, which generate high-resolution 3D point clouds, are capable of detecting and measuring these deteriorations. However, despite advancements in the fields of automated point cloud analysis for as-built modeling and structural inspection, the potential use of spatial 3D data for updating numerical finite-element (FE) models of structures is still an emergent topic. This paper presents a localized methodology for the automatic and systematic detection and quantification of damages in structural components using high-fidelity 3D point cloud data, followed by a corresponding local update to an FE model. In this study, 3D point cloud data of a targeted structure were first obtained by using dense structure from motion (DSfM) algorithms. Section loss damage was then identified and located through computer vision and 3D data processing techniques. In order to preserve data integrity and resolve localized high-fidelity details, direct 3D point cloud comparisons were performed. An experimental study validating the developed approach is presented as well. The results indicate that the presented methodology will enable engineers to use the updated structural model to determine the reserved capacity and remaining service life of structural elements, though further studies on methods to improve mesh generation and defect quantification are warranted.