Condition Assessment of Bridges Using Terrestrial Laser Scanners

Assessing the current condition of structures and infrastructure systems has long been critical to ensure their effectiveness and remaining life. Even though nondestructive evaluation technologies have improved significantly, visual inspection is still a main tool used to assess the condition of structures, especially bridges. This research investigates the use of laser scanners coupled with images as a tool for improving the current visual inspection strategies. Laser scanning capabilities have advanced in recent years and have gained more recognition as a tool for applications in numerous fields. It is now possible to collect millions of texture-mapped data points that are accurate to within millimeters. In this work, captured texture-mapped datasets are processed using several damage detection strategies, which integrate existing condition rating criteria for a wide range of damage types, in order to locate, quantify and document the surface damage. These damage detection strategies include methods developed for detecting both element and surface damage that are present on the investigated structures. In order to show that defect localization, quantification, and documentation are performed successfully, the proposed methods are used to process the texture-mapped 3D point cloud collected from test-bed bridges. The investigated test-bed bridges include a range of element and surface damage that includes cracks, spalled concrete regions, steel section loss, delamination, and corrosion. The obtained results show that texturemapped 3D point clouds may be used effectively to detect and document quantitative information on present conditions of bridges.