This research investigates the use of high‐resolution three‐dimensional terrestrial laser scanners as tools to capture geometric range data of complex scenes for structural engineering applications. Laser scanning technology is continuously improving, with commonly available scanners now able to capture over 1,000,000 points per second with an accuracy of ∼0.1 mm. This research focuses on developing the foundation toward the use of laser scanning to structural engineering applications, including structural health monitoring, collapse assessment, and post‐hazard response assessment. One of the keys to this work is to establish a process for extracting important information from raw laser‐scanned data sets such as the location, orientation, and size of objects in a scene, and location of damaged regions on a structure. A methodology for processing range data to identify objects in the scene is presented. Previous work in this area has created an initial foundation of basic data processing steps. Existing algorithms, including sharp feature detection and segmentation are implemented and extended in this work. Additional steps to remove extraneous and outlying points are added. Object detection based on a predefined library is developed allowing generic description of objects. The algorithms are demonstrated on synthetic scenes as well as validated on range data collected from an experimental test specimen and a collapsed bridge. The accuracy of the object detection is presented, demonstrating the applicability of the methodology. These additional steps and modifications to existing algorithms are presented to advance the performance of data processing on laser scan range data sets for future application in structural engineering applications such as robust determination of damage location and finite element modeling.

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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. Structures Congress 2015 355