Light Detection and Ranging (LiDAR), is a relatively new class of survey instrument has become popular and is increasingly used in providing as-built and inventory data in civil applications. Private and governmental agencies possess LiDAR systems, but understanding of the technology’s capabilities is not yet mature. Routine bridge inspections require labor intensive and highly subjective visual interpretation for determination of surface condition. By re-purposing the data collection process of LiDAR, critical information pertaining to surface condition can be collected, such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns. Those data provide more quantitative surface condition information than visual interpretation, resulting in more accurate structural health monitoring, which will allow for the appropriate corrective action to occur. The authors have collected and analyzed multiple bridge LiDAR data sets, each of which displaying a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in AcrGIS Python were used to locate and quantify surface defects. The results were visual and numerically displayed in a user-friendly interface integrating prior bridge condition metrics for comparison.

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