Bridge deck surface distress evaluation using S-UAS acquired high-spatial resolution aerial imagery

• Published in: Annals of GIS Vol. 29; no. 2; pp. 261 - 272
• Main Authors: Zhang, Su, Bogus, Susan M., Baros, Shirley V., Neville, Paul R. H., Barrett, Hays A., Eshelman, Tyler
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 03.04.2023; Taylor & Francis Group
• Subjects: aerial imagery; Bridge deck; evaluation; UAS; wearing surface distress
• ISSN: 1947-5683; 1947-5691
• DOI: 10.1080/19475683.2023.2166112

Bridge decks need to be routinely inspected to ensure their serviceability, capacity, and safety under current traffic conditions. Traditionally, bridge deck inspection is performed on the ground by having inspectors either visually inspect surface conditions or interpret the acoustic feedback from hammer sounding or chain dragging to determine subsurface conditions. These traditional methods have many limitations, including but not limited to, expensive, labour-intensive, time-consuming, subjective, can exhibit a high degree of variability, requiring specialized staff on a regular basis, and unsafe. Recent advancements in remote sensing, especially small-uncrewed aircraft systems (S-UAS) based airborne imaging techniques and advanced image analysis techniques, have shown promise in improving current bridge deck inspection practices by providing an above-ground inspection method. This research explored the utility of S-UAS-based airborne imaging techniques and image processing techniques to develop a complete aerial data acquisition and analysis system to accurately detect and assess bridge deck wearing surface distresses in a timely and cost-effective manner. As part of the research project, a robust tool was also developed with the aim of being able to detect, extract, and map bridge deck wearing surface distresses with an adequate degree of accuracy while maximizing the ability to assist bridge inspectors with varying expertise. Research results revealed that the developed tool is able to effectively detect and map bridge deck wearing surface distresses at a high accuracy.