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As one of critical components of a transportation infrastructure system, bridges are very important to a country’s economy because they provide passage over physical obstacles to substantially reduce travel time and travel cost. Similar to other types of transportation infrastructure, bridges deteriorate over time. Therefore, bridges should be routinely inspected to ensure their serviceability, capacity, and safety under current traffic. Subsequently, transportation agencies at all levels (e.g., federal, state, local, and tribal) dedicate large amounts of time and money to routinely monitor and inspect bridge conditions as part of their infrastructure asset management programs. These transportation agencies use the collected data to make maintenance, repair, and construction decisions. As one important component of bridge inspection, bridge deck inspection ensures the serviceability and safety of everything above, on, and in bridge decks. 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, labor-intensive, time-consuming, can exhibit a high degree of variability, requiring specialized staff on a regular basis, and unsafe. Recent advancements in remote sensing, especially small-unmanned aircraft systems (S-UAS) based airborne imaging techniques and object based image analysis techniques, have shown promise in improving bridge deck inspection. This project explored the utility of S-UAS based airborne imaging techniques and object based image processing techniques in developing a complete data acquisition and analysis system to accurately and rapidly detect and assess bridge deck wearing surface and subsurface distresses at a low cost. This project developed a guidebook for the implementation of the proposed S-UAS based inspection system to assist transportation agencies with workforce development and professional training.


Tran-SET Project: 19STUNM04