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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. [ABSTRACT FROM AUTHOR]

All transportation management agencies face the yearly challenge of inspecting the condition of myriad miles of road surface. With routinely acquired and publicly available geospatial data and geospatial modelling techniques, there is potential to substantially reduce the number of survey sites required to characterize overall pavement surface distress condition. Using roadway pavement assets in the State of New Mexico as an example, this study investigated if overall pavement surface conditions could be estimated based on geospatial modelling. A total of 17 explanatory variables, which were extracted from three types of geospatial data, including traffic volumes, environmental conditions and topography, were used to estimate overall pavement surface distress conditions. Results show that overall pavement surface conditions can be effectively (R2 > 0.9) estimated based on the extent of geospatial data and inferential modelling techniques with fewer survey sites, substantially reducing the cost and time for pavement surface distress condition survey. These results also open the way for the future application of geospatial modelling techniques for automated assessment of overall pavement surface distress conditions, which only require users to collect the geospatial data for the surveyed sites and upload the geospatial data to the system, while the computing-intensive process such as model development is fully automated. [ABSTRACT FROM AUTHOR]