AbstractPeriodic bridge deck inspection is critically important in bridge management and maintenance. This paper presents a comprehensive bridge deck evaluation using sub-mm three-dimensional (3D) laser imaging technology in a nondestructive and efficient manner at highway speeds with complete lane coverage. A total of 58 bridge decks in Oklahoma were evaluated via Pave3D 8K system and 3D Safety Sensor. The Pave3D 8K system collected both 0.5-mm 3D height images, 2D grayscale images, and longitudinal profiler at highway speeds for bridge deck assessment in terms of distress detection and roughness evaluation. A Deep Learning-based method was applied to detect cracks automatically, and a manual tool was developed to label other distress for bridge decks and joints from the obtained images. Further, the 0.5-mm 3D images and longitudinal profiler data were combined to identify locations with roughness issues and the causes on bridge decks or approach slabs. Finally, the 3D Safety Sensor acquired 0.1-mm 2D/3D images for hydroplaning speed prediction of bridge decks. The evaluation results demonstrate the efficacy of the sub-mm 3D laser imaging technology for nondestructive bridge deck conditions and safety evaluations.

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