Performance of the UAS-LiDAR sensing approach in detecting and measuring pavement frost heaves

Abstract

LiDAR technology, increasingly prevalent in various applications, has not been significantly used for assessing pavement elevation changes due to frost-induced heaves. This research examines the efficiency and accuracy of LiDAR sensors mounted on an Unpiloted Aerial System (UAS) to measure pavement frost heaves in remote cold climate regions. Experiments on simulated heaves and actual pavement sections assessed flight pattern data gathering protocols and data processing methods. Optimal measurements of simulated frost heave were achieved with a 45m altitude, 2m/s ground speed, and 10cm x 10cm spatial resolution. The finalized protocol was applied to in-service pavement sites in a cold climate region, comparing seasonal results to demonstrate the ability of LiDAR sensors to capture frost-induced heaving on road surfaces. The study shows that UAS-LiDAR can reliably capture vertical deviations due to surface roughness, supporting the development of an automated system to measure pavement roughness caused by frost heave distresses.