Abstract
The combination of low temperatures, precipitation, and high-water table causes the formation of subsurface frost in pavements leading to the formation of frost heaves. This leads to significant damage to the structural integrity of the pavements and causes extensive surface roughness. Frost heaving is a common type of distress in cold climate regions, especially for road and airport infrastructures located in remote areas. Research discussed in this article focuses on determining the efficiency and accuracy of photogrammetry-based sensing systems to measure the extent of pavement frost heaves. The sensors are mounted on an unpiloted aircraft system (UAS) capable of providing measurements over large spatial domains in a single flight and are well suited for difficult-to-access regions. Experiments have been conducted on simulated heaves as well as actual cold climate pavement sections. The research described herein provides suitable UAS flight parameters for conducting frost heave measurements. Flight at an altitude of 50 m above the ground surface and a flight speed of 5 m/s with capturing images every 2 s resulted in more than 80 % front and side image overlap. The pavement surface profile has been constructed from extracted photogrammetry elevation data collected in three different seasons. Pavement surface roughness has been measured in terms of the international roughness index (IRI), and variation between measured IRI values has been evaluated during different seasons because of the formation of the frost heave distress. As it is expected, the IRI values increase as the cold season approaches. Moreover, the measured IRI values have been compared with the most recent available IRI data collected by an instrumented vehicle. The key outcomes of this work demonstrate that photogrammetry can reliably detect pavement frost heaves and provide high confidence in the future development of an automated system to measure pavement roughness that is attributable to frost heave distresses.
