Comparison of Antistripping Performance Evaluation Methods on Asphalt Runway Pavement in Cold Regions

Influenced by aircraft engine wake effects, aggregates stripped off from asphalt runway surfaces are more severe during winter than during the other seasons in cold regions. In this study, five types of asphalt specimens with asphalt–aggregate ratios (AARs) ranging from 3% to 7% were used to assess antistripping performance and foreign object debris (FOD) risk. Freeze–thaw cycles and repeated load tests were performed to obtain the ratio of stripped aggregates (RSA) using three different evaluation methods. The results showed that the RSA was effective in evaluating the antistripping performance of asphalt pavement using the proposed methods. An increase in the RSA was observed with an increase in the number of experimental cycles and a decrease in the AAR. The improved weight loss (IWL), image recognition (IR), and laser scanning (LS) methods showed reliable RSA prediction in different aspects. The IWL method showed high efficiency in estimating general stripped aggregates in large areas but low accuracy in identifying the location and condition of FOD risk. The IR method could accurately determine the area and locations of stripped aggregates. The LS method was the most effective in assessing FOD risk by distinguishing stripped coarse aggregate scaled-off from the surface. The combined applications of these three methods can be a helpful tool for antistripping performance assessments in asphalt runway pavement management in cold regions. In addition, hot mixed asphalt with an AAR between 5% and 6% is suggested for asphalt runway pavement design and construction to improve antistripping performance.