Long-Term Pavement Performance Effectiveness of Preventive Maintenance Treatments Using Markov Chain Algorithm

Abstract

In the Long-term Pavement Performance (LTPP) study, the SPS-3 experiment was designed to assess the performance of different flexible pavement maintenance treatments, relative to the performance of untreated control sections. The experiment consists of a control section and four maintenance treatments: thin overlay, slurry seal, chip seal, and crack seal. Several studies in the past have evaluated the effectiveness of the maintenance treatments; however, there is a need to re-evaluate the results as more performance data become available. This paper uses Markov chain algorithm (MCA) to evaluate the effectiveness of maintenance treatments at the network level. For each treatment, the transition matrices were determined from the observed time series performance data for ride quality, fatigue cracking, and rutting. The advantages of using MCA includes the ability to dynamically model pavement deterioration and improvement at the same time, evaluate the impact of initial pavement conditions on the short- and long-term performance, and relative comparison of pavement performance among different maintenance treatments. The results show that different maintenance treatments have varying effectiveness depending on the distress type. For example, thin overlay is more effective in the long-term for improving IRI and rutting while chip seal seems to be a better choice in case of alligator cracking. Generally, different seals considered in the SPS-3 experiment are more effective when applied to a network in good condition while overlay is more effective for a network in poor condition.