A computational tool for the reliable prediction of pavement performance based on temperature data

Project summary:

The increasing frequency of extreme events associated with climate change significantly impacts the degradation process of pavements. Fluctuations in environmental conditions are becoming as relevant as traffic loading in contributing to the aging and degradation of pavements in Minnesota. In particular, the rising temperatures and frequency of heat waves in Minnesota are critical factors affecting pavement performance throughout its life cycle. Preliminary analyses performed using performance and temperature data from flexible and rigid test sections of MnROAD have identified a potential relationship between the statistics of the temperature profile along the pavement depth and its condition, as determined by a forensic analysis. Therefore, this research project proposes the development of a probabilistic methodology to establish a robust relationship between pavement temperature fluctuations and pavement condition. This will facilitate the creation of innovative, temperature-based monitoring techniques and tools for predicting pavement degradation over time. The proposed methodology aims to enhance the information provided by roughness-based indexes (e.g., IRI) by incorporating subsurface pavement damage data at a reduced cost for data acquisition. Moreover, it will enable the determination of optimized preventive and predictive maintenance strategies, reducing the economic and environmental impacts to MnDOT and the state of Minnesota associated with pavement construction and rehabilitation.