Using long-term pavement performance data to predict seasonal variation in asphalt concrete modulus

The seasonal variation of the asphalt concrete (AC) modulus with changes in pavement temperature is discussed. The main goals of the research was to develop (a) regression models that enable design engineers to assess seasonal changes in AC modulus and (b) an algorithm for calculating a seasonal adjustment factor (SAF) that allows estimating AC modulus in any season from a known reference value. The study is based on analyzing data collected at Long-Term Pavement Performance (LTPP) program sites in both freezing and nonfreezing zones. The data were obtained from the LTPP database in the DataPave 3.0 software. The approach adopted in this study was to select LTPP-seasonal monitoring program sites that represent various climatic regions and use the backcalculated modulus and pavement temperature data to develop regression models for the modulus-temperature relationships for various sites in both freezing and nonfreezing zones. Two regression models were developed to relate the variation in modulus with the variation in pavement temperatures in various seasons for both freezing and nonfreezing zones. These models incorporate AC layer properties such as thickness, bulk specific gravity, air voids, and asphalt binder grade. A model for determining the SAF was also developed.