Development of a New Dynamic Modulus Predictive Model Based on Binder Viscosity for the Superpave Mixtures of New Mexico

Abstract

The newly developed mechanistic-empirical pavement design method uses the dynamic modulus as one of the crucial input parameters for the asphalt pavement to be designed or analyzed. This study proposes a new regression-based predictive model to estimate dynamic modulus of asphalt concrete from the viscosity of the asphalt binder used in the asphalt-aggregate mixture. Other parameters related to the aggregate gradation, such as, fineness modulus, and uniformity coefficient and the parameters related to the mixture volumetric are also incorporated in this model. A total of 21 asphalt concrete mixtures with asphalt binders having different performance grades and Superpave gradations were collected from different mixing plants and paving sites at various regions of New Mexico. The collected mixtures were then compacted, cored and sawed to cylindrical specimens. The asphalt concrete specimens were then tested for dynamic modulus at different temperatures and loading frequencies. The time-temperature superposition principle was then applied to develop dynamic modulus mastercurves at 70 °F (21.1 °C) reference temperature. The mastercurves were then fitted by the sigmoid function. The parameters of the sigmoid function were then correlated to the physical attributes of the asphalt concrete samples. Finally, a predictive model is developed to estimate the dynamic modulus of the AC mixtures typically used in New Mexico. Statistical evaluation showed that a fairly accurate estimation of dynamic modulus can be found by using this new dynamic modulus predictive model.