A new long-term aging model for asphalt pavements using morphology-kinetics based approach

Abstract

Asphalt pavements inevitably experience the long-term oxidative aging due to exposure under field conditions, which severely degrades the pavement performance and serviceability. Thus, accurate prediction of the long-term aging is important. Most studies on long-term aging prediction exhibit both systematic bias and data scatter due to their weakness of fully characterizing the field aging condition. This study presents a new long-term aging model for asphalt pavements, which focuses on the pavement binder viscosity as the target property and makes use of combined kinetics and mixture morphology framework for the model development. In this new model, the two-stage aging kinetics, representing the fast and constant aging reaction rates, are utilized to capture the oxidation mechanism of the long-term aging, which have the default values for different climate zones; the rheological kinetics are introduced to evaluate the temperature sensitivity of the long-term aging; a morphology parameter (i.e. primary structure coating thickness) that quantifies the asphalt mastic coating thickness on the load-bearing structure in the mixture is incorporated to characterize the effects of mixture morphology on the long-term aging. Besides, an energy-based pavement temperature model that considers climatic properties and site-specific pavement parameters is employed to determine the pavement field aging temperature. A large data set extracted from the Long-Term Pavement Performance (LTPP) database is used for the global model coefficients determination as well as the model validation, which takes up of 85% and 15% of the data set, respectively. A statistical analysis is performed to evaluate the model prediction accuracy, which indicates that the new model is able to accurately predict the long-term aged viscosity without any significant bias. In this regard, it is believed that the new long-term aging predictive model is a suitable candidate for the long-term aging prediction of asphalt pavements.