Development of mechanical properties predictive model of extracted asphalt binder based on long-term field asphalt pavements

Abstract

This paper was to develop a prediction framework for obtaining the rheological properties of extracted and recovered asphalt binders utilizing mixture performance and other factors such as in-place air voids, pavement structure, traffic level, and aging duration. A total of 21 field projects consisting of 66 pavement sections were included in the analysis, which were located in different climatic zones with various RAP contents, traffic levels, pavement structures, pavement age, and material properties. Results indicate that the neural network can be successfully adopted to predict cracking-related binder properties consisting of effective asphalt binder, G*sin(δ) and binder fracture energy, and rutting-related binder properties including high temperature performance grade (PG), MSCR Jnr3.2, and R3.2. Both prediction and validation models worked well for pavement sections with varied contents of RAP when new data was introduced.