Correlations among chemical and rheological aging indices/indicators of asphalt binder at high temperatures

This study examines the relationships between chemical and rheological-viscoelastic aging indicators of asphalt binders to clarify how oxidation-induced changes are reflected in high-temperature performance. Three neat binders with penetration grades of 40-50, 60-70, and 85-100 were subjected to short-term and then progressive long-term aging using the Rolling Thin Film Oven and multiple Pressure Aging Vessel (PAV) cycles. Aging was characterized using Fourier Transform Infrared (FTIR) spectroscopy, dynamic shear rheometry, and rotational viscosity measurements. While most previous studies have either examined relationships between a limited number of chemical and rheological indicators or focused exclusively on rheological aging indices, this study evaluates multiple key indicators, including an FTIR-based combined carbonyl-sulfoxide aging index, the Superpave rutting parameter (G^*/sinδ), zero shear viscosity (ZSV), apparent viscosity, and selected parameters of the 2S2P1D viscoelastic model. The results show that all indicators increase with aging, with the most pronounced changes occurring during the first PAV cycle. The FTIR-based combined carbonyl-sulfoxide index effectively reflects aging progression, and strong correlations were observed between chemical and rheological indices, including linear relationships between the FTIR aging index and both Log(ZSV) and the 2S2P1D α parameter, as well as a strong relationship between apparent viscosity and G^*/sinδ. In addition, the Superpave parameter G^*/sinδ at 60 °C exhibited a strong power-law correlation with the FTIR aging index and a robust exponential relationship with Log(ZSV) at the same temperature. These findings demonstrate that chemical oxidation is consistently reflected in rheological-viscoelastic behavior and reveal robust, trend-based relationships that can support flexible interpretation of binder aging and provide a basis for simplified, data-driven aging assessment.