Analysis of genetic characteristics and structure-performance mechanisms in multi-source asphalt

Abstract:

To reveal the intrinsic structure-property relationships between the inherent "genetic" characteristics of multi-source base asphalt and its performance, this study selected ten commonly used base asphalts from different crude oil sources. Through systematic characterization of the macroscopic properties (such as the three major indicators), chemical composition (saturates, aromatics, resins, and asphaltene content), and elemental composition of the base asphalts, combined with compositional analysis and correlation analysis, it was revealed that the resin-to-asphaltene ratio (I_R/A) exhibits a very strong correlation with the rotational viscosity at 135 ℃, while the resin-to-aromatics ratio (I_R/A) only shows a strong negative correlation with the softening point after aging. Furthermore, combined with gel permeation chromatography test results, it was found that the synergistic effect between molecular weight distribution characteristics and the four fractions significantly influences the rheological behavior of asphalt. Among these, the weight-average molecular weight demonstrates a strong negative correlation with ductility after aging. The polydispersity index exhibits strong or very strong positive or negative correlations with the penetration index (PI), softening point after aging, standard viscosity, and rotational viscosity at 135 ℃. This indicates that a broader molecular weight distribution leads to higher temperature susceptibility and viscosity of asphalt, while reducing its ductility. Through partial least squares regression analysis, the influence of the four fractions and molecular weight parameters on macroscopic properties was further validated, and structure-property equations between these "genetic" characteristics and asphalt performance were established. To some extent, the constructed structure-property relationship model allows for the prediction of corresponding performance indicators based on genetic characteristic parameters. This study provides a theoretical foundation for the precise design and performance prediction of asphalt materials in the future.