Quantitative Characterization and Improvement Method of Blending Efficiency of Virgin and Aged Asphalt in Recycled Asphalt Mixtures

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Authors: Chao Yang, Zhenwei Huang, Xingyang He, Ying Su, Jin Yang, Zihao Jin, Huahui Qi, Cong Tian, Qiao Liu, Fusong Wang, Lei Zhang
Cited by: 2
References: 96

Abstract

A critical factor in promoting the high-value application of recycled asphalt mixtures (RAMs) is the quantitative characterization of the degree of blending (DOB) between virgin and aged asphalt, along with efficient improvement methods. This paper reviews recent advances in the quantification and improvement methods for the blending efficiency of virgin and aged asphalt in RAMs. It delves into asphalt extraction and tracing technologies, assessing the strengths and weaknesses of various tracers. The paper also provides a thorough overview of various quantitative characterization methods for the DOB at the macroscale, mesoscale, microscale, and molecular scale for quantifying the DOB at different scales. Furthermore, it proposes strategies to enhance the DOB by optimizing factors. Accurate asphalt extraction is essential for subsequent RAM investigations and deserves further survey. Selection of suitable tracer technology for determining the DOB is crucial to ensure their effectiveness and practical usability. Optimizing blending methods, including increasing blending temperature and time, significantly improves the DOB between virgin and aged asphalt. Techniques such as microwave and induction heating are highlighted for their ability to enhance asphalt fluidity and permeability, thus markedly boosting the DOB. This work offers valuable insights and guidance for developing high-performance RAM in sustainable pavement construction.

Fields of Study & Subjects

Asphalt Pavement Performance Evaluation Infrastructure Maintenance and Monitoring Railway Engineering and Dynamics

Publication details

Journal / source
Journal of Materials in Civil Engineering
Publisher
American Society of Civil Engineers (ASCE)
Volume
38
Issue
4
Publication date
31 January 2026
Language
EN