ORIGINAL RESEARCH article
Front. Mater.
Sec. Polymeric and Composite Materials
Optimization Analysis of Theoretical Maximum Specific Gravity of Asphalt Mixtures in Segregated Areas of Asphalt Pavements Based on Asphalt Medium Method
Provisionally accepted- 1Poly Changda Engineering Co., Ltd., Guangzhou, China
- 2Foshan University, Foshan, China
- 3South China University of Technology, Guangzhou, China
- 4XIAONING INSTITUTE OF ROADWAY ENGINEERING, Guangzhou, China
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Abstract: During the transportation, paving, and compaction of hot mix asphalt (HMA), aggregate segregation occurs, leading to changes in the theoretical maximum specific gravity (Gmm) of the asphalt mixture. To accurately determine the Gmm of the asphalt mixture after segregation, field samples of the asphalt mixture were collected and analyzed using the ignition method to evaluate the extent of aggregate segregation at different locations (auger and paver overlap zones). The asphalt immersion method was employed to precisely measure the Gmm. Grey relational analysis was applied to identify influencing factors and their patterns of impact on Gmm, and the relational degree of each factor was determined. By introducing weight factors and variation ratios for each factor, an optimized prediction model for Gmm was established. The results indicate that the specific gravity ratio of coarse to fine aggregate is the most significant factor affecting Gmm. The error between the predicted air void content of the asphalt pavement based on the corrected Gmm and the core-measured values obtained via the asphalt immersion method does not exceed 0.5%. This model is suitable for estimating the Gmm of segregated asphalt mixtures, thereby improving the accuracy of compaction quality assessment in segregated areas of asphalt pavements. Keywords: Asphalt mixture; aggregate segregation; theoretical maximum specific gravity; asphalt immersion method; grey relational analysis
Keywords: asphalt mixture, aggregate segregation, theoretical maximum specific gravity, asphalt immersion method, Grey relational analysis
Received: 02 Oct 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Wang, Xiong, Lian, Wan, Chen, Cai, Su, Huang, Li and Xiong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xuetang Xiong
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