Abstract
Asphalt recycling technologies have advanced considerably over the last few decades with the utilization of reclaimed asphalt pavements (RAP) and recycled asphalt shingles (RAS). Characterizing aged and heterogeneous binders in these mixtures is challenging, particularly with limited extracted binders. This study suggests a data-driven framework that considers the rheological, chemical, and thermal characteristics to predict the binders' performance. Ninety-seven mixtures with 0–35% of the asphalt binder replaced with RAP/RAS binders were included as cores from the field, plant-produced mixtures, and laboratory-fabricated mixtures. The binders were chemically quantified using aging, aromatic, and aliphatic indices. Thermal analyses of the binders involved the percentage of the thermal residue. The framework predicted the rheological resistance of the binders to rutting and cracking using linear and nonlinear machine learning models. The nonlinear models outperformed the linear models for the three rheological parameters. The nonlinear models achieved a 69% reduction in the root mean square error (RMSE) for rutting, a 37% reduction in the RMSE for fatigue cracking, and a 21% reduction in the RMSE for thermal cracking. However, the nonlinear models overfitted for block cracking and had an RMSE 41% higher than the linear models, despite a perfect correlation (R = 1.00). The feature importance demonstrated the strong effects of the chemical and thermal parameters on rheological prediction. The data-driven framework can successfully support efforts to better manage asphalt recycling by predicting the binder performance.
Recommended Citation
E. Deef-Allah and M. Abdelrahman, "Data-Driven Prediction of Binder Rheological Performance in RAP/RAS-Containing Asphalt Mixtures," Applied Sciences Switzerland, vol. 15, no. 13, article no. 6976, MDPI, Jul 2025.
The definitive version is available at https://doi.org/10.3390/app15136976
Department(s)
Civil, Architectural and Environmental Engineering
Publication Status
Open Acess
Keywords and Phrases
aging indices; aliphatic; aromatic; asphaltene; FTIR spectroscopy; machine learning; RAP; RAS; TGA; thermal residue
International Standard Serial Number (ISSN)
2076-3417
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2025 The Authors, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 Jul 2025
Comments
Missouri University of Science and Technology, Grant TR201807