Relating Aggregate Friction Properties to Asphalt Pavement Friction Loss Through Laboratory Testing, Statistical Analysis, and Machine Learning Insights
Abstract
This study applied statistical and machine learning approaches to relate aggregate friction properties and shape characteristics to asphalt mixture friction loss, focusing on gap- and dense-graded (DG) mixtures. Aggregate morphology and friction properties were quantified through an aggregate imaging measurement system (AIMS), a dynamic friction tester (DFT), and a British pendulum tester (BPT) over varying Micro-Deval test (MDT) durations. Meanwhile, DFT and circular track metre (CTMeter) assessed the friction and texture properties of asphalt mixtures in conjunction with the three-wheel polishing device (TWPD), simulating traffic-induced polishing. Random Forest Analysis (RFA) emphasized the significance of aggregate terminal friction properties in pavement frictional performance. Loss of aggregate friction properties (%), measured by DFT at 20 km/hr (DFT20), was the most significant material property for evaluating pavement friction loss, accounting for approximately 20% of the total effect, followed by the terminal texture index. Two analytical models were proposed: the first correlated the friction properties of asphalt mixtures to the corresponding aggregate sources using DFT20 values. Meanwhile, the second model integrated mixture gradation and aggregate morphology, achieving an overall coefficient of determination (R2) of 0.93. Finally, the study provides a preliminary approach for screening aggregate quality and mix design to achieve cost-effective, optimal frictional performance.
Recommended Citation
A. S. El-Ashwah and M. Abdelrahman, "Relating Aggregate Friction Properties to Asphalt Pavement Friction Loss Through Laboratory Testing, Statistical Analysis, and Machine Learning Insights," International Journal of Pavement Engineering, vol. 26, no. 1, article no. 2456739, Taylor and Francis Group; Taylor and Francis, Jan 2025.
The definitive version is available at https://doi.org/10.1080/10298436.2025.2456739
Department(s)
Civil, Architectural and Environmental Engineering
Keywords and Phrases
aggregate morphology; aggregate rings; highway safety; machine learning; Skid resistance
International Standard Serial Number (ISSN)
1477-268X; 1029-8436
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Taylor and Francis Group; Taylor and Francis, All rights reserved.
Publication Date
01 Jan 2025
Comments
Missouri Department of Transportation, Grant None