Abstract
Skid resistance of pavement plays a vital role in transportation safety, and it continuously deteriorates because the pavement was exposed to traffic and environmental loadings. Although the evaluation law of asphalt pavement skid resistance has been intensively studied, few has focused on its impact on driving safety. To close this knowledge gap, the current study used the CarSim/Simulink simulation and back-propagation (BP) neural network method to establish the relationship between pavement characteristics and vehicle driving safety. The skid resistance performances of nine types of asphalt mixture specimen were evaluated using a full life cycle dynamic friction testing machine (LCDFM), and results showed that the deterioration rate of the dynamic friction coefficient (μLCDFM) first increases rapidly, then slowly decreases and finally stabilizes. The early-stage increase resulted from the polish of asphalt film from the aggregate surface. The simulation studies indicated that driving speed exhibits the most significant impact on driving safety, followed by vehicle type, mean texture depth (MTD), and polishing time. The MTD is negatively correlated with μLCDFM, and the other factors are positively correlated. The driving safety rankings of the three pavement types and three gradations examined are open-graded friction coarse (OGFC) > stone mastic asphalt (SMA) > asphalt concrete (AC). Meanwhile, a greater value of nominal maximum aggregate size (NMAS) also induces better driving safety owing to the more abundant surface macrotexture. The research findings from this study provide reference to the materials design and pavement skid resistance prediction. © 2024 American Society of Civil Engineers.
| Original language | English |
|---|---|
| Article number | 04024047 |
| Journal | Journal of Transportation Engineering Part B: Pavements |
| Volume | 150 |
| Issue number | 4 |
| Online published | 20 Sept 2024 |
| DOIs | |
| Publication status | Published - Dec 2024 |
Funding
This work was supported by the National Key Research and Development Program of China (Grant No. 2023YFB2603500).
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Research Keywords
- Asphalt pavement
- Laboratory accelerated polishing
- Neural network
- Skid resistance
- Vehicle dynamics simulation
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