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Temperature-Dependent Models for Rutting Performance of Asphalt Pavement Surface Layer Materials Under Varying Load
Jincai Yang1, Guanqing Li2, Yantao Chen2
1Nanyang Communications Construction Investment Group Co., Ltd., Nanyang 473000, China.
This study investigates asphalt pavement performance at high temperatures, finding that increased temperature significantly reduces dynamic stability and increases rut depth. Modified asphalt mixtures show superior high-temperature resistance compared to base mixtures.
Area of Science:
- Civil Engineering
- Materials Science
- Geotechnical Engineering
Background:
- High-temperature performance is crucial for asphalt pavement durability.
- Rutting is a major failure mode in asphalt surfaces under high temperatures and traffic loads.
- Understanding temperature dependence is key to predicting and preventing pavement distress.
Purpose of the Study:
- To characterize the temperature-dependent high-temperature performance of asphalt pavement surface layer materials.
- To investigate the influence of temperature, load, and number of actions on anti-rutting performance.
- To develop predictive models for high-temperature performance and rutting.
Main Methods:
- Indoor rutting tests were conducted on AC-13 and AC-16 asphalt mixtures.
- Temperature-dependent models for dynamic stability were established.
- A rutting prediction model incorporating temperature, load, and action indices was constructed.
Main Results:
- Dynamic stability decreases with increasing temperature following an S-shaped curve.
- Rut depth increases exponentially with temperature.
- Modified asphalt mixtures (SBS, HMB) exhibited significantly higher dynamic stability than base asphalt mixtures.
- Transition temperatures for dynamic stability varied among different asphalt types.
- Rutting prediction model achieved correlation coefficients above 0.95.
Conclusions:
- Temperature is a critical factor influencing asphalt pavement high-temperature performance.
- Modified asphalt binders enhance resistance to rutting.
- Developed models accurately predict rut depth based on temperature, load, and traffic volume.
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