Volume 21 Issue 4
Sep.  2021
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(4): 32-47
TAN Yi-qiu, XIAO Shen-qing, XIONG Xue-tang. Review on detection and prediction methods for pavement skid resistance[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 32-47. doi: 10.19818/j.cnki.1671-1637.2021.04.002
Citation: TAN Yi-qiu, XIAO Shen-qing, XIONG Xue-tang. Review on detection and prediction methods for pavement skid resistance[J]. Journal of Traffic and Transportation Engineering, 2021, 21(4): 32-47. doi: 10.19818/j.cnki.1671-1637.2021.04.002
shu

Review on detection and prediction methods for pavement skid resistance

doi: 10.19818/j.cnki.1671-1637.2021.04.002
  • 1.

    School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China

  • 2.

    State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, Heilongjiang, China

Funds:

National Natural Science Foundation of China U20A20315

National Key Research and Development Program of China 2016YFE0202400

More Information
  • Author Bio:

    TAN Yi-qiu(1968-), female, professor, PhD, yiqiutan@163.com

  • Received Date: 2021-03-15
    Available Online: 2021-09-16
  • Publish Date: 2021-08-01
    Abstract
  • The relevant achievements and progress of skid resistance performance of pavements were systematically reviewed based on three aspects: mechanical mechanism, detection methods, and prediction models. The friction mechanism of pavement skid resistance was introduced based on the traditional Coulomb friction law, and the factors influencing the skid resistance were summarized based on road surface, tire, and contact environment. The direct and indirect measurement methods of skid resistance were evaluated, and the difficulties of road-surface texture detection and test data preprocessing methods were analyzed. The advantages and disadvantages of the skid resistance prediction methods, including traditional empirical statistical models, mechanical models, and machine learning, were compared and analyzed. Analysis results show that many factors influence the skid resistance of pavements, and it is difficult to describe the mechanical behavior of the third body between rubber and rough surface. Thus, further investigations are required to reveal the friction mechanism toward the contact interface with the lubrication medium.For the single function and high cost of the direct detection of skid resistance, surface texture detection using automatic high-speed noncontact measurements will be more in line with future intelligent integrated requirements. However, high-precision and large-range detection and data cleaning are still bottlenecks that need to break through. Compared to the existing prediction models, the tire-pavement contact characteristics are weakened by the empirical statistical model and machine learning, resulting in a lack of scalability in the prediction model. The implementation of the finite element simulation model method is expected to reveal the friction mechanism under complex physical fields to develop a more precise and efficient model for predicting the skid resistance of pavements. 1 tab, 13 figs, 89 refs.

     

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