Volume 40 Issue 1
Feb.  2022
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XING Xiaoliang, WANG Xiaocun, ZHANG Yu, GAO Lixiao, FAN Zhaodong. Influences of Adverse Meteorological Micro-environment on Skid Resistance of Airport Pavement[J]. Journal of Transport Information and Safety, 2022, 40(1): 80-88. doi: 10.3963/j.jssn.1674-4861.2022.01.010
Citation: XING Xiaoliang, WANG Xiaocun, ZHANG Yu, GAO Lixiao, FAN Zhaodong. Influences of Adverse Meteorological Micro-environment on Skid Resistance of Airport Pavement[J]. Journal of Transport Information and Safety, 2022, 40(1): 80-88. doi: 10.3963/j.jssn.1674-4861.2022.01.010

Influences of Adverse Meteorological Micro-environment on Skid Resistance of Airport Pavement

doi: 10.3963/j.jssn.1674-4861.2022.01.010
  • Received Date: 2021-10-29
    Available Online: 2022-03-31
  • The performance and safety of airport pavement is affected by adverse meteorological micro-environment directly. On the basis of analyzing the function mechanism of adverse meteorological microenvironment, the correlation of key impact factors and friction coefficient under different meteorological micro-environment conditions are studied through pavement icing tests carried out in an environment chamber. The prediction models of the thickness of water film, snow, ice, and skid resistance of pavement are proposed. The results show that the friction coefficient of the pavement covered by thick ice is between 0.09 and 0.15, where skid resistance is the worst. Although thin ice & water-covered pavement, thick ice & water-covered pavement, and the pavement covered by thin ice have better skid resistance than the pavement covered by thick ice, they still cannot meet the requirements for safe operation of airplanes and working vehicles. In addition, the friction coefficient of snow-covered pavement is good and it usually ranges between 0.37 and 0.46, but such pavement may form a smooth surface under load pressure, which will seriously affect the traffic safety of airports. A relationship model between water film, snow, ice thickness, and skid resistance of airport pavement is developed through a multiple nonlinear regression analysis. It is found that the goodness of fit of the proposed model is greater than 0.8, which meets the requirements for the goodness-of-fit and significance test of regression analysis. It is believed that the models developed are useful for providing early warning for low skid resistance of airport pavement.

     

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