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Related Experiment Video

Updated: Sep 1, 2025

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall
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Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall

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Evaluate Pavement Skid Resistance Performance Based on Bayesian-LightGBM Using 3D Surface Macrotexture Data.

Yuanjiao Hu1, Zhaoyun Sun1, Yuxi Han1

  • 1School of Information Engineering, Chang'an University, Xi'an 710064, China.

Materials (Basel, Switzerland)
|August 12, 2022
PubMed
Summary
This summary is machine-generated.

Road surface texture significantly impacts skid resistance, crucial for preventing accidents. A new Bayesian-LightGBM model accurately predicts pavement skid resistance using 3D texture features, outperforming other algorithms.

Keywords:
3D macrotexture characteristicsBayesian-LightGBMfeature importance analysisfriction coefficientskid resistance

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Transportation Safety

Background:

  • Skid resistance is vital for road safety, directly linked to pavement surface friction.
  • Surface texture significantly influences tire-pavement friction and overall skid resistance performance.
  • Understanding the relationship between texture and friction is key to improving road safety.

Purpose of the Study:

  • To investigate the correlation between asphalt mixture surface texture and skid resistance.
  • To develop and validate a predictive model for pavement skid resistance using texture features.
  • To compare the skid resistance performance of asphalt concrete (AC) and open-graded friction course (OGFC) mixtures.

Main Methods:

  • Collected 3D point cloud data of asphalt mixture surfaces using a Gocator 3110 sensor.
  • Measured friction using the British pendulum tester.
  • Extracted ten macrotexture features and constructed a dataset of 200 texture-friction data groups.
  • Trained and optimized a Bayesian-LightGBM model for skid resistance prediction.

Main Results:

  • The Bayesian-LightGBM model achieved a high accuracy of 92.83% R-squared value for skid resistance evaluation.
  • Ten macrotexture features were identified as significant contributors to skid resistance prediction.
  • Open-graded friction course (OGFC) mixtures exhibited higher roughness and superior skid resistance compared to asphalt concrete (AC).

Conclusions:

  • The developed Bayesian-LightGBM model offers a more accurate method for evaluating pavement skid resistance based on surface texture.
  • Macrotexture features play a crucial role in determining pavement friction and safety.
  • OGFC mixtures demonstrate enhanced skid resistance, suggesting their suitability for improving road safety.