Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
Source abstract
Abstract
Accurate assessment of road pavement friction is crucial for maintaining road safety. This study explores the prediction of the friction coefficient (µ) using 3D texture parameters of pavement surfaces. Measurements were conducted on 17 different rural road sections using the Traction Watcher One (TWO) for friction coefficients and a newly developed Static Road Scanner (SRS) for surface texture. Multiple linear regression models were created, incorporating texture parameters such as the valley material portion (Smr2,MIC), arithmetic mean peak curvature (Spc,MAC), and dale void volume (Vvv,PS). The results demonstrate a strong correlation between texture characteristics and friction, with R2 values up to 0.80 and an RMSE as low as 0.076, validating the model’s accuracy. This approach highlights the potential of using non-contact texture measurements for reliable prediction of friction, offering a significant advancement in pavement management and safety.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Highway and Environmental Engineering, Faculty of Civil Engineering, University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia
Funding
VEGA
References (31)
Show source-provided references
1. An Assessment of the Skid Resistance Effect on Traffic Safety under Wet-Pavement Conditions10.1016/j.aap.2009.05.004 ↗
2. A State-of-the-Art Review of Parameters Influencing Measurement and Modeling of Skid Resistance of Asphalt Pavements10.1016/j.conbuildmat.2016.04.002 ↗
3. Hall, J.W., Smith, K.L., Titus-Glover, L., Wambold, J.C., Yager, T.J., and Rado, Z. (2009). Guide for Pavement Friction, Transportation Research Board.
4. (2002). Characterization of Pavement Texture by Use of Surface Profiles—Part 2: Terminology and Basic Requirements Related to Pavement Texture Profile Analysis (Standard No. ISO 13473-2:2002).
5. De Martino, M., Costa, A.L.A., Timpone, F., and Sakhnevych, A. (2020). A Tire/Road Interaction Tool for the Evaluation of Tire Wear for Commercial Vehicles. Advances in Service and Industrial Robotics: Results of RAAD, Springer.10.1007/978-3-030-48989-2_31 ↗
6. Tyre/Road Interaction Model for the Prediction of Road Texture Influence on Rolling Resistance10.1504/IJVD.2014.060815 ↗
7. Kane, M., Riahi, E., and Do, M.-T. (2021). Tire/Road Rolling Resistance Modeling: Discussing the Surface Macrotexture Effect. Coatings, 11.10.3390/coatings11050538 ↗
8. Li, T. (2018). Influencing Parameters on Tire–Pavement Interaction Noise: Review, Experiments, and Design Considerations. Designs, 2.10.3390/designs2040038 ↗
9. Evaluation of Airfield Pavement Micro and Macrotexture in the Light of Skid Resistance (Friction Coefficient) Measurements10.1051/matecconf/201926205017 ↗
10. Gierasimiuk, P., Wasilewska, M., and Gardziejczyk, W. (2021). A Comparative Study on Skid Resistance of Concrete Pavements Differing in Texturing Technique. Materials, 14.10.3390/ma14010178 ↗
11. A Model for Hierarchical Anisotropic Friction, Adhesion and Wear10.1016/j.triboint.2020.106549 ↗
12. Rubber Friction, Tread Deformation and Tire Traction10.1016/j.wear.2008.02.016 ↗
13. Evolution of Coefficient of Friction between Tire and Pavement under Wet Conditions Using Surface Free Energy Technique10.1016/j.conbuildmat.2019.01.122 ↗
14. Theory of Rubber Friction and Contact Mechanics10.1063/1.1388626 ↗
15. Elastic Contact between Randomly Rough Surfaces: Comparison of Theory with Numerical Results10.1103/PhysRevB.65.184106 ↗
16. Contact Mechanics and Rubber Friction for Randomly Rough Surfaces with Anisotropic Statistical Properties10.1140/epje/i2009-10484-8 ↗
17. Calculation of Skid Resistance from Texture Measurements
18. Pranjić, I., and Deluka-Tibljaš, A. (2022). Pavement Texture–Friction Relationship Establishment via Image Analysis Methods. Materials, 15.10.3390/ma15030846 ↗
19. Yan, Y., Ran, M., Sandberg, U., Zhou, X., and Xiao, S. (2020). Spectral Techniques Applied to Evaluate Pavement Friction and Surface Texture. Coatings, 10.10.3390/coatings10040424 ↗
20. Multiresolution Analysis of Three-Dimensional (3D) Surface Texture for Asphalt Pavement Friction Estimation10.1080/10298436.2020.1726350 ↗
21. Fractal Assessment of the Surface Texture of Pavements10.1080/10298430108901723 ↗
22. Skid Resistance and Fractal Structure of Pavement Surface10.1016/S0960-0779(97)00085-4 ↗
23. Wang, Y., Lai, X., Zhou, F., and Xue, J. (2020). Evaluation of Pavement Skid Resistance Using Surface Three-Dimensional Texture Data. Coatings, 10.10.3390/coatings10020162 ↗
24. Research on Skid Resistance Performance Assessment and Rehabilitation Decision-Making Technology of Asphalt Pavement10.1051/e3sconf/202449003017 ↗
25. Simone, A., Lantieri, C., Vignali, V., and Bitelli, G. (2012, January 28–30). 3D Laser Scanner Technique for In Situ Analysis of Road Pavement Surface Texture. Proceedings of the 7th International Conference on Maintenance and Rehabilitation of Pavements and Technological Control, Auckland, New Zealand.
26. A Comprehensive Implementation of Road Surface Classification for Vehicle Driving Assistance: Dataset, Models, and Deployment10.1109/TITS.2023.3264588 ↗
27. Road Friction Estimation Based on Vision for Safe Autonomous Driving10.1016/j.ymssp.2023.111019 ↗
28. (2015). Geometrical Product Specification (GPS)—Filtration—Part 61: Linear Areal Filters—Gaussian Filters (Standard No. ISO 16610-61:2015).
29. Correlation between Morphology Parameters and Skid Resistance of Asphalt Pavement10.1093/tse/tdac002 ↗
30. Characterization of the Skid Resistance and Mean Texture Depth in a Permeable Asphalt Pavement10.1088/1757-899X/471/2/022029 ↗
31. Effect of Three-Dimensional Macrotexture Characteristics on Dynamic Frictional Coefficient of Asphalt Pavement Surface10.1016/j.conbuildmat.2016.09.088 ↗