Prediction of Skid Resistance in Asphalt Pavements Under High-Temperature and High-Humidity Conditions Using Machine Learning

Abstract

The skid resistance of pavement surfaces is a critical factor in ensuring the safety of airport runways and highways. Traditional skid resistance testing methods are inefficient and susceptible to operational interference, rendering them unsuitable for rapid assessment. This study employs laser scanning technology to capture three-dimensional texture data from asphalt mixture specimens and extracts multi-source features, including texture parameters, fractal dimensions, ambient temperature, and relative humidity. Four machine learning models—Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Categorical Gradient Boosting (CatBoost)—were employed to predict the British Pendulum Number (BPN) under high-temperature and high-humidity conditions. The SHAP (SHapley Additive exPlanations) method was used to analyze the contribution of each feature. Results demonstrated that the CatBoost model achieved the highest predictive performance, with a coefficient of determination (R²) of 0.938, a mean absolute percentage error (MAPE) of 2.807%, and a root mean square error (RMSE) of 2.72. Humidity conditions were found to contribute significantly more to model predictions than temperature. Among texture-related features, the three-dimensional fractal dimension (DBC3) played a prominent role in macro-texture models, while the linear profile fractal dimension (DBC1) contributed most to micro-texture models. This research provides an effective approach for intelligent prediction of skid resistance in asphalt pavements under hot and humid climatic conditions.

Keywords

• Asphalt pavement
• skid resistance
• high temperature and high humidity
• machine learning

Supporting Institution

The Transportation Science and Technology Project of Shandong Province

Project Number

No. 2021B95

Ethical Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Thanks

The work is supported by the Transportation Science and Technology Project of Shandong Province (No. 2021B95), The authors are very grateful for the financial contribution and convey their appreciation for supporting this basic research.

References

1. Kienle R ,Ressel W ,Götz T , et al. The influence of road surface texture on the skid resistance under wet conditions[J].Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology,2020,234(3):313-319.DOI:10.1177/1350650117753995.
2. Singh D ,Patel H ,Habal A , et al. Evolution of coefficient of friction between tire and pavement under wet conditions using surface free energy technique[J].Construction and Building Materials,2019,204105-112.DOI:10.1016/j.conbuildmat.2019.01.122.
3. Hofko B ,Kugler H ,Chankov G , et al. A laboratory procedure for predicting skid and polishing resistance of road surfaces[J].International Journal of Pavement Engineering,2019,20(4):439-447.DOI:10.1080/10298436.2017.1309191.
4. Sergio C C ,Cesare S . A Review on Acoustic and Skid Resistance Solutions for Road Pavements[J].Infrastructures,2021,6(3):41-41.DOI:10.3390/INFRASTRUCTURES6030041.
5. Kane M ,Edmondson V . Skid resistance: understanding the role of road texture scales using a signal decomposition technique and a friction model[J].International Journal of Pavement Engineering,2020,23(2):1-15.DOI:10.1080/10298436.2020.1757669.
6. Do T M ,Cerezo V ,Ropert C . Questioning the approach to predict the evolution of tire/road friction with traffic from road surface texture[J].Surface Topography Metrology and Properties,2020,8(2):024004.DOI:10.1088/2051-672x/ab8ba9.
7. Panagouli O . Skid resistance and fractal structure of pavement surface[J].Chaos, Solitons and Fractals,1998,9(3):493-505.DOI:10.1016/S0960-0779(97)00085-4.
8. Vallejo* E L . Fractal Assessment of the Surface Texture of Pavements[J].International Journal of Pavement Engineering,2001,2(2):149-156.DOI:10.1080/10298430108901723.

9. Das P ,V N ,Chilakapati S K , et al. Mango ripening detection using machine learning optimized Sierpinski fractal antenna[J].International Journal of Remote Sensing,2026,47(6):2863-2886.DOI:10.1080/01431161.2026.2622641.
10. Shah A F ,Hamdani R S ,Tolasa T F , et al. Some generalized recurrence relations for nonlinear equations by using decomposition technique with application of fractal geometry[J].MethodsX,2026,16103835-103835.DOI:10.1016/J.MEX.2026.103835.
11. Tian C ,Wang L . Multi-process precision surface topography characterization and simulation based on multi-Gaussian and multi-fractal model[J].Measurement,2026,271121004-121004.DOI:10.1016/J.MEASUREMENT.2026.121004.
12. Murtaza S ,Sambas A ,Khan N M , et al. Thermal and Solutal Analysis Of A Ternary Hybrid Nanofluid Flow In A Porous Channel: A Fractal–Fractional Model[J].Fractals,2026,(prepublish):DOI:10.1142/S0218348X26400177.
13. Ullah I ,Khan M ,Ahmad I , et al. Stability Analysis Of An 풮ℰℐ풬ℛ Epidemic Model With Saturated Incidence Rate Using Fractal-Fractional Order Techniques[J].Fractals,2026,(prepublish):DOI:10.1142/S0218348X26400487.
14. Qin X ,Yang X ,Yin W , et al. A fractal modeling approach for effective thermal conductivity in unsaturated granular porous media with rough surfaces[J].International Communications in Heat and Mass Transfer,2026,173110852-110852.DOI:10.1016/J.ICHEATMASSTRANSFER.2026.110852.
15. Huber M ,Stępniewska K ,Huber W M . The Relationship Between the Fractal Dimension and the Evolution of Rock-Forming Minerals Crystallization on the Example of the Northwestern Part of the Lovozero Intrusion[J].Fractal and Fractional,2025,9(2):100-100.DOI:10.3390/FRACTALFRACT9020100.
16. Birhane N F ,Choi T Y ,Basyouny E K . A hybrid multi-task 1D CNN and XGBoost approach for ballast layer characterization using GPR data[J].Transportation Geotechnics,2026,59101972-101972.DOI:10.1016/J.TRGEO.2026.101972.
17. Peng Y ,Yu I G . Prediction of metal phytotoxicity and threshold identification in calcareous purple soils: An integrated framework using XGBoost–SHAP and PLS-SEM[J].Journal of Environmental Chemical Engineering,2026,14(2):122029-122029.DOI:10.1016/J.JECE.2026.122029.
18. Zheng G ,Yang L ,Li Z . Hybrid LSTM-XGBoost forecasting model of building cooling load based on WOA-DE algorithm[J].Energy & Buildings,2026,359117261-117261.DOI:10.1016/J.ENBUILD.2026.117261.
19. Hu J ,Chang J ,Zhang Z , et al. A Hybrid XGBoost-Augmented Gaussian Process Framework for Accurate Battery State-of-Health Estimation[J].Journal of The Electrochemical Society,2026,173(5):050510-050510.DOI:10.1149/1945-7111/AE4899.
20. Feng Y ,Yu H ,Zhuo L . Predicting Olympic Medal Distribution For LA 2028 Based On K-Means Clustering And An Xgboost-Bootstrap Ensemble[J].World Journal of Information Technology,2026,4(1):DOI:10.61784/WJIT3082.
21. Zhao Q ,He Y ,Zhang S , et al. Predicting perovskite solar cell stability using CatBoost with Bayesian optimization and SHAP interpretability analysis[J].Journal of Alloys and Compounds,2026,1057186879-186879.DOI:10.1016/J.JALLCOM.2026.186879.
22. Chen Y ,Cheng X ,Gong F , et al. Research on the Improvement of High-Temperature Performance of Asphalt by Waste Scallop Shell Powder[J].Materials,2025,18(5):983-983.DOI:10.3390/MA18050983.
23. Tian S ,Wang J ,Li G , et al. Functional Design of Skeleton-Dense Asphalt Mixtures in High Humidity and Heat Regions[J].International Journal of Pavement Research and Technology,2025,19(5):1-13.DOI:10.1007/S42947-025-00502-X.
24. Yu Y ,Wang H ,Wang H , et al. Texture evolution and skid resistance in polymer-modified asphalt mixtures for runway pavements during wear process[J].Construction and Building Materials,2025,498144037-144037.DOI:10.1016/J.CONBUILDMAT.2025.144037.
25. Design Specifications for Asphalt Concrete Pavements at Civil Airports: MH 5010-1999[S], 1999.
26. Abut Y ,Abut S . Improved Pavement Quality Assurance via ANN Prediction of Compaction and Water Absorption Using Marshall Parameters[J].International Journal of Pavement Research and Technology,2026,(prepublish):1-17.DOI:10.1007/S42947-026-00716-7.
27. Shaikh S ,Gupta A ,Lokhande V . Comparative analysis of different surrogate performance tests for evaluating the rutting potential of Marshall-designed bituminous mixtures[J].International Journal of Pavement Engineering,2025,26(1):DOI:10.1080/10298436.2025.2520982.
28. Jaya P E ,Sugito S . The Influence of Variations in the Thickness and Density of Hot Asphalt Pavement Layers on the Results of the Marshall Test Field Test of Laston AC-WC Mixture[J].Construction Technologies and Architecture,2025,1969-78.DOI:10.4028/P-V73N1G.
29. Jaleel M M ,Albdairi M ,Almusawi A . Marshall-Based Thermal Performance Analysis of Conventional and Polymer-Modified Asphalt Binders[J].Construction Materials,2025,5(2):40-40.DOI:10.3390/CONSTRMATER5020040.
30. Yuan J ,Feng Z ,Cui P . Research on Anti-Slip and Wear Resistance of Concrete Pavement After Optimization of Stone-Planting Process[J].Materials,2025,18(10):2210-2210.DOI:10.3390/MA18102210.
31. Qian F ,Chupeng C ,Feng L , et al. Analysis on Attenuation Law of Skid Resistance Performance of Asphalt Pavement with Thin Overlay[J].IOP Conference Series: Earth and Environmental Science,2021,781(2):DOI:10.1088/1755-1315/781/2/022100.
32. Singh D ,Patel H ,Habal A , et al. Evolution of coefficient of friction between tire and pavement under wet conditions using surface free energy technique[J].Construction and Building Materials,2019,204105-112.DOI:10.1016/j.conbuildmat.2019.01.122.
33. Carmon N ,Ben-Dor E . Mapping Asphaltic Roads’ Skid Resistance Using Imaging Spectroscopy[J].Remote Sensing,2018,10(3):430-430.DOI:10.3390/rs10030430.
34. Wang C ,Lan X ,Cong Y , et al. Evaluation of skid resistance of pavement maintenance energy-absorbing seal based on surface texture characteristics[J].Measurement,2025,245116588-116588.DOI:10.1016/J.MEASUREMENT.2024.116588.
35. Ren W ,Li J ,Zhang Y , et al. Road Surface Texture Evaluation and Relation to Low-Speed Skid Resistance for Different Types of Mixtures[J].Coatings,2024,14(11):1367-1367.DOI:10.3390/COATINGS14111367.
36. Wang W ,Liu B ,Jin D , et al. Durability Investigation of Ultra-Thin Polyurethane Wearing Course for Asphalt Pavement[J].Materials,2024,17(20):4977-4977.DOI:10.3390/MA17204977.
37. Nicolosi V ,D'Apuzzo M ,Evangelisti A , et al. A new methodological approach for road friction deterioration models development based on energetic road traffic characterization[J].Transportation Engineering,2024,16100251-.DOI:10.1016/J.TRENG.2024.100251.
38. Shenqing X ,Chenchen X ,Li X , et al. Effects of road particulate contaminants on pavement skid resistance[J].Road Materials and Pavement Design,2024,25(4):874-887.DOI:10.1080/14680629.2023.2229921.
39. Kováč M ,Brna M ,Pisca P , et al. The Influence of Road Pavement Materials on Surface Texture and Friction[J].Sustainability,2023,15(16):DOI:10.3390/SU151612195.
40. Claudio L ,Navid G ,Luca C , et al. The evaluation of the effects of hazardous spills by road accidents on the surface performance of an asphalt-wearing course[J].International Journal of Pavement Engineering,2023,24(2):DOI:10.1080/10298436.2022.2138878.
41. Nanxiang Z ,Jiefu B ,Shi D , et al. Testing and evaluation for long-term skid resistance of asphalt pavement composite seal using texture characteristics[J].Construction and Building Materials,2022,356DOI:10.1016/J.CONBUILDMAT.2022.129241.
42. Abdelhakim S F ,Khedoudja S ,Smail H . Laboratory study of the skid resistance of asphalt concrete made of local low performances aggregates by partially replacing sand with olive mill waste[J].Construction and Building Materials,2022,324DOI:10.1016/J.CONBUILDMAT.2022.126657.
43. K. A ,A. K ,Sheik J M M , et al. Prediction modeling of skid resistance and texture depth on flexible pavement for urban roads[J].Materials Today: Proceedings,2022,52(P3):923-929.DOI:10.1016/J.MATPR.2021.10.304.
44. Song L ,Rui X ,Xiaoming D , et al. Effect of chemical composition of calcined bauxite aggregates on mechanical and physical properties for high friction surface course[J].Construction and Building Materials,2021,302DOI:10.1016/J.CONBUILDMAT.2021.124390.