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Published VersionCC BY V4.0, Open Access
Journal article
XGBoost-based intelligent framework for asphalt pavement skid resistance assessment under different variables
Smart Construction, Vol.2(4), pp.1-26
2025
Abstract
To address the constraints of conventional evaluation methods for asphalt pavement anti-skidding performance, such as insufficient generalization across gradation scenarios and difficulties in collaborative quantification of multi-scale features, this study proposes a composite parameter-based asphalt pavement anti-skidding performance evaluation model using the XGBoost algorithm. The study selected three typical types of asphalt pavements, AC13, AC20, and SMA13, which are widely used in paving practice. The texture feature information of these test road sections was extracted and developed a full-scale texture characterization system by integrating the statistical features (energy and entropy) and fractal features (fractal dimension and multifractal spectrum width Δα). The feature segmentation counting method was employed to quantify the contribution mechanism of different texture features to skid resistance, and the application effectiveness of the model in cross-gradation scenarios was systematically evaluated. The results showed that XGBoost-based composite parameter asphalt pavement anti-skidding performance evaluation model achieved test set accuracy of R² > 0.9 and RMSE < 0.06, confirming its capability to accurately and effectively assess asphalt pavement anti-skidding performance. Model performance was influenced by pavement gradation type: AC13 fine-graded mixtures yielded optimal prediction, SMA13 mixtures ranked second, while AC20 coarse-graded mixtures exhibited relatively weaker generalization but still met requirements. Feature importance analysis identified fractal dimension as the core predictive factor of the model, with different gradation types significantly affecting the effectiveness of texture feature characterization. The research provides significant theoretical and technical support for achieving intelligent detection and gradation-adaptive evaluation of asphalt pavement anti-skidding performance.
Details
- Title
- XGBoost-based intelligent framework for asphalt pavement skid resistance assessment under different variables
- Authors
- Yu Zhao (Corresponding Author) - Universiti Sains MalaysiaMohd Rosli Mohd Hasan - Universiti Sains MalaysiaKe Zhang - Fuyang Normal UniversityLingyun You - Huazhong University of Science and TechnologyAli Jamshidi - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringJunxian Jiang - Universiti Sains Malaysia
- Publication details
- Smart Construction, Vol.2(4), pp.1-26
- Publisher
- ELS Publishing
- Date published
- 2025
- DOI
- 10.55092/sc20250029
- ISSN
- 2960-2033; 2960-2025
- Copyright note
- Copyright2025 by the authors. Published by ELSP. This work is licensed under Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
- Grant note
- This work is supported by Universiti Sains Malaysia via the Bridging Grant (R501-LR-RND003-0000001344-0000), Key project of Natural Science Research of Anhui Provincial Department of Education (2023AH052853). Research support from the Wuhan Key Research and Development Program (2025050102030009) is also appreciated.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991192343502621
- Output Type
- Journal article
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