Effects of filling degree on skid resistance in fluid mastic asphalt mixtures
Source abstract
Abstract
Macrotexture is critical for pavement skid resistance in wet conditions, yet conventional mixtures face a trade-off between durability and texture depth. This study proposes Fluid Mastic Asphalt (FMA), a skeleton-superdense mixture in which flowable asphalt mastic fills the aggregate skeleton. By adjusting the filling degree, macrotexture can be controlled while maintaining high durability. FMA mixtures with varying filling degrees and asphalt–aggregate ratios were evaluated through abrasion and rutting tests. Results recommend an optimal filling degree of 94%–96%. Compared with AC and SMA, FMA exhibits superior skid durability, with BPN degradation rates reduced by 34.5% and 19.5%, respectively. For MTD, FMA retains values above 1.4 mm after abrasion, reaching 4 and 1.65 times those of AC and SMA. An XGBoost model was developed to analyze FMA skid resistance degradation, with SHAP values quantifying the contributions of input factors.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Asphalt Pavement Performance Evaluation · Infrastructure Maintenance and Monitoring · Materials Engineering and Processing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Guangzhou University
Xinyue (Guangzhou) Materials Technology Research Institute Co., Ltd., Guangzhou, People’s Republic of China
Ltd
Funding
National Natural Science Foundation of China
References (48)
Show source-provided references
1. 10.1016/j.conbuildmat.2018.05.15310.1016/j.conbuildmat.2018.05.153 ↗
2. 10.1016/j.aap.2021.10654510.1016/j.aap.2021.106545 ↗
3. 10.3390/buildings1112062310.3390/buildings11120623 ↗
4. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, San Francisco, CA, USA (pp. 785–794). https://doi.org/10.1145/2939672.2939785
5. 10.1016/j.cscm.2023.e0249010.1016/j.cscm.2023.e02490 ↗
6. 10.1016/j.conbuildmat.2022.12642710.1016/j.conbuildmat.2022.126427 ↗
7. 10.1016/j.conbuildmat.2021.12296610.1016/j.conbuildmat.2021.122966 ↗
8. 10.1214/aos/101320345110.1214/aos/1013203451 ↗
9. 10.5254/1.353570310.5254/1.3535703 ↗
10. 10.1016/j.conbuildmat.2024.13816710.1016/j.conbuildmat.2024.138167 ↗
11. 10.1016/j.cscm.2022.e0170610.1016/j.cscm.2022.e01706 ↗
12. 10.1016/j.wear.2013.03.02210.1016/j.wear.2013.03.022 ↗
13. 10.1016/j.conbuildmat.2016.04.00210.1016/j.conbuildmat.2016.04.002 ↗
14. 10.1016/j.conbuildmat.2023.13191510.1016/j.conbuildmat.2023.131915 ↗
15. 10.1016/j.resconrec.2020.10479610.1016/j.resconrec.2020.104796 ↗
16. 10.1016/j.conbuildmat.2025.14152210.1016/j.conbuildmat.2025.141522 ↗
17. Ma, B., Lv, C., Liu, Y., Zheng, M., Yang, Y., & Ji, X. (2018). Estimation of road adhesion coefficient based on tire aligning torque distribution. Journal of Dynamic Systems,: Measurement, and Control, 140(5), 051010.
18. 10.1016/j.aap.2009.05.00410.1016/j.aap.2009.05.004 ↗
19. 10.1080/10298436.2025.247964210.1080/10298436.2025.2479642 ↗
20. 10.1680/tran.2009.162.1.1910.1680/tran.2009.162.1.19 ↗
21. 10.3390/ma1503084610.3390/ma15030846 ↗
22. 10.1016/j.conbuildmat.2019.11699110.1016/j.conbuildmat.2019.116991 ↗
23. Putatunda, S., & Rama, K. (2018). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. In Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, Shanghai, China (pp. 6–10). https://doi.org/10.1145/3297067.3297080
24. 10.1016/j.conbuildmat.2016.03.21010.1016/j.conbuildmat.2016.03.210 ↗
25. 10.1016/j.conbuildmat.2024.13872810.1016/j.conbuildmat.2024.138728 ↗
26. 10.1007/s10115-013-0679-x10.1007/s10115-013-0679-x ↗
27. Sun, H. (2011). Research on the attenuation characteristics of anti-sliding performance of asphalt pavement. Highway, 56(7), 73–78.
28. 10.1016/j.sbspro.2012.09.96410.1016/j.sbspro.2012.09.964 ↗
29. 10.1016/j.wear.2014.08.01310.1016/j.wear.2014.08.013 ↗
30. 10.1016/j.wear.2013.09.01310.1016/j.wear.2013.09.013 ↗
31. 10.1016/j.jclepro.2024.14277910.1016/j.jclepro.2024.142779 ↗
32. 10.1016/j.conbuildmat.2019.11775910.1016/j.conbuildmat.2019.117759 ↗
33. 10.19721/j.cnki.1001-7372.2025.02.00810.19721/j.cnki.1001-7372.2025.02.008 ↗
34. Wu, K., Wu, C., Nie, G., Cai, X., Xu, X., Zheng, Y., Huang, K., Wang, Z., & Huang, J. (2024). Research on theory and design method of fluid mastic asphalt mixture (FMA). China Journal of Highway and Transport, 38(2), 1–17. https://link.cnki.net/urlid/61.1313.U.20240604.1542.002
35. 10.1016/j.measurement.2021.10930310.1016/j.measurement.2021.109303 ↗
36. 10.1016/j.conbuildmat.2022.12896110.1016/j.conbuildmat.2022.128961 ↗
37. 10.1016/j.conbuildmat.2023.13264210.1016/j.conbuildmat.2023.132642 ↗
38. 10.1016/j.conbuildmat.2022.13015010.1016/j.conbuildmat.2022.130150 ↗
39. 10.1016/j.conbuildmat.2024.13738610.1016/j.conbuildmat.2024.137386 ↗
40. 10.1016/j.measurement.2021.11063810.1016/j.measurement.2021.110638 ↗
41. 10.1080/22243682.2013.86877710.1080/22243682.2013.868777 ↗
42. 10.1016/j.conbuildmat.2023.13459710.1016/j.conbuildmat.2023.134597 ↗
43. 10.1016/j.cscm.2025.e0484510.1016/j.cscm.2025.e04845 ↗
44. 10.1080/14680629.2025.258456510.1080/14680629.2025.2584565 ↗
45. 10.1016/j.conbuildmat.2024.13750810.1016/j.conbuildmat.2024.137508 ↗
46. 10.1142/S0218625X1450015210.1142/S0218625X14500152 ↗
47. Zhu, L., Huang, X., Luo, H., & Ma, T. (2023). Point cloud coordinate correction methods for road surface texture index calculation. Journal of Southeast University(Natural Science Edition), 53(03), 526–536.
48. 10.1016/j.conbuildmat.2021.12273010.1016/j.conbuildmat.2021.122730 ↗