Construction of Data-Driven Explicit Rutting Evolution Model for Asphalt Pavement

Bo Kou; Jinde Cao; Zhanzhong Shi; Wei Huang; Tao Ma; Yi Gong

doi:10.53941/ams.2025.100005

Abstract

Rutting is a non-stationary complex process, and its research has been the focus and difficulty in the world and industry for a long time. Effectively revealing the evolution law of asphalt rutting will play a positive role in optimizing road design, preventive maintenance and resource saving, which will help promote the development of transportation infrastructure to a more environmentally friendly and low-carbon direction. How to capture the temporal and spatial evolution law of rutting and express it through an explicit model will greatly improve the research level of permanent damage of asphalt pavement, which is the research difficulty of the industry and also the focus of this paper. In this paper, RIOHTrack was used to measure the database of more than 100 million loads of full-scale ring road. Time series method was used to overlay the original Kou&Cao model framework proposed by our team, and an explicit model framework was proposed here, which can well present the linear and nonlinear process of rutting evolution in time and space. The model framework perfectly fits the process of rutting, the fitting accuracy reaches 0.993 when tested on seven kinds of pavement structure data. The rolling prediction accuracy reaches 0.972. The proposed model framework effectively improves the interpretability of the asphalt rutting evolution model, greatly improves the accuracy of the asphalt rutting evolution model, and has excellent generalization ability, which is closer to revealing the real situation of the temporal and spatial evolution of asphalt pavement, and plays an important role in the study of the long life of asphalt pavement.

References

1.
Hafeez, I.; Kamal, M.A.; Mirza, M.W.; et al. Laboratory fatigue performance evaluation of different field laid asphalt mixtures. Constr. Build. Mater. 2013, 44, 792–797.
2.
Javilla, B.; Fang, H.; Mo, L.; et al. Test evaluation of rutting performance indicators of asphalt mixtures. Constr. Build. Mater. 2017, 155, 1215–1223.
3.
Coleri, E.; Harvey, J.T.; Yang, K.; et al. A micromechanical approach to investigate asphalt concrete rutting mechanisms. Constr. Build. Mater. 2012, 30, 36–49.
4.
Barksdale, R.D. Laboratory evaluation of rutting in basecourse materials. In Proceedings of the Third International Conference on the Structural Design of Asphalt Pavements, London, UK, 11–15 September 1972.
5.
Eisenmann, J.; Hilmer, A. Influence of wheel load and inflation pressure on the rutting effect at asphalt-pavements- experiments and theoretical investigations. Annu. Rev. Political Sci. 1987, 14, 149–175.
6.
Peng, M.J. Nonlinear Theory and Methods for Rutting Analysis of Asphalt Pavement; Tongji University: Shanghai, China, 2005.
7.
Du, Plessis, L.; Coetzee, N.F.; Hoover, T.P.; et al. Three decades of development and achievements: the heavy vehicle simulator in accelerated pavement testing. In Pavement Mechanics and Performance; ASCE: Reston, VA, USA, 2006.
8.
Metcalf, J.B. A history of full-scale Accelerated Pavement Testing facilities to 1962. Road Transp. Res. 2014, 23, 25–40.
9.
Banan, M.R.; Hjelmstad, K.D. Neural networks and AASHO road test. J. Transp. Eng. 1996, 122, 358–366.
10.
Tsai, B.W.; Coleri, E.; Harvey, J.T.; et al. Evaluation of AASHTO T324 Hamburg-Wheel track device test. Constr. Build. Mater. 2016, 114, 248–260.
11.
Martin, A.E.; Walubita, L.F.; Hugo, F.; et al. Pavement Response and Rutting for Full-Scale and Scaled APT. J. Transp. Eng. 2003, 129, 451–461.
12.
Kou, B.; Cao, J.; Huang, W.; et al. Rutting prediction model of asphalt pavement based on riohtrack full-scale ring road. Measurement 2025, 242, 115915.
13.
Contreras, J.; Espinola, R.; Nogales, F.J.; et al. Arima models to predict next-day electricity prices. IEEE Trans. Power Syst. 2003, 18, 1014–1020.
14.
Prez, I.; Gallego, J. Rutting prediction of a granular material for base layers of low-traffic roads. Constr. Build. Mater. 2010, 24, 340–345.
15.
Wang, X.D.; Zhang, L.; Zhou, X.Y.; et al. Research progress of RIOHTRACK in china. In Accelerated Pavement Testing to Transport Infrastructure Innovation; Springer: Berlin/Heidelberg, Germany, 2020.
16.
Priest, A.L.; Timm, D.H. A full-scale pavement structural study for mechanistic-empirical pavement design (with discussion). J. Assoc. Asph. Paving Technol. 2005, 74, 519–556.
17.
Kou, B.; Cao, J.; Huang, W.; et al. The rutting model of semi-rigid asphalt pavement based on RIOHTRACK full-scale track. Math. Biosci. Eng. 2023, 20, 8124–8145.
18.
Guo, Y.; Wang, X.; Wang, S.; Hu, K.; et al. Identification method of coal and coal gangue based on dielectric characteristics. IEEE Access 2021, 9, 9845–9854.
19.
Kou, B.; Cao, J.; Liu, D.; et al. Generalization ability of rutting prediction model for asphalt pavement based on RIOHTrack full-scale track. In Proceedings of the 2024 9th International Conference on Information and Education Innovations, Verbania, Italy, 12–14 April 2024.
20.
Khan, I.; Hou, F.; Le H.P. The impact of natural resources, energy consumption, and population growth on environmental quality: fresh evidence from the united states of America. Sci. Total. Environ. 2020, 754, 142222.
21.
Quintanilla, R.; Rajagopal, K.R. On burgers fluids. Math. Methods Appl. Sci. 2006, 29, 2133–2147.
22.
Zhuang, C.; Chen, K.; Ye, Y.; et al. Experimental and computational study on the anti-rutting behavior of an asphalt mixture based on an advanced mts test. SSRN Electron. J. 2023, 18, e02176.
23.
Liu, J.; Cheng, C.; Zheng, C.; et al. Rutting prediction using deep learning for time series modeling and k-means clustering based on RIOHTrack data. Constr. Build. Mater. 2023, 385, 131515.

Scilight Press

Author Information

Abstract

Keywords

References

About Scilight

Journals

Publishing Policies

Contact Us