Investigation of rutting performance of asphalt mixture and pavements based on mesostructured finite element simulation

ABSTRACT

The pursuit of robust research methodologies to forecast the rutting sensitivity of asphalt mixtures and pavements remains a challenge for the industry. An in-depth examination of the rutting behaviour of asphalt mixtures and pavements under varying influencing factors was conducted via indoor experiments and finite element simulations to investigate the evolution of rutting. This study leverages high-temperature test data derived from multiple stress creep recovery tests of asphalt mortar, complemented by pertinent theories (Burgers model) as the constitutive model. At the same time, a contact law is defined based on the thickness of the asphalt binder layer and increases the accuracy of the rutting simulation. Generate random aggregate asphalt mixture and surface layer models using Python scripts in finite element software. The results show that the simulation results are consistent with the experimental results, and the overall measured amplitude on site is lower than the numerical simulation value. Furthermore, the model parameters will be applied to a microscopic pavement model to study the mechanical response and rutting development of pavement structures under thermal-mechanical coupling. The meso numerical simulation method developed in this study serves as an effective tool for unraveling the high-temperature failure mechanism of asphalt mixtures.

KEYWORDS:

• Rutting behaviour
• Asphalt mixtures and pavements
• Finite element simulation
• Random aggregate model
• Thermal-mechanical coupling

Acknowledgments

The author would like to express gratitude to the National Center for Materials Service Safety (NCMS) of China, the Liaoning University of Technology Doctoral Research Initiation Fund Project, Grant no (XB2023031), and the Natural Science Foundation of Liaoning Province, Grant no (2022-MS-380) for their funding of this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Liaoning University of Technology Doctoral Research Initiation Fund Project [grant number: XB2023031]; This project was funded by one of the authors (Duo Meng). The specific fund project is the Natural Science Foundation of Liaoning Province [grant number: 2022-MS-380].