4.7 Article

Mathematical Modeling of Pavement Gyratory Compaction: A Perspective on Granular-Fluid Assemblies

Journal

MATHEMATICS
Volume 11, Issue 9, Pages -

Publisher

MDPI
DOI: 10.3390/math11092096

Keywords

mathematical modeling; granular physics; asphalt mixtures; compaction; discrete element method

Categories

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This paper proposes a simple mathematical model to predict the compaction of asphalt mixtures, considering the viscous properties of the asphalt binder and grain size distributions of the aggregates. The model is calibrated and validated with Superpave gyratory compaction tests, showing good predictions for the experiments and suggesting its potential for enhancing the design of asphalt mixtures.
The compaction of asphalt mixture is crucial to the performance of the pavement. However, the mix design (i.e., porosity, aggregate size distribution, binder content), which is based on compaction results, remains largely empirical. It is difficult to relate the aggregate size distribution and the asphalt binder properties to the compaction curve in both the field and laboratory compaction of asphalt mixtures. In this paper, the author proposes a simple mathematical model from the perspective of granular physics to predict the compaction of asphalt mixtures. In this model, the compaction process is divided into two mechanisms: (i) viscoplastic deformation of an ordered granular-fluid assembly, and (ii) the transition from an ordered system to a disordered system due to particle rearrangement. This model could take into account both the viscous properties of the asphalt binder and the grain size distributions of the aggregates, where the viscous deformation is calculated with a proposed governing equation and the particle rearrangement effect is solved using simple DEM simulations. This model is calibrated based on the Superpave gyratory compaction tests in the pavement lab, and the R-squares of model predictions are all above 0.95. The model results are compared with experimental data to show that it can provide good predictions for the experiments, suggesting its potential for enhancing the design of asphalt mixtures.

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