Characterization of Force Distribution and Force Chain Topology in Asphalt Mixtures Using the Discrete Element Method

The force chain network within asphalt mixtures serves as the primary load-bearing structure to resist external forces. The objective of this study is to quantitatively characterize the contact force distribution and force chain topology structure. The discrete element method (DEM) was employed to construct simulation models for two stone matrix asphalt (SMA) and two open-graded friction course (OGFC) mixtures. Load distribution characteristics, including average contact force, load bearing contribution and contact force angle, and force chain topological network parameters, clustering coefficient, edge betweenness and average path length, were analyzed to elucidate the load transfer mechanisms. The findings of the present study demonstrate that the average contact force between aggregate-aggregate contact types in specific particle sizes significantly exceeds the average contact force of the same particle size aggregates. For SMA16 and OGFC16 asphalt mixtures, the load-bearing contribution of aggregates initially increases and then decreases with decreasing particle size, peaking at 13.2 mm. SMA13 and OGFC13 mixtures demonstrate a consistent decline in load bearing contribution with decreasing aggregate size. The analysis of the force chain network topology of the asphalt mixture reveals that SMA mixtures exhibited higher average clustering coefficients in force chain topological features in comparison to OGFC mixtures. It indicates that SMA gradations have superior skeletal load-bearing structures. While the maximum nominal aggregate size minimally influences the average path length with a relative change rate of 3%, the gradation type exerts a more substantial impact, exhibiting a relative change rate of 7% to 9%. These findings confirm that SMA mixtures have more stable load-bearing structures than OGFC mixtures. The proposed topological parameters effectively capture structural distinctions in force chain networks, offering insights for optimizing gradation design and enhancing mechanical performance.