Compaction process tracking for asphalt mixture using discrete element method

摘要

Although the same compaction degree is achieved in practice, asphalt mixture samples prepared by different compaction methods often have different mechanical properties. In this paper, the air void content (AV) and distribution of aggregates and asphalt mortar in the process of asphalt mixture compaction are traced to capture the meso structural change characteristics of asphalt mixture during compaction. Using the discrete element method (DEM), a numerical technique is developed to simulate the laboratory compaction by taking into account the critical aggregate size and boundary effect. First, the critical aggregate size (CAS) is determined by the 2D and 3D binary particle assembly. Second, DEM simulations of both the Marshall impact compaction (MIC) and static compaction (SC) methods are conducted by the mass-wall and servo boundary, respectively. Third, the applicability of the 2D model is demonstrated through laboratory tests and numerical calculations. Finally, the distribution of aggregates and asphalt mortar are displayed and analyzed. The results show that the variation of CAS presents linear growth approximately with the increase of coarse particle size, less affected by the boundary. The primary control sieve (PCS) is applicable to separate the coarse and fine particles in the 3D assembly, but the CAS is around 0.195 for the 2D assembly, which is obviously less than the PCS. It is verified by two compaction methods and two mixture gradations that the DEM simulation is an effective way to evaluating the compacting effects of the compaction process. By double-sided hammering, coarse aggregates are moved to accumulate more closely, thus the coordination number at the bottom increases. Although a dense specimen can be achieved by compaction method, the size distribution of particles is still uneven in horizontal direction, since the position of large size particles (>16 mm) is difficult to be changed in the compaction process. (C) 2019 Elsevier Ltd. All rights reserved.