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Abstract:
Asphalt mixture is a typical granular material, and its macroscopic properties are closely related to the interaction behavior of aggregate particles at the micro scales. Discrete element method (DEM), as an important numerical simulation method in granular mechanics, plays an increasingly important role in analyzing the aggregate packing characteristics, skeleton features, predicting mechanical and pavement performance of asphalt mixtures. However, the complexity of the composition of asphalt mixtures, including the particle size, shape, spatial distribution of aggregate particles, and the viscoelasticity of asphalt binders sensitive to temperature, load, and time, increases the difficulty of constructing DEM models of asphalt mixtures, selecting contact constitutive models, and determining parameters, which directly affects the accuracy and efficiency of DEM simulations. In this review, the generation methods of idealized models, image models, and user-defined models currently used to generate DEM models of asphalt mixtures were summarized in detail. Conventional contact constitutive models, including elastic and viscoelastic contact stiffness constitutive models, as well as fracture models including bonding models and cohesive softening models, were discussed. Moreover, special contact constitutive models, such as damage, self-healing, and two-stage contact models considering compaction evolution, were also summarized and discussed. Then, the contact constitutive model parameters were classified and related determination methods were summarized, and the latest application directions of DEM in current asphalt mixture research were classified. On this basis, relevant discussions were carried out, and the challenges and future development prospects faced by DEM in asphalt mixture research were proposed.
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CONSTRUCTION AND BUILDING MATERIALS
ISSN: 0950-0618
Year: 2024
Volume: 414
7 . 4 0 0
JCR@2023
Cited Count:
WoS CC Cited Count: 3
SCOPUS Cited Count: 3
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 4
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