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Modeling of asphalt mixture based on aggregate morphology distribution using the contact dynamics method
Haitao Ge, Juan Carlos Quezada Guajardo, Cyrille Chazallon, Vincent Le Houerou

Modeling of asphalt mixture based on aggregate morphology distribution using the contact dynamics method

Science & Mathematics Published 2022 Verification Pending

Overview & Synopsis

Asphalt mixture performance depends highly on the particle shape. Discrete element method (DEM) has become a highly prevalent approach for testing and reconfiguring particles in asphalt mixtures in recent decades. Nevertheless, most simulation studies simplify particle shapes and ignore irregular morphology features. In this study, morphological characteristics for a large number of particles were analyzed statistically using three-dimensional laser scanning technology. Combining morphology statistics and particle size distribution, numerical samples of asphalt mixtures were generated using Voronoi tessellations. Finally, the model reliability is validated by conducting experiments and simulations for the complex modulus test based on the Contact Dynamics (CD) method. Using this procedure, the aggregate morphology and size distribution can be incorporated in numerical simulations of asphalt mixtures. The modeling method provides a basis for the high-precision simulation of asphalt mixes comprising polyhedral particles, which can be seen as an enriching improvement of the classical procedures.

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Document Information

Document Title Modeling of asphalt mixture based on aggregate morphology distribution using the contact dynamics method
Author / Organization Haitao Ge, Juan Carlos Quezada Guajardo, Cyrille Chazallon, Vincent Le Houerou
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2022
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-189360
Original Source Link Visit Source Page on hal.science »