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Asphalt Durability and Self-healing Modelling with Discrete Particles Approach

  • V. Magnanimo
  • H. L. ter Huerne
  • S. Luding
Conference paper
Part of the RILEM Bookseries book series (RILEM, volume 4)

Abstract

Asphalt is an important road paving material, where besides an acceptable price, durability, surface conditions (like roughening and evenness), age-, weather- and traffic-induced failures and degradation are relevant aspects. In the professional road engineering branch empirical models are used to describe the mechanical behaviour of the material and to address large-scale problems for road distress phenomena like rutting, ravelling, cracking and roughness. The mesoscopic granular nature of asphalt and the mechanics of the bitumen between the particles are only partly involved in this kind of approach. The discrete particle method is a modern tool that allows for arbitrary (self-)organization of the asphalt meso-structure and for rearrangements due to compaction/cyclic loading. This is of utmost importance for asphalt during the construction phase and the usage period, in forecasting the relevant distress phenomena and understand their origin on the grain-, contact-, or molecular scales. Contact models that involve visco-elasticity, plasticity, friction and roughness are state-of-the art in fields like particle technology and can now be modified for asphalt and validated experimentally on small samples. The ultimate goal is then to derive micro- and meso-based constitutive models that can be applied to modellingbehaviour of asphalt pavements on the larger scales. Using the new contact models, damage and crack formation in asphalt and their propagation can be modeled. Furthermore, the possibility to trigger self-healing in the material can be investigated from a micromechanical point of view.

Keywords

Contact Force Discrete Element Method Contact Model Asphalt Mixture Contact Stiffness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© RILEM 2012 2012

Authors and Affiliations

  1. 1.Tire-Road Consortium, CTWUniversity of TwenteEnschedeNetherlands

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