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Quantitative Investigation of Aggregate Skeleton Force Chains of Asphalt Mixtures Based on Computational Granular Mechanics

Autor(en):


Medium: Fachartikel
Sprache(n): Englisch
Veröffentlicht in: Advances in Civil Engineering, , v. 2020
Seite(n): 1-14
DOI: 10.1155/2020/2196503
Abstrakt:

For asphalt mixtures, the difference between strong force chains (SCF) can reflect the skeleton performance. In this paper, six kinds of mineral mixture discrete element model were established. And various SCF evaluation indices of different mineral mixtures were calculated. Results indicate that the short length SCF number proportions of dense-skeleton type mineral mixtures are higher than that of dense-suspended type mineral mixtures under the same nominal maximum aggregate size (NMAS). And the NMAS has a great influence on the SCF length cumulative proportions, and different NMAS can significantly change the stress transfer path for dense-suspended type mixture. Nevertheless, the SCF length cumulative proportions have consistency for dense-skeleton type mixtures. The small SCF alignment coefficient proportions of dense-suspended type mixtures are higher than that of dense-skeleton type mixtures. In particular, under larger NMAS, the difference is more obvious. The SCF that is close to straight line is conducive to transfer loading. Therefore, dense-skeleton type mixture has better rutting resistance. The SCF bears the main loading for mixtures. Mixtures stone matrix asphalt (SMA) has a stronger bearing capacity than that of mixtures AC under the same NMAS. These findings provide insight into the mechanics of skeleton structure.

Copyright: © Guoqiang Liu et al.
Lizenz:

Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden.

  • Über diese
    Datenseite
  • Reference-ID
    10421776
  • Veröffentlicht am:
    12.05.2020
  • Geändert am:
    02.06.2021
 
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