^ A Prediction Method for the California Bearing Ratio of Soil-Rock Mixture Based on the Discrete Element Method and CT Scanning | Structurae
0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

A Prediction Method for the California Bearing Ratio of Soil-Rock Mixture Based on the Discrete Element Method and CT Scanning

Auteur(s):







Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2020
Page(s): 1-12
DOI: 10.1155/2020/9794756
Abstrait:

Because of the large amount of gravel with particle sizes over 40 mm in the soil-rock mixture (SRM), it is impossible to determine its California Bearing Ratio (CBR) via the indoor test method, which is a key parameter for designing the backfill in underground mined cavities or the road subgrade constructed with SRM. In this paper, X-ray computed tomography (CT) scanning and 3D reconstruction technology were used to construct the 3D structure of SRM particles with a particle size greater than 5 mm. Based on the vertical vibration test method (VVTM) and PFC3D, the numerical simulation method (NSM-CBR) of SRM was established. The CBR of the SRM with a maximum particle size over 40 mm (SRM-G) was studied by NSM-CBR, and the effects of factors such as maximum particle size, soil content, and large-size particle content (d ≥ 40 mm) on the CBR were investigated via NSM-CBR. Based on the laboratory tests and NSM-CBR, the prediction model and the determining method of CBR of SRM-G were established and verified. The results show that the maximum deviation between the CBR obtained from NSM-CBR and laboratory tests was 7.4%. The CBR of SRM-G decreases linearly with the increase in soil content and increases with the increase in maximum particle size and large-size particle content. The practical project shows that the maximum deviation between the predictive and measured values of the CBR of SRM-G was less than 1.5%, indicating that the prediction model and the method established in this paper have high reliability.

Copyright: © Xiaoping Ji et al.
License:

Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original.

  • Informations
    sur cette fiche
  • Reference-ID
    10444065
  • Publié(e) le:
    05.10.2020
  • Modifié(e) le:
    02.06.2021