Skip to main content

From computed tomography to mechanics of granular materials via level set bridge

Abstract

This paper details the ‘level set bridge’: a single platform for the characterization of various aspects of granular micro-mechanics, including grain morphology, grain kinematics, and inter-granular contact. This platform is studied and verified for accuracy using synthetic examples, in particular, its robustness with respect to the variables of image resolution and noise. The level set bridge is then applied to analysis of XRCT images of real 3D triaxial experiments of two types of granular materials. Contact statistics and kinematics are reported inside and outside of the failure band of one, and kinematics inside a failure band are reported in the other, from preload to critical state.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Notes

  1. 1.

    ... and also capillary effects though in our case, the specimen were tested dry and not affected by capillary effects

References

  1. 1.

    Andò E, Hall S, Viggiani G, Desrues J, Bésuelle P (2012) Grain-scale experimental investigation of localised deformation in sand: a discrete particle tracking approach. Acta Geotech 7:1–13. doi:10.1007/s11440-011-0151-6

    Article  Google Scholar 

  2. 2.

    Andò E, Hall S, Viggiani G, Desrues J, Bésuelle P (2012) Experimental micromechanics: grain-scale observation of sand deformation. Géotech Lett 2(3):107–112

    Article  Google Scholar 

  3. 3.

    Andrade J, Tu X (2009) Multiscale framework for behavior prediction in granular media. Mech Mater 41(6):652–669

    Article  Google Scholar 

  4. 4.

    Andrade J, Avila C, Hall S, Lenoir N, Viggiani G (2010) Multiscale modeling and characterization of granular matter: from grain kinematics to continuum mechanics. J Mech Phys Solids 59:237–250

    Article  MATH  Google Scholar 

  5. 5.

    Andrade JE, Vlahinić I, Lim K-W, Jerves A (2012) Multiscale tomography-to-simulation framework for granular matter: the road ahead. Géotech Lett 2:135–139. doi:10.1680/geolett.12.00023

    Article  Google Scholar 

  6. 6.

    Bagi K (1996) Stress and strain in granular assemblies. Mech Mater 22:165–177

    Article  Google Scholar 

  7. 7.

    Behringer R, Bi D, Chakraborty B, Clark A, Dijksman J, Ren J, Zhang J (2014) Statistical properties of granular materials near jamming. J Stat Mech Theory Exp 2014(6):P06004

    Article  Google Scholar 

  8. 8.

    Calvetti F, Combe G, Lanier J (1997) Experimental micromechanical analysis of 2d granular material: relation between structure evolution and loading path. Mech Cohes Frict Mater 2:121–163

    Article  Google Scholar 

  9. 9.

    Caselles V, Catté F, Coll T, Dibos F (1993) A geometric model for active contours in image processing. Numer Math 66:1–31. doi:10.1007/bf01385685

    MathSciNet  Article  MATH  Google Scholar 

  10. 10.

    Jenkinson M, Bannister P, Brady M, Smith S (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17:825–841

    Article  Google Scholar 

  11. 11.

    Kichenassamy S, Kumar A, Olver P, Tannenbaum A, Yezzi A (1996) Conformal curvature flows: from phase transitions to active vision. Arch Ration Mech Anal 134:275–301

    MathSciNet  Article  MATH  Google Scholar 

  12. 12.

    Li X, Dafalias Y (2012) Anisotropic critical state theory: role of fabric. J Eng Mech 138:263–275. doi:10.1061/(ASCE)EM.1943-7889.0000324

    Article  Google Scholar 

  13. 13.

    Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: a new variational formulation. In: Proceedings of the IEEE, computer vision and pattern recognition, pp 430–436

  14. 14.

    Li C, Xu C, Gui C, Fox MD (2010) Distance regularized level set evolution and its application to image segmentation. In: Proceedings of the IEEE, image processing, pp 3243–3254

  15. 15.

    Oda M (1972) Initial fabrics and their relations to mechanical properties of granular material. Soils Found 12:17–36

    Article  Google Scholar 

  16. 16.

    Oda M, Iwashita K (1999) Mechanics granular materials, an introduction, 1st edn. Balkema, Rotterdam

    Google Scholar 

  17. 17.

    Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 79:12–49

    MathSciNet  Article  MATH  Google Scholar 

  18. 18.

    Rechemacher A, Finno R (2004) Digital image correlation to evaluate shear banding in dilative sands. Geotech Test J 27:13–22

    Google Scholar 

  19. 19.

    Santamarina JC (2003) Soil behavior at the microscale: particle forces. In: Proceedings of a ASCE, soil behavior and soft ground construction, pp 25–56. doi:10.1061/40659(2003)2

  20. 20.

    Schofield A, Wroth P (1968) Critical state soil mechanics. McGraw-Hill, New York

    Google Scholar 

  21. 21.

    Vlahinić I, Andò E, Viggiani G, Andrade JE (2013) Towards a more accurate characterization of granular media: extracting quantitative descriptors from tomographic images. Granul Matter 16:9–21

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to José E. Andrade.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Vlahinić, I., Kawamoto, R., Andò, E. et al. From computed tomography to mechanics of granular materials via level set bridge. Acta Geotech. 12, 85–95 (2017). https://doi.org/10.1007/s11440-016-0491-3

Download citation

Keywords

  • Fabric
  • Granular
  • Level sets
  • Multi scale
  • Tomography