Fabric Evolution and Its Effect on Strain Localization in Sand

  • Zhiwei GaoEmail author
  • Jidong Zhao
Conference paper
Part of the Springer Series in Geomechanics and Geoengineering book series (SSGG)


Fabric anisotropy affects importantly the overall behaviour of sand including its strength and deformation characteristics. While both experimental and numerical evidence indicates that soil fabric evolves steadily with the applied stress/strain, how evolving fabric influences the initiation and development of shear band in sand remains an intriguing question to be fully addressed. In this paper, we present a numerical study on strain localization in sand, highlighting the special role played by soil fabric and its evolution. In particular, a critical state sand plasticity model accounting for the effect of fabric and its evolution is used in the finite element analysis of plane strain compression tests. It is found that the initiation of shear band is controlled by the initial fabric, while the development of shear band is governed by two competing physical mechanisms, namely, the structural constraint and the evolution of fabric. The evolution of fabric generally makes the sand response more coaxial with the applied load, while the structural constraint induced by the sample ends leads to more inhomogeneous deformation within the sand sample when the initial fabric is non-coaxial with the applied stress. In the case of smooth boundary condition, structural constraint dominates over the fabric evolution and leads to the formation of a single shear band. When the boundary condition is rough, the structural constraint may play a comparable role with fabric evolution, which leads to symmetric cross-shape shear bands. If the fabric is prohibited from evolving in the latter case, a cross-shape shear band pattern is found with the one initiated first by the structural constraint dominant over the second one.


Shear Band Strain Localization Structural Constraint Fabric Evolution Plane Strain Compression 
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.



The study was financially supported by RGC/GRF 622910 and DAG08/09.EG04.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of EngineeringUniversity of GlasgowGlasgowUK
  2. 2.Department of Civil and Environmental EngineeringHong Kong University of Science and TechnologyKowloonHong Kong

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