Fabric evolution within shear bands of granular materials and its relation to critical state theory

By Pengcheng Fu, Y. Dafalias. 2011

200 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/9407935e3d07d1c82fc73ca6e2a302e2406fa5fc

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In Civil and Structural Engineering

In Management, Monitoring, Policy and Law

Citations in the corpus

A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (2018, background): “For phenomenological models where history-dependent behavior is encoded in the evolution of internal variables, the influence of micro-structural attributes is often implicitly incorporated (except a few exception such as [91,92]), this could be a difficult task.”
The signature of shear-induced anisotropy in granular media (2013, unknown): “, [13]).”
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning (2018, background): “…literature on fabric tensor and critical state theory to deduce that the porosity and fabric tensor can be used as state variables to improve the forward prediction accuracy of the traction-separation law (cf. Fu and Dafalias [2011], Li and Dafalias [2011], Sun [2013], Wang and Sun [2016]).”
All you need is shape: Predicting shear banding in sand with LS-DEM (2018, background): “inclination angle of σ1 is never observed, so it is thus questionable, in light of recent suggestions of a ‘critical state fabric’ [12], whether or not the specimen actually reached critical state by the end of loading, and certainly provides an impetus for more research to be done on this subject.”
DEM study of fabric features governing undrained post-liquefaction shear deformation of sand (2016, background): “The contact law used in the simulations was described by Fu and Dafalias [11], and the inter-particle contact normal and tangential stiffness parameters (Kn and Ks) used in the simulations are 2100 and 700 GPa/m (the dimension is force/overlap area/unit thickness), respectively.”
3D generation of realistic granular samples based on random fields theory and Fourier shape descriptors (2014, methodology): “[2–9]), polygons, ellipses [10] and polyarcs to super-ellipsoids [11–13], cylinders [14] and polyhedrons or sphero-polyhedrons [15–22].”