SIMPLE PLASTICITY SAND MODEL ACCOUNTING FOR FABRIC CHANGE EFFECTS

By Y. Dafalias, M. Manzari. 2004

998 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9dc396233bfc42843b7d85c31adfb364de7892a3

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In Civil and Structural Engineering

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Citations in the corpus

A bounding surface plasticity model for cyclic loading of granular soils (2005, background): “Previous attempts to describe the shape of bounding surface have included the use of a straight line [15, 16, 25], a single ellipse [11], or a combination of segments of a hyperbola and an ellipse [8, 10, 12–14].”
Sand Plasticity Model Accounting for Inherent Fabric Anisotropy (2004, methodology): “Although in Li and Dafalias (2000) and Dafalias and Manzari (2004) the same sand was used, the different methods of preparation (wet tamping versus dry deposition here) by different experimentalists, rendered it necessary to change some of the constants from the foregoing references.”
A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning (2018, background): “…nomenological models where history-dependent behavior is encoded in the evolution of internal variables, the inﬂuence of micro-structural attributes are often implicitly incorporated (except a few exception such as [Dafalias and Manzari, 2004, Fu and Dafalias, 2011]), this could be a difﬁcult task.”
ISA model: A constitutive model for soils with yield surface in the intergranular strain space (2015, background): “Although the two simulations (with and without small cycles) are not identical during the large cycles, the response is still very similar and lacks from overshooting as shown by many other models, for example, [25, 45].”
SANISAND‐FN: An evolving fabric‐based sand model accounting for stress principal axes rotation (2018, neutral): “k̇ = ⟨L⟩ck [D(1 − |Nα|)ks − |D| k] , (24)”
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning (2018, methodology): “The evolution of fabric tensor has been incorporated into the hardening law and the plastic ﬂow rule to capture the induced anisotropy and critical state of sand [Mehrabadi et al., 1982, Dafalias and Manzari, 2004, Dafalias et al., 2004].”
Constitutive modeling of sand: Formulation of a new plasticity approach (2016, methodology): “fabric-dilatancy tensor), as indicated by Dafalias and Manzari [9].”
International Benchmark on Numerical Simulations for 1D, Nonlinear Site Response (PRENOLIN): Verification Phase Based on Canonical Cases (2016, background): “(2008) and Stewart and Kwok (2009). In linear/equivalent linear/NL site response analyses, two cases can be distinguished: In order to avoid any ambiguity, we will systematically use the expression reference motion, which should be understood as detailed above for the elastic and rigid base conditio”