Generalized strain probing of constitutive models

Advanced material constitutive models are used to describe complex soil behaviour. These models are often used in the solution of boundary value problems under general loading conditions. Users and developers of constitutive models need to methodically investigate the represented soil response under a wide range of loading conditions. This paper presents a systematic procedure for probing constitutive models. A general incremental strain probe, 6D hyperspherical strain probe (HSP), is introduced to examine rate-independent model response under all possible strain loading conditions. Two special cases of HSP, the true triaxial strain probe (TTSP) and the plane-strain strain probe (PSSP), are used to generate 3-D objects that represent model stress response to probing. The TTSP, PSSP and general HSP procedures are demonstrated using elasto-plastic models. The objects resulting from the probing procedure readily highlight important model characteristics including anisotropy, yielding, hardening, softening and failure. The PSSP procedure is applied to a Neural Network (NN) based constitutive model. It shows that this probing is especially useful in understanding NN constitutive models, which do not contain explicit functions for yield surface, hardening, or anisotropy. Copyright © 2004 John Wiley & Sons, Ltd.

[1]  John Burland,et al.  Stress-Probe Experiments on Saturated Normally Consolidated Clay , 1970 .

[2]  Jamshid Ghaboussi,et al.  New nested adaptive neural networks (NANN) for constitutive modeling , 1998 .

[3]  Andrew J. Whittle,et al.  Integration of the modified Cam-Clay model in non-linear finite element analysis , 1992 .

[4]  Youssef M A Hashash,et al.  Visual framework for development and use of constitutive models , 2002 .

[5]  Youssef M A Hashash,et al.  Numerical implementation of a neural network based material model in finite element analysis , 2004 .

[6]  Andrew J. Whittle,et al.  Mechanisms of Load Transfer and Arching for Braced Excavations in Clay , 2002 .

[7]  J H Garrett,et al.  KNOWLEDGE-BASED MODELLING OF MATERIAL BEHAVIOUR WITH NEURAL NETWORKS , 1991 .

[8]  Ahmed Elgamal,et al.  Dynamic Soil Properties, Seismic Downhole Arrays and Applications in Practice , 2001 .

[9]  Y. Hashash,et al.  Glyph and hyperstreamline representation of stress and strain tensors and material constitutive response , 2003 .

[10]  Jean-Pierre Bardet.  Numerical simulations of the incremental responses of idealized granular materials , 1994 .

[11]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[12]  Jamshid Ghaboussi,et al.  Effective Stress Analysis of Seismic Response and Liquefaction: Theory , 1984 .

[13]  Youssef M A Hashash,et al.  Systematic update of a deep excavation model using field performance data , 2003 .

[14]  James Graham,et al.  Yield states and stress–strain relationships in a natural plastic clay , 1983 .

[15]  H B Poorooshasb,et al.  Yielding and Flow of Sand in Triaxial Compression: Part I , 1966 .

[16]  R. Hill.  The mathematical theory of plasticity , 1950 .

[17]  Andrew J. Whittle,et al.  Formulation of MIT-E3 constitutive model for overconsolidated clays , 1994 .

[18]  James H. Garrett,et al.  Knowledge-Based Modeling of Material Behavior with Neural Networks , 1992 .

[19]  Youssef M A Hashash,et al.  Analysis of deep excavations in clay , 1992 .

[20]  Kenji Ishihara,et al.  Yielding of Sand in Triaxial Compression , 1974 .

[21]  Jamshid Ghaboussi,et al.  Constitutive modeling of geomaterials from non-uniform material tests , 1998 .

[22]  K. Roscoe,et al.  ON THE GENERALIZED STRESS-STRAIN BEHAVIOUR OF WET CLAY , 1968 .

[23]  Yannis F. Dafalias,et al.  The Concept and Application of the Bounding Surface in Plasticity Theory , 1981 .

[24]  Khaled Sobhan,et al.  Incremental Stress-Strain Behavior of Granular Soil , 1995 .