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research article

Optimization framework for calibration of constitutive models enhanced by neural networks

Obrzud, RF
•
Vulliet, Laurent  
•
Truty, A.
2009
International Journal for Numerical and Analytical Methods in Geomechanics

A two-level procedure designed for the estimation of constitutive model parameters is presented in this paper. The neural network (NN) approach at the first level is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient-based optimization techniques, considered here as a corrector that improves predicted parameters. The feedforward NN (FFNN) and the modified Gauss–Newton algorithms are briefly presented. The proposed framework is verified for the elasto-plastic modified Cam Clay model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the drained compression test. Two different formulations of the input data to the NN, enhanced by a dimensional reduction of experimental data using principal component analysis, are presented. The determination of model characteristics is demonstrated, first on numerical pseudo-experiments and then on the experimental data. The efficiency of the proposed approach by means of accuracy and computational effort is also discussed.

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Type
research article
DOI
10.1002/nag.707
Web of Science ID

WOS:000262276300004

Author(s)
Obrzud, RF
Vulliet, Laurent  
Truty, A.
Date Issued

2009

Published in
International Journal for Numerical and Analytical Methods in Geomechanics
Volume

33

Issue

1

Start page

71

End page

94

Subjects

back analysis

•

parameter identification

•

elasto-plastic model calibration

•

neural networks

•

principal component analysis

•

optimization

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LMS  
Available on Infoscience
April 21, 2008
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/23393
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