A combined neural network/gradient‐based approach for the identification of constitutive model parameters using self‐boring pressuremeter tests
Identifieur interne : 000A21 ( Main/Exploration ); précédent : 000A20; suivant : 000A22A combined neural network/gradient‐based approach for the identification of constitutive model parameters using self‐boring pressuremeter tests
Auteurs : Rafał F. Obrzud [Suisse] ; Laurent Vulliet [Suisse] ; Andrzej Truty [Pologne]Source :
- International Journal for Numerical and Analytical Methods in Geomechanics [ 0363-9061 ] ; 2009-04-25.
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Abstract
This paper presents a numerical procedure of material parameter identification for the coupled hydro‐mechanical boundary value problem (BVP) of the self‐boring pressuremeter test (SBPT) in clay. First, the neural network (NN) technique is applied to obtain an initial estimate of model parameters, taking into account the possible drainage conditions during the expansion test. 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. Parameter identification based on measurements obtained through the pressuremeter expansion test and two types of holding tests is illustrated on the Modified Cam clay model. NNs are trained using a set of test samples, which are generated by means of finite element simulations of SBPT. The measurements obtained through expansion and consolidation tests are normalized so that NN predictors operate independently of the testing depth. Examples of parameter determination are demonstrated on both numerical and field data. The efficiency of the combined parameter identification in terms of accuracy, effectiveness and computational effort is also discussed. Copyright © 2008 John Wiley & Sons, Ltd.
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DOI: 10.1002/nag.750
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Geomech.</title><idno type="ISSN">0363-9061</idno><idno type="eISSN">1096-9853</idno><imprint><publisher>John Wiley & Sons, Ltd.</publisher><pubPlace>Chichester, UK</pubPlace><date type="published" when="2009-04-25">2009-04-25</date><biblScope unit="volume">33</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page" from="817">817</biblScope><biblScope unit="page" to="849">849</biblScope></imprint><idno type="ISSN">0363-9061</idno></series><idno type="istex">6F74BE073B110E3118395C214CA00DECBABAEB13</idno><idno type="DOI">10.1002/nag.750</idno><idno type="ArticleID">NAG750</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0363-9061</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>modified cam clay</term><term>neural networks</term><term>parameter identification</term><term>self‐boring pressuremeter</term><term>soft clay deposit</term></keywords></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper presents a numerical procedure of material parameter identification for the coupled hydro‐mechanical boundary value problem (BVP) of the self‐boring pressuremeter test (SBPT) in clay. 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