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Numerical modeling and neural networks to identify model parameters from piezocone tests: II. Multi‐parameter identification from piezocone data
Author(s) -
Obrzud Rafał F.,
Truty Andrzej,
Vulliet Laurent
Publication year - 2011
Publication title -
international journal for numerical and analytical methods in geomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.419
H-Index - 91
eISSN - 1096-9853
pISSN - 0363-9061
DOI - 10.1002/nag.1028
Subject(s) - geotechnical engineering , artificial neural network , test data , engineering , boundary value problem , laboratory test , benchmark (surveying) , identification (biology) , geology , structural engineering , computer science , mathematics , machine learning , mathematical analysis , botany , biochemical engineering , biology , software engineering , geodesy
SUMMARY This paper completes the study presented in the accompanying paper, and demonstrates a numerical algorithm for parameter prediction from the piezocone test (CPTU) data. This part deals with a development of neural network (NN) models which are able to map multi‐variable input data onto typical geotechnical characteristics and constitutive parameters of the modified Cam clay model, which has been applied in this study. The identification procedure is designed for the coupled hydro‐mechanical boundary value problem in normally‐and lightly overconsolidated clayey soils including partially drained conditions that may appear during cone penetration. The NN models are trained with pseudo‐experimental measurements derived with the aid of the numerical model of the piezocone test, presented in the accompanying paper. Different input configurations containing CPTU measurements and some complementary data are studied with respect to the accuracy of predicted parameter values. Finally, the performance of the developed NN predictors is tested with field CPTU data which are derived from three well‐documented characterization sites, and the obtained predictions are compared with benchmark laboratory results. Copyright © 2011 John Wiley & Sons, Ltd.