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NOVÁK, D. LEHKÝ, D.
Original Title
Identification of Quasibrittle material parameters based on stochastic nonlinear simulation and artificial neural networks
Type
conference paper
Language
English
Original Abstract
A new approach of inverse analysis is proposed to obtain material parameters of a constitutive law for quasibrittle material in order to achieve the best agreement with experimental data. The inverse analysis is based on the coupling of a stochastic simulation and an artificial neural network (ANN). The identification parameters play the role of basic random variables with a scatter reflecting the physical range of potential values. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling (LHS) used for the stochastic preparation of the training set utilized in training the neural network. Once the network has been trained, it represents an aapproximation consequently utilized to provide the best possible set of model parameters for the given experimental data.
Keywords
Identification, materila parameters, stochastic nonlinear simulation, artificial neural networks
Authors
NOVÁK, D.; LEHKÝ, D.
RIV year
2007
Released
25. 6. 2007
Location
Praha, Česká republika
Pages from
94
Pages to
95
Pages count
2
BibTex
@inproceedings{BUT23249, author="Drahomír {Novák} and David {Lehký}", title="Identification of Quasibrittle material parameters based on stochastic nonlinear simulation and artificial neural networks", booktitle="MHM 2007", year="2007", pages="94--95", address="Praha, Česká republika" }