Detail publikace

Identification of Quasibrittle material parameters based on stochastic nonlinear simulation and artificial neural networks

NOVÁK, D. LEHKÝ, D.

Originální název

Identification of Quasibrittle material parameters based on stochastic nonlinear simulation and artificial neural networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Identification, materila parameters, stochastic nonlinear simulation, artificial neural networks

Autoři

NOVÁK, D.; LEHKÝ, D.

Rok RIV

2007

Vydáno

25. 6. 2007

Místo

Praha, Česká republika

Strany od

94

Strany do

95

Strany počet

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"
}