Detail publikace

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

Originální název

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

Anglický název

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

Jazyk

en

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.

Anglický 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.

BibTex


@inproceedings{BUT23249,
  author="Drahomír {Novák} and David {Lehký}",
  title="Identification of Quasibrittle material parameters based on stochastic nonlinear simulation and artificial neural networks",
  annote="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.",
  booktitle="MHM 2007",
  chapter="23249",
  year="2007",
  month="june",
  pages="94--95",
  type="conference paper"
}