Detail publikace

Inverse reliability problem solved by artificial neural networks

Originální název

Inverse reliability problem solved by artificial neural networks

Anglický název

Inverse reliability problem solved by artificial neural networks

Jazyk

en

Originální abstrakt

An efficient inverse reliability analysis method is proposed to obtain design parameters in order to achieve the prescribed reliability level. The inverse analysis method is based on the coupling of an artificial neural network and a small-sample simulation method of the Monte Carlo type used for efficient stochastic preparation of the training set utilized in artificial neural network training. The calculation of reliability is performed using the first order reliability method. The validity and efficiency of the approach is shown using numerical examples taken from the literature as well as from civil engineering computational mechanics for both single and multiple design parameters and single and multiple reliability constraints.

Anglický abstrakt

An efficient inverse reliability analysis method is proposed to obtain design parameters in order to achieve the prescribed reliability level. The inverse analysis method is based on the coupling of an artificial neural network and a small-sample simulation method of the Monte Carlo type used for efficient stochastic preparation of the training set utilized in artificial neural network training. The calculation of reliability is performed using the first order reliability method. The validity and efficiency of the approach is shown using numerical examples taken from the literature as well as from civil engineering computational mechanics for both single and multiple design parameters and single and multiple reliability constraints.

BibTex


@inproceedings{BUT107532,
  author="David {Lehký} and Drahomír {Novák}",
  title="Inverse reliability problem solved by artificial neural networks",
  annote="An efficient inverse reliability analysis method is proposed to obtain design parameters in order to achieve the prescribed reliability level. The inverse analysis method is based on the coupling of an artificial neural network and a small-sample simulation method of the Monte Carlo type used for efficient stochastic preparation of the training set utilized in artificial neural network training. The calculation of reliability is performed using the first order reliability method. The validity and efficiency of the approach is shown using numerical examples taken from the literature as well as from civil engineering computational mechanics for both single and multiple design parameters and single and multiple reliability constraints.",
  booktitle="Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures",
  chapter="107532",
  howpublished="electronic, physical medium",
  year="2013",
  month="june",
  pages="5303--5310",
  type="conference paper"
}