Publication detail

Artificial neural network based inverse reliability analysis

LEHKÝ, D. NOVÁK, D.

Original Title

Artificial neural network based inverse reliability analysis

Type

journal article - other

Language

English

Original Abstract

An inverse reliability analysis is the problem to find design parameters corresponding to specified reliability levels expressed by reliability index or by theoretical failure probability. Design parameters can be deterministic or they can be associated to random variables described by statistical moments. The aim is to solve generally not only the single design parameter case but also the multiple parameter problems with given multiple reliability constraints. A new general approach of inverse reliability analysis is proposed. The inverse analysis is based on the coupling of a stochastic simulation of Monte Carlo type and an artificial neural network. A novelty of the approach is the utilization of the efficient small-sample simulation method Latin Hypercube Sampling used for the stochastic preparation of the training set.

Keywords

Neural network, reliability analysis

Authors

LEHKÝ, D.; NOVÁK, D.

RIV year

2010

Released

10. 10. 2010

Location

Winheim

ISBN

1617-7061

Periodical

Proceedings in Applied Mathematics and Mechanics

Year of study

1

Number

10

State

United States of America

Pages from

187

Pages to

188

Pages count

2

BibTex

@article{BUT50904,
  author="David {Lehký} and Drahomír {Novák}",
  title="Artificial neural network based inverse reliability analysis",
  journal="Proceedings in Applied Mathematics and Mechanics",
  year="2010",
  volume="1",
  number="10",
  pages="187--188",
  issn="1617-7061"
}