Publication detail

Solving Inverse Structural Reliability Problem Using Artificial Neural Networks and Small-Sample Simulation

LEHKÝ, D. NOVÁK, D.

Original Title

Solving Inverse Structural Reliability Problem Using Artificial Neural Networks and Small-Sample Simulation

Type

journal article - other

Language

English

Original Abstract

A new general inverse reliability analysis approach based on artificial neural networks is proposed. An inverse reliability analysis is a problem of obtaining design parameters corresponding to a specified reliability (reliability index or theoretical failure probability). Design parameters can be deterministic or they can be associated with random variables. The aim is to generally solve not only single design parameter cases but also multiple parameter problems with given multiple reliability constraints. Inverse analysis is based on the coupling of a stochastic simulation of the Monte Carlo type (the small-sample simulation method Latin hypercube sampling) and an artificial neural network. The validity and efficiency of this approach is shown using numerical examples with single as well as multiple reliability constraints and with single as well as multiple design parameters, and with independent basic random variables as well as random variables with prescribed statistical correlations.

Keywords

Inverse reliability problem, identification, artificial neural network, Latin hypercube sampling, uncertainties, reliability

Authors

LEHKÝ, D.; NOVÁK, D.

RIV year

2012

Released

30. 11. 2012

Location

United Kingdom

ISBN

1369-4332

Periodical

ADVANCES IN STRUCTURAL ENGINEERING

Year of study

15

Number

11

State

United States of America

Pages from

1911

Pages to

1920

Pages count

10

BibTex

@article{BUT97432,
  author="David {Lehký} and Drahomír {Novák}",
  title="Solving Inverse Structural Reliability Problem Using Artificial Neural Networks and Small-Sample Simulation",
  journal="ADVANCES IN STRUCTURAL ENGINEERING",
  year="2012",
  volume="15",
  number="11",
  pages="1911--1920",
  issn="1369-4332"
}