Detail publikace

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

LEHKÝ, D. NOVÁK, D.

Originální název

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

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

LEHKÝ, D.; NOVÁK, D.

Rok RIV

2012

Vydáno

30. 11. 2012

Místo

United Kingdom

ISSN

1369-4332

Periodikum

ADVANCES IN STRUCTURAL ENGINEERING

Ročník

15

Číslo

11

Stát

Spojené státy americké

Strany od

1911

Strany do

1920

Strany počet

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"
}