Detail publikace

Inverse reliability problem solved by artificial neural networks

LEHKÝ, D. NOVÁK, D.

Originální název

Inverse reliability problem solved by artificial neural networks

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

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.

Klíčová slova

Design parameters; First order reliability methods; Inverse analysis methods; Inverse reliability analysis; Inverse reliability problem; Multiple reliability constraints; Reliability level; Training sets

Autoři

LEHKÝ, D.; NOVÁK, D.

Rok RIV

2013

Vydáno

16. 6. 2013

Místo

New York, USA

ISBN

978-1-138-00086-5

Kniha

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures

Strany od

5303

Strany do

5310

Strany počet

8

BibTex

@inproceedings{BUT107532,
  author="David {Lehký} and Drahomír {Novák}",
  title="Inverse reliability problem solved by artificial neural networks",
  booktitle="Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures",
  year="2013",
  pages="5303--5310",
  address="New York, USA",
  isbn="978-1-138-00086-5"
}