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LEHKÝ, D. NOVÁK, D.
Original Title
Inverse reliability problem solved by artificial neural networks
Type
conference paper
Language
English
Original Abstract
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.
Keywords
Design parameters; First order reliability methods; Inverse analysis methods; Inverse reliability analysis; Inverse reliability problem; Multiple reliability constraints; Reliability level; Training sets
Authors
LEHKÝ, D.; NOVÁK, D.
RIV year
2013
Released
16. 6. 2013
Location
New York, USA
ISBN
978-1-138-00086-5
Book
Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures
Pages from
5303
Pages to
5310
Pages count
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" }