Detail publikace

Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

LEHKÝ, D. ŠOMODÍKOVÁ, M.

Originální název

Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

The reliability analysis of complex structural systems requires utilization of approximation methods for calculation of reliability measures with the view of reduction of computational efforts to an acceptable level. The aim is to replace the original limit state function by an approximation, the so-called response surface, whose function values can be computed more easily. In the paper, an artificial neural network based response surface method in the combination with the small-sample simulation technique is introduced. An artificial neural network is used as a surrogate model for approximation of original limit state function. Efficiency is emphasized by utilization of the stratified simulation for the selection of neural network training set elements. The proposed method is employed for reliability assessment of post-tensioned composite bridge. Response surface obtained is independent of the type of distribution or correlations among the basic variables.

Klíčová slova

Artificial neural network, Latin hypercube sampling, Response surface method, Reliability, Failure probability, Load-bearing capacity

Autoři

LEHKÝ, D.; ŠOMODÍKOVÁ, M.

Rok RIV

2015

Vydáno

25. 9. 2015

Nakladatel

L. Iliadis and Ch. Jayne

Místo

Rhodos, Řecko

ISBN

978-3-319-23983-5

Kniha

Engineering Applications of Neural Networks, Proceedings of the 16th International Conference, EANN 2015, Rhodes, Greece, September 25–28, 2015

Strany od

35

Strany do

44

Strany počet

10

BibTex

@inproceedings{BUT120715,
  author="David {Lehký} and Martina {Sadílková Šomodíková}",
  title="Reliability Analysis of Post-Tensioned Bridge Using Artificial Neural Network-Based Surrogate Model",
  booktitle="Engineering Applications of Neural Networks, Proceedings of the 16th International Conference, EANN 2015, Rhodes, Greece, September 25–28, 2015",
  year="2015",
  pages="35--44",
  publisher="L. Iliadis and Ch. Jayne",
  address="Rhodos, Řecko",
  isbn="978-3-319-23983-5"
}