Publication detail

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

LEHKÝ, D. ŠOMODÍKOVÁ, M.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

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

Authors

LEHKÝ, D.; ŠOMODÍKOVÁ, M.

RIV year

2015

Released

25. 9. 2015

Publisher

L. Iliadis and Ch. Jayne

Location

Rhodos, Řecko

ISBN

978-3-319-23983-5

Book

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

Pages from

35

Pages to

44

Pages count

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"
}