Publication detail

Combinatorial reliability-based optimization of nonlinear finite element model using an artificial neural network-based approximation

SLOWIK, O. LEHKÝ, D. NOVÁK, D.

Original Title

Combinatorial reliability-based optimization of nonlinear finite element model using an artificial neural network-based approximation

Type

conference paper

Language

English

Original Abstract

The paper describes the reliability-based optimization of TT shaped precast roof girder produced in Austria. Extensive experimental studies on small specimens and small and full-scale beams have been performed to gain information on fracture mechanical behaviour of utilized concrete. Subsequently, the destructive shear tests under laboratory conditions were performed. Experiments helped to develop an accurate numerical model of the girder. The developed model was consequently used for advanced stochastic analysis of structural response followed by reliability-based optimization to maximize shear and bending capacity of the beam and minimize production cost under defined reliability constraints. The enormous computational requirements were significantly reduced by the utilization of artificial neural network-based approximations of the original nonlinear finite element model of optimized structure.

Keywords

Reliability-based optimization, combinatorial optimization, heuristic optimization, artificial neural network, double-loop reliability-based optimization, prestressed concrete girder optimization, stochastic analysis.

Authors

SLOWIK, O.; LEHKÝ, D.; NOVÁK, D.

Released

8. 1. 2021

Location

Siena, Italy

ISBN

978-3-030-64583-0

Book

Lecture Notes in Computer Science

Pages from

359

Pages to

371

Pages count

13

BibTex

@inproceedings{BUT169080,
  author="Ondřej {Slowik} and David {Lehký} and Drahomír {Novák}",
  title="Combinatorial reliability-based optimization of nonlinear finite element model using an artificial neural network-based approximation",
  booktitle="Lecture Notes in Computer Science",
  year="2021",
  pages="359--371",
  address="Siena, Italy",
  doi="10.1007/978-3-030-64583-0\{_}33",
  isbn="978-3-030-64583-0"
}