Publication detail

Surrogate Modeling for Stochastic Assessment of Engineering Structures

LEHKÝ, D. NOVÁK, L. NOVÁK, D.

Original Title

Surrogate Modeling for Stochastic Assessment of Engineering Structures

Type

conference paper

Language

English

Original Abstract

In many engineering problems, the response function such as the strain or stress field of the structure, its load-bearing capacity, deflection, etc., comes from a finite element method discretization and is therefore very expensive to evaluate. For this reason, methods that replace the original computationally expensive (high-fidelity) model with a simpler (low-fidelity) model that is fast to evaluate are desirable. This paper is focused on the comparison of two surrogate modeling techniques and their potential for stochastic analysis of engineering structures; polynomial chaos expansion and artificial neural network are compared in two typical engineering applications. The first example represents a typical engineering problem with a known analytical solution, the maximum deflection of a fixed beam loaded with a single force. The second example represents a real-world implicitly defined and computationally demanding engineering problem, an existing bridge made of post-tensioned concrete girders.

Keywords

Artificial neural networks; Polynomial chaos expansion; Stochastic assessment; Surrogate modelling; Uncertainties propagation

Authors

LEHKÝ, D.; NOVÁK, L.; NOVÁK, D.

Released

9. 3. 2023

Publisher

Springer Science and Business Media Deutschland GmbH

Location

Germany

ISBN

9783031258909

Book

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Edition number

13811

Pages from

388

Pages to

401

Pages count

14

BibTex

@inproceedings{BUT185583,
  author="David {Lehký} and Lukáš {Novák} and Drahomír {Novák}",
  title="Surrogate Modeling for Stochastic Assessment of Engineering Structures",
  booktitle="Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
  year="2023",
  number="13811",
  pages="388--401",
  publisher="Springer Science and Business Media Deutschland GmbH",
  address="Germany",
  doi="10.1007/978-3-031-25891-6\{_}29",
  isbn="9783031258909"
}