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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" }