Surrogate Modeling for Stochastic Assessment of Engineering Structures

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU149731" target="_blank" >RIV/00216305:26110/23:PU149731 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-031-25891-6_29" target="_blank" >http://dx.doi.org/10.1007/978-3-031-25891-6_29</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-25891-6_29" target="_blank" >10.1007/978-3-031-25891-6_29</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Surrogate Modeling for Stochastic Assessment of Engineering Structures

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Surrogate Modeling for Stochastic Assessment of Engineering Structures

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20102 - Construction engineering, Municipal and structural engineering

Projekt

<a href="/cs/project/GA22-00774S" target="_blank" >GA22-00774S: Pravděpodobnostní posouzení v mostním inženýrství za užití náhradního metamodelu (MAPAB)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

9783031258909

ISSN

—

e-ISSN

—

Počet stran výsledku

14

Strana od-do

388-401

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

Germany

Místo konání akce

Certosa di Pontignano, Italy

Datum konání akce

18. 9. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000995538200029