Reliability-based optimization of a prestressed concrete roof girder using a surrogate model and the double-loop approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F21%3APU141563" target="_blank" >RIV/00216305:26110/21:PU141563 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1002/suco.202000455" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/suco.202000455</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/suco.202000455" target="_blank" >10.1002/suco.202000455</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reliability-based optimization of a prestressed concrete roof girder using a surrogate model and the double-loop approach

Popis výsledku v původním jazyce

The paper describes the reliability-based optimization of a TT-shaped precast roof girder produced in Austria. An extensive experimental programme was performed using laboratory specimens to gain information on the mechanical fracture parameters of the utilized concrete. Subsequently, destructive shear tests were performed on scaled-down and full-scale girders under laboratory conditions. The experiments helped in the development of an accurate numerical model of the girder. The developed model was consequently used for the advanced stochastic analysis of structural response, followed by reliability-based optimization, which was used to maximize the shear and bending capacities of the girder and minimize production cost under defined reliability constraints. The enormous computational requirements of the double-loop optimization approach were significantly reduced by the utilization of an artificial neural network-based surrogate model instead of the original nonlinear finite element model of the optimized structure.

Název v anglickém jazyce

Reliability-based optimization of a prestressed concrete roof girder using a surrogate model and the double-loop approach

Popis výsledku anglicky

The paper describes the reliability-based optimization of a TT-shaped precast roof girder produced in Austria. An extensive experimental programme was performed using laboratory specimens to gain information on the mechanical fracture parameters of the utilized concrete. Subsequently, destructive shear tests were performed on scaled-down and full-scale girders under laboratory conditions. The experiments helped in the development of an accurate numerical model of the girder. The developed model was consequently used for the advanced stochastic analysis of structural response, followed by reliability-based optimization, which was used to maximize the shear and bending capacities of the girder and minimize production cost under defined reliability constraints. The enormous computational requirements of the double-loop optimization approach were significantly reduced by the utilization of an artificial neural network-based surrogate model instead of the original nonlinear finite element model of the optimized structure.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

<a href="/cs/project/GA19-09491S" target="_blank" >GA19-09491S: Víceúrovňové stanovení lomově-mechanických parametrů pro simulaci betonových konstrukcí (MUFRAS)</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

Structural Concrete

ISSN

1464-4177

e-ISSN

1751-7648

Svazek periodika

22

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

18

Strana od-do

2184-2201

Kód UT WoS článku

000664424600001

EID výsledku v databázi Scopus

2-s2.0-85108283172