Inverse analysis and optimization-based model updating for structural damage detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F23%3APU149729" target="_blank" >RIV/00216305:26110/23:PU149729 - isvavai.cz</a>

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/cepa.2136" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1002/cepa.2136</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/cepa.2136" target="_blank" >10.1002/cepa.2136</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Inverse analysis and optimization-based model updating for structural damage detection

Popis výsledku v původním jazyce

Structural health monitoring and early detection of structural damage is extremely important to maintain and preserve the service life of civil engineering structures. Identification of structural damage is usually performed using non-destructive vibration experiments combined with a mathematical procedure called model updating. The finite element model of the investigated structure is updated by incrementally adjusting its parameters so that the model responses gradually approach those of the real possibly damaged structure under investigation. This paper describes the use of two model updating methods. The first method employs metaheuristic optimization technique aimed multilevel sampling to efficiently search the design parameter space to achieve the best match between the deformed structure and its model. The second method approaches model updating as an inverse problem and uses machine learning-based model to approximate inverse relationship between structural response and structural parameters. Both methods are applied to damage identification of single- and double-span steel trusses. Finally, initial results of the hybrid method are presented. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed.

Název v anglickém jazyce

Inverse analysis and optimization-based model updating for structural damage detection

Popis výsledku anglicky

Structural health monitoring and early detection of structural damage is extremely important to maintain and preserve the service life of civil engineering structures. Identification of structural damage is usually performed using non-destructive vibration experiments combined with a mathematical procedure called model updating. The finite element model of the investigated structure is updated by incrementally adjusting its parameters so that the model responses gradually approach those of the real possibly damaged structure under investigation. This paper describes the use of two model updating methods. The first method employs metaheuristic optimization technique aimed multilevel sampling to efficiently search the design parameter space to achieve the best match between the deformed structure and its model. The second method approaches model updating as an inverse problem and uses machine learning-based model to approximate inverse relationship between structural response and structural parameters. Both methods are applied to damage identification of single- and double-span steel trusses. Finally, initial results of the hybrid method are presented. The effect of the damage rate and location on the identification speed and the accuracy of the solution is investigated and discussed.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20101 - Civil engineering

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

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

EUROSTRUCT 2023 - European Association on Quality Control of Bridges and Structures: Digital Transformation in Sustainability

ISBN

—

ISSN

2509-7075

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1228-1233

Název nakladatele

Ernst & Sohn

Místo vydání

Berlin, Germany

Místo konání akce

Vienna, Austria

Datum konání akce

25. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001256546000037