2020
DOI: 10.3390/jsan9020027
|View full text |Cite
|
Sign up to set email alerts
|

Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge

Abstract: A Bayesian framework is presented for finite element model-updating using experimental modal data. A novel likelihood formulation is proposed regarding the inclusion of the mode shapes, based on a probabilistic treatment of the MAC value between the model predicted and experimental mode shapes. The framework is demonstrated by performing model-updating for the Metsovo bridge using a reduced high-fidelity finite element model. Experimental modal identification methods are used in order to extract the modal char… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
18
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 24 publications
(18 citation statements)
references
References 48 publications
0
18
0
Order By: Relevance
“…BMU is best employed for analyzing laboratory experiments conducted in controlled environments (Prajapat and Ray-Chaudhuri 2016;Zhang et al, 2017;Rappel et al, 2018Rappel et al, , 2020Mohamedou et al, 2019;Rappel and Beex 2019) to reduce bias from modelling assumptions. BMU is also appropriate for analyzing large-scale systems when the physics-based models developed are unbiased approximations of reality as is often the case for mechanicalengineering applications (Abdallah et al, 2017;Avendaño-Valencia and Chatzi 2017;Hara et al, 2017;Argyris et al, 2020;Cooper et al, 2020;Patsialis et al, 2020).…”
Section: Low Model Complexitymentioning
confidence: 99%
“…BMU is best employed for analyzing laboratory experiments conducted in controlled environments (Prajapat and Ray-Chaudhuri 2016;Zhang et al, 2017;Rappel et al, 2018Rappel et al, , 2020Mohamedou et al, 2019;Rappel and Beex 2019) to reduce bias from modelling assumptions. BMU is also appropriate for analyzing large-scale systems when the physics-based models developed are unbiased approximations of reality as is often the case for mechanicalengineering applications (Abdallah et al, 2017;Avendaño-Valencia and Chatzi 2017;Hara et al, 2017;Argyris et al, 2020;Cooper et al, 2020;Patsialis et al, 2020).…”
Section: Low Model Complexitymentioning
confidence: 99%
“…Often, the dynamic response of the bridges is used either without considering uncertainties in the inference [5][6][7] or by adopting a probabilistic approach. 4,8 For example, Teughels and De Roeck 7 used ambient vibrations recorded by accelerometers to detect and quantify the damage of a three-span prestressed concrete bridge (bridge Z24) in Switzerland. The damage in the bridge was caused by settlement of one of the supporting piers.…”
Section: Introductionmentioning
confidence: 99%
“…The current paper focuses on the impact of the uncertainty of the estimated modal parameters in the model optimization, and most particularly for the MAC estimate, whose statistical properties in this context were never investigated, but only inferred in Bayesian updating [50]. In general, the MAC can be viewed as the inner product of two Gaussian unit vectors.…”
Section: Introductionmentioning
confidence: 99%