Details
| Original language | English |
|---|---|
| Journal | e-Journal of Nondestructive Testing |
| Volume | 29 |
| Issue number | 7 |
| Publication status | Published - 1 Jul 2024 |
Abstract
Model updating is a popular tool for damage localisation and quantification in structural monitoring of buildings, infrastructure and wind turbines. In general, model updating relies on information about damage sensitive features, most often natural frequencies of the structure, which change in case of damage. Taking into account the prevailing uncertainty in these applications is very important to achieve reliable results. A frequently used method to conduct model updating while considering uncertainty is Bayesian model updating. This work shows, that the state-of-the-art formulation of the likelihood function in Bayesian model updating may lead to inaccurate results in practical structural applications. According to the state of the art, natural frequencies, which are identified with a higher certainty, are more heavily weighted. Even though this approach is useful in general, for use in structural application, this may lead to inaccurate results. This work presents the aforementioned issues with the state-of-the-art method in detail and presents an option to solve them using a new formulation of the likelihood function, which weighs every natural frequency equally. Different likelihood functions are applied and validated using a laboratory steel beam experiment equipped with reversible damage mechanisms. This allows a comparison of the results for a variety of different damage scenarios. This work shows that the new formulation of the likelihood function can improve the results of the model updating.
Keywords
- ABC, Bayesian model updating, BayOMA, laboratory experiment, TMCMC
ASJC Scopus subject areas
- Health Professions(all)
- Health Information Management
- Computer Science(all)
- Computer Science Applications
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In: e-Journal of Nondestructive Testing, Vol. 29, No. 7, 01.07.2024.
Research output: Contribution to journal › Article › Research
}
TY - JOUR
T1 - Investigations of different likelihood functions for the use in Bayesian model updating in structural applications
AU - Dierksen, Niklas
AU - Hofmeister, Benedikt
AU - Jonscher, Clemens
AU - Hübler, Clemens
N1 - Publisher Copyright: © 2024 11th European Workshop on Structural Health Monitoring, EWSHM 2024. All rights reserved.
PY - 2024/7/1
Y1 - 2024/7/1
N2 - Model updating is a popular tool for damage localisation and quantification in structural monitoring of buildings, infrastructure and wind turbines. In general, model updating relies on information about damage sensitive features, most often natural frequencies of the structure, which change in case of damage. Taking into account the prevailing uncertainty in these applications is very important to achieve reliable results. A frequently used method to conduct model updating while considering uncertainty is Bayesian model updating. This work shows, that the state-of-the-art formulation of the likelihood function in Bayesian model updating may lead to inaccurate results in practical structural applications. According to the state of the art, natural frequencies, which are identified with a higher certainty, are more heavily weighted. Even though this approach is useful in general, for use in structural application, this may lead to inaccurate results. This work presents the aforementioned issues with the state-of-the-art method in detail and presents an option to solve them using a new formulation of the likelihood function, which weighs every natural frequency equally. Different likelihood functions are applied and validated using a laboratory steel beam experiment equipped with reversible damage mechanisms. This allows a comparison of the results for a variety of different damage scenarios. This work shows that the new formulation of the likelihood function can improve the results of the model updating.
AB - Model updating is a popular tool for damage localisation and quantification in structural monitoring of buildings, infrastructure and wind turbines. In general, model updating relies on information about damage sensitive features, most often natural frequencies of the structure, which change in case of damage. Taking into account the prevailing uncertainty in these applications is very important to achieve reliable results. A frequently used method to conduct model updating while considering uncertainty is Bayesian model updating. This work shows, that the state-of-the-art formulation of the likelihood function in Bayesian model updating may lead to inaccurate results in practical structural applications. According to the state of the art, natural frequencies, which are identified with a higher certainty, are more heavily weighted. Even though this approach is useful in general, for use in structural application, this may lead to inaccurate results. This work presents the aforementioned issues with the state-of-the-art method in detail and presents an option to solve them using a new formulation of the likelihood function, which weighs every natural frequency equally. Different likelihood functions are applied and validated using a laboratory steel beam experiment equipped with reversible damage mechanisms. This allows a comparison of the results for a variety of different damage scenarios. This work shows that the new formulation of the likelihood function can improve the results of the model updating.
KW - ABC
KW - Bayesian model updating
KW - BayOMA
KW - laboratory experiment
KW - TMCMC
UR - http://www.scopus.com/inward/record.url?scp=85202529889&partnerID=8YFLogxK
U2 - 10.58286/29616
DO - 10.58286/29616
M3 - Article
VL - 29
JO - e-Journal of Nondestructive Testing
JF - e-Journal of Nondestructive Testing
SN - 1435-4934
IS - 7
ER -