Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 |
| Editors | Michael Beer, Enrico Zio, Kok-Kwang Phoon, Bilal M. Ayyub |
| Pages | 59-66 |
| Number of pages | 8 |
| Publication status | Published - 2024 |
| Event | 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 - Hannover, Germany Duration: 4 Sept 2022 → 7 Sept 2022 |
Abstract
Nowadays, model updating has an increasing importance in many areas of interest for engineering applications such as structural health monitoring or risk and reliability assessment. As a matter of fact, it allows for solving a plethora of inverse problems with high computationally efficiency, allowing for example to monitor structural parameter, to detect damage quickly and to timely intervene with mitigating actions. Among the algorithms available to solve Bayesian updating, methods based on sampling present a flexibility that allows solving the problem numerically, either based on Markov Chain Monte Carlo (MCMC) method or based on the usage of Bayesian updating with structural reliability (BUS) methods. Additionally, BUS can be coupled with additional methods such as surrogate modelling and efficient simulation methods to further improve its numerical efficiency. Thus, engineering practitioners need to understand which possible combination of the available algorithms should be used to solve their needs. In this paper, we provide an overview of different MCMC and BUS methods, directly calling the model and also employing the Kriging meta-model, covering in detail the advantages and disadvantages of each method as well as their applicability. The investigated methods are applied to solve model updating and model class selection. Two numerical examples are used to verify and test the analysed methods, drawing conclusion on their accuracy, performance, robustness, numerical efficiency and ability to perform model class selection.
Keywords
- Bayesian Updating, Kriging, Markov Chain, Model Identification, Simulation
ASJC Scopus subject areas
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Safety, Risk, Reliability and Quality
Cite this
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Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022. ed. / Michael Beer; Enrico Zio; Kok-Kwang Phoon; Bilal M. Ayyub. 2024. p. 59-66.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Comparison of state of the art sampling-based Bayesian Updating techniques
AU - Dodt, M. B.
AU - Kitahara, M.
AU - Broggi, M.
AU - Beer, M.
PY - 2024
Y1 - 2024
N2 - Nowadays, model updating has an increasing importance in many areas of interest for engineering applications such as structural health monitoring or risk and reliability assessment. As a matter of fact, it allows for solving a plethora of inverse problems with high computationally efficiency, allowing for example to monitor structural parameter, to detect damage quickly and to timely intervene with mitigating actions. Among the algorithms available to solve Bayesian updating, methods based on sampling present a flexibility that allows solving the problem numerically, either based on Markov Chain Monte Carlo (MCMC) method or based on the usage of Bayesian updating with structural reliability (BUS) methods. Additionally, BUS can be coupled with additional methods such as surrogate modelling and efficient simulation methods to further improve its numerical efficiency. Thus, engineering practitioners need to understand which possible combination of the available algorithms should be used to solve their needs. In this paper, we provide an overview of different MCMC and BUS methods, directly calling the model and also employing the Kriging meta-model, covering in detail the advantages and disadvantages of each method as well as their applicability. The investigated methods are applied to solve model updating and model class selection. Two numerical examples are used to verify and test the analysed methods, drawing conclusion on their accuracy, performance, robustness, numerical efficiency and ability to perform model class selection.
AB - Nowadays, model updating has an increasing importance in many areas of interest for engineering applications such as structural health monitoring or risk and reliability assessment. As a matter of fact, it allows for solving a plethora of inverse problems with high computationally efficiency, allowing for example to monitor structural parameter, to detect damage quickly and to timely intervene with mitigating actions. Among the algorithms available to solve Bayesian updating, methods based on sampling present a flexibility that allows solving the problem numerically, either based on Markov Chain Monte Carlo (MCMC) method or based on the usage of Bayesian updating with structural reliability (BUS) methods. Additionally, BUS can be coupled with additional methods such as surrogate modelling and efficient simulation methods to further improve its numerical efficiency. Thus, engineering practitioners need to understand which possible combination of the available algorithms should be used to solve their needs. In this paper, we provide an overview of different MCMC and BUS methods, directly calling the model and also employing the Kriging meta-model, covering in detail the advantages and disadvantages of each method as well as their applicability. The investigated methods are applied to solve model updating and model class selection. Two numerical examples are used to verify and test the analysed methods, drawing conclusion on their accuracy, performance, robustness, numerical efficiency and ability to perform model class selection.
KW - Bayesian Updating
KW - Kriging
KW - Markov Chain
KW - Model Identification
KW - Simulation
UR - http://www.scopus.com/inward/record.url?scp=85202021813&partnerID=8YFLogxK
U2 - 10.3850/978-981-18-5184-1_MS-02-146-cd
DO - 10.3850/978-981-18-5184-1_MS-02-146-cd
M3 - Conference contribution
AN - SCOPUS:85202021813
SN - 9789811851841
SP - 59
EP - 66
BT - Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
A2 - Beer, Michael
A2 - Zio, Enrico
A2 - Phoon, Kok-Kwang
A2 - Ayyub, Bilal M.
T2 - 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022
Y2 - 4 September 2022 through 7 September 2022
ER -