Comparison of state of the art sampling-based Bayesian Updating techniques

authored by: M. B. Dodt, M. Kitahara, M. Broggi, M. Beer
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.
Organisation(s): Institute for Risk and Reliability
External Organisation(s): KU Leuven
Type: Conference contribution
Pages: 59-66
No. of pages: 8
Publication date: 2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Management Science and Operations Research, Safety, Risk, Reliability and Quality
Electronic version(s): https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias3839846&context=SearchWebhook&vid=32KUL_KUL:Lirias&lang=en&search_scope=lirias_profile&adaptor=SearchWebhook&tab=LIRIAS&query=any%2Ccontains%2CLIRIAS3839846&offset=0 (Access: Open)
https://doi.org/10.3850/978-981-18-5184-1_MS-02-146-cd (Access: Closed)

BibTeX

@inproceedings{945ca59643bf4d048ec58c79f8a99467,
title = "Comparison of state of the art sampling-based Bayesian Updating techniques",
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",
author = "Dodt, {M. B.} and M. Kitahara and M. Broggi and M. Beer",
year = "2024",
doi = "10.3850/978-981-18-5184-1_MS-02-146-cd",
language = "English",
isbn = "9789811851841",
pages = "59--66",
editor = "Michael Beer and Enrico Zio and Kok-Kwang Phoon and Ayyub, {Bilal M.}",
booktitle = "Proceedings of the 8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022",
publisher = "Research Publishing Services, Singapore",
note = "8th International Symposium on Reliability Engineering and Risk Management, ISRERM 2022 ; Conference date: 04-09-2022 Through 07-09-2022",
}