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https://scholarbank.nus.edu.sg/handle/10635/246511
Title: | On-line Bayesian Inference for Structural Health Monitoring under Model Uncertainty using Sequential Ensemble Monte Carlo | Authors: | Lye, Adolphus Cicirello, Alice Patelli, Edoardo |
Issue Date: | 17-Sep-2023 | Citation: | Lye, Adolphus, Cicirello, Alice, Patelli, Edoardo (2023-09-17). On-line Bayesian Inference for Structural Health Monitoring under Model Uncertainty using Sequential Ensemble Monte Carlo. 13th International Conference on Structural Safety and Reliability. ScholarBank@NUS Repository. | Abstract: | This paper presents an application of the Sequential Ensemble Monte Carlo (SEMC) sampler to perform on-line Bayesian inference of latent parameters. The SEMC implements the Affine-invariant Ensemble sampler algorithm in place of the traditional Metropolis-Hastings algorithm. The objective of this research is to illustrate the strength of the SEMC, when applied to the analysis of a SDoF Spring-Mass-Damper system to identify the time-varying stiffness and damping coefficient parameters subjected to a random process degradation under model uncertainty. The results not only highlight the ability of the SEMC sampler to identify time-varying parameters at a lower computational cost, but also its robustness in moderating the sample acceptance rates via an adaptive tuning algorithm. | Source Title: | 13th International Conference on Structural Safety and Reliability | URI: | https://scholarbank.nus.edu.sg/handle/10635/246511 |
Appears in Collections: | Elements Staff Publications |
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File | Description | Size | Format | Access Settings | Version | |
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ICOSSAR 2021 Conference Paper [Adolphus Lye].pdf | Accepted version | 1.04 MB | Adobe PDF | OPEN | Published | View/Download |
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