Please use this identifier to cite or link to this item: 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
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