ORCID Profile
0000-0002-5007-7247
Current Organisation
University Of Strathclyde
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Publisher: CRC Press
Date: 15-06-2018
Publisher: ASME International
Date: 24-03-2023
DOI: 10.1115/1.4056934
Abstract: Several on-line identification approaches have been proposed to identify parameters and evolution models of engineering systems and structures when sequential datasets are available via Bayesian inference. In this work, a robust and “tune-free” s ler is proposed to extend one of the sequential Monte Carlo implementations for the identification of time-varying parameters which can be assumed constant within each set of data collected but might vary across different sequences of datasets. The proposed approach involves the implementation of the affine-invariant Ensemble s ler in place of the Metropolis–Hastings s ler to update the s les. An adaptive-tuning algorithm is also proposed to automatically tune the step-size of the affine-invariant ensemble s ler which, in turn, controls the acceptance rate of the s les across iterations. Furthermore, a numerical investigation behind the existence of inherent lower and upper bounds on the acceptance rate, making the algorithm robust by design, is also conducted. The proposed method allows for the off-line and on-line identification of the most probable models under uncertainty. The proposed s ling strategy is first verified against the existing sequential Monte Carlo s ler in a numerical ex le. Then, it is validated by identifying the time-varying parameters and the most probable model of a nonlinear dynamical system using experimental data.
Publisher: Research Publishing Services
Date: 2020
Publisher: Elsevier BV
Date: 2022
Publisher: Research Publishing Services
Date: 2021
Publisher: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece
Date: 2019
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 10-2021
Publisher: CRC Press
Date: 15-06-2018
Publisher: Elsevier BV
Date: 04-2020
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 12-2018
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Edoardo Patelli.