Bayesian Updating of Model Parameters by Iterative Particle Filter with Importance Sampling
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6, Issue 2
Abstract
Data assimilation with a particle filter (PF) has attracted attention for use in Bayesian updating. However, PFs have a problem known as degeneracy, where weights tend to concentrate into only a few particles after a few iterations (all other particles degenerate), which causes poor computational performance. This study discusses the applicability of a PF to the Bayesian updating of model parameters and probabilistic prediction with the updated model and proposes a method that uses a PF to limit degeneracy. The proposed method, called iterative particle filter with importance sampling (IPFIS), uses iterative observation updating in a PF, a Gaussian mixture model, and importance sampling. Two examples are used to demonstrate the proposed algorithm. In the first example, posterior distributions of the stiffness parameters of a two-degree-of-freedom model are identified. In the second example, IPFIS is applied to a consolidation settlement problem of a soft ground due to embankment loading, and probability distributions of the geotechnical parameters in an elastoplastic finite-element model and the simulated settlement displacements are updated based on time-series observation.
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Data Availability Statement
All the numerical simulation results generated or used during the study are available from the corresponding author by request.
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Published In
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 6 • Issue 2 • June 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Jan 30, 2019
Accepted: Aug 30, 2019
Published online: Jan 22, 2020
Published in print: Jun 1, 2020
Discussion open until: Jun 22, 2020
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