Nonparametric Bayesian stochastic model updating with hybrid uncertainties

Kitahara, Masaru, Bi, Sifeng, Broggi, Matteo and Beer, Michael ORCID: 0000-0002-0611-0345 (2022) Nonparametric Bayesian stochastic model updating with hybrid uncertainties. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 163. p. 108195.

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Official URL: http://dx.doi.org/10.1016/j.ymssp.2021.108195

Abstract

This work proposes a novel methodology to fulfil the challenging expectation in stochastic model updating to calibrate the probabilistic distributions of parameters without any assumption about the distribution formats. To achieve this task, an approximate Bayesian computation model updating framework is developed by employing staircase random variables and the Bhattacharyya distance. In this framework, parameters with aleatory and epistemic uncertainties are described by staircase random variables. The discrepancy between model predictions and observations is then quantified by the Bhattacharyya distance-based approximate likelihood. In addition, a Bayesian updating using the Euclidian distance is performed as preconditioner to avoid non-unique solutions. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated shear building model example and a challenging benchmark problem for uncertainty treatment. These examples demonstrate feasibility of the combined application of staircase random variables and the Bhattacharyya distance in stochastic model updating and uncertainty characterization.

Item Type:	Article
Uncontrolled Keywords:	Stochastic model updating, Approximate Bayesian computation, Nonparametric probability-box, Staircase random variable, Bhattacharyya distance
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	02 Aug 2021 07:54
Last Modified:	18 Jan 2023 21:34
DOI:	10.1016/j.ymssp.2021.108195
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3131934