The role of the Bhattacharyya distance in stochastic model updating
The role of the Bhattacharyya distance in stochastic model updating
The Bhattacharyya distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework, in which the Bhattacharyya distance is fully embedded. The Bhattacharyya distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the stochastic updating by utilizing the superior features of the Bhattacharyya distance, representing a convenient, efficient, and capable metric for stochastic model updating and uncertainty characterization.
Approximate Bayesian computation, Bayesian updating, Model validation, Stochastic model updating, Uncertainty quantification
437-452
Bi, Sifeng
93deb24b-fda1-4b18-927b-6225976d8d3f
Broggi, Matteo
104881e3-cd8d-4909-9049-a914be5ee548
Beer, Michael
e44760ce-70c0-44f2-bb18-7197ba142788
17 August 2018
Bi, Sifeng
93deb24b-fda1-4b18-927b-6225976d8d3f
Broggi, Matteo
104881e3-cd8d-4909-9049-a914be5ee548
Beer, Michael
e44760ce-70c0-44f2-bb18-7197ba142788
Bi, Sifeng, Broggi, Matteo and Beer, Michael
(2018)
The role of the Bhattacharyya distance in stochastic model updating.
Mechanical Systems and Signal Processing, 117, .
(doi:10.1016/j.ymssp.2018.08.017).
Abstract
The Bhattacharyya distance is a stochastic measurement between two samples and taking into account their probability distributions. The objective of this work is to further generalize the application of the Bhattacharyya distance as a novel uncertainty quantification metric by developing an approximate Bayesian computation model updating framework, in which the Bhattacharyya distance is fully embedded. The Bhattacharyya distance between sample sets is evaluated via a binning algorithm. And then the approximate likelihood function built upon the concept of the distance is developed in a two-step Bayesian updating framework, where the Euclidian and Bhattacharyya distances are utilized in the first and second steps, respectively. The performance of the proposed procedure is demonstrated with two exemplary applications, a simulated mass-spring example and a quite challenging benchmark problem for uncertainty treatment. These examples demonstrate a gain in quality of the stochastic updating by utilizing the superior features of the Bhattacharyya distance, representing a convenient, efficient, and capable metric for stochastic model updating and uncertainty characterization.
This record has no associated files available for download.
More information
Accepted/In Press date: 5 August 2018
e-pub ahead of print date: 17 August 2018
Published date: 17 August 2018
Additional Information:
Publisher Copyright:
© 2018 Elsevier Ltd
Keywords:
Approximate Bayesian computation, Bayesian updating, Model validation, Stochastic model updating, Uncertainty quantification
Identifiers
Local EPrints ID: 490443
URI: http://eprints.soton.ac.uk/id/eprint/490443
ISSN: 0888-3270
PURE UUID: fb2dbb83-ae6b-4263-8385-110bc2b6e528
Catalogue record
Date deposited: 28 May 2024 16:44
Last modified: 29 May 2024 02:09
Export record
Altmetrics
Contributors
Author:
Sifeng Bi
Author:
Matteo Broggi
Author:
Michael Beer
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics