Gardner, P. orcid.org/0000-0002-1882-9728, Barthorpe, R.J. orcid.org/0000-0002-6645-8482 and Lord, C. orcid.org/0000-0002-2470-098X (2017) Bayesian calibration and bias correction for forward model-driven SHM. In: Chang, F.-K. and Kopsaftopoulos, F., (eds.) Structural Health Monitoring 2017: Real-Time Material State Awareness and Data-Driven Safety Assurance. 11th International Workshop on Structural Health Monitoring (IWSHM), 12-14 Sep 2017, Stanford, CA, USA. DEStech Publications , pp. 2019-2027. ISBN 9781605953304
Abstract
Forward model-driven structural health monitoring (SHM) is an alternative approach to the two main categories of SHM research: model-driven and data-driven processes. It is a methodology whereby a validated numerical model is used, in a forward manner, in order to produce simulated damage state data for machine learning applications. This paper explores the use of Bayesian calibration and bias correction (BCBC) in order to simulate representative predictions of observational data. The technique allows calibration of a numerical model to be performed in a Bayesian scheme whilst accounting for bias that may occur due to simplifications of the underlying physics in the model. This paper demonstrates the application of BCBC, in a forward model-driven framework, for producing representative damage features for different damage extents and shows a comparison with both experimental and non-bias corrected damage features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 DEStech Publications. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Mar 2020 12:37 |
Last Modified: | 16 Mar 2020 12:37 |
Published Version: | http://www.dpi-proceedings.com/index.php/shm2017/a... |
Status: | Published |
Publisher: | DEStech Publications |
Refereed: | Yes |
Identification Number: | 10.12783/shm2017/14088 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:158404 |