Bull, L.A. orcid.org/0000-0002-0225-5010, Gardner, P. orcid.org/0000-0002-1882-9728, Rogers, T.J. orcid.org/0000-0002-3433-3247 et al. (3 more authors) (2021) Probabilistic inference for structural health monitoring: new modes of learning from data. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7 (1). 03120003. ISSN 2376-7642
Abstract
In data-driven structural health monitoring (SHM), the signals recorded from systems in operation can be noisy and incomplete. Data corresponding to each of the operational, environmental, and damage states are rarely available a priori; furthermore, labeling to describe the measurements is often unavailable. In consequence, the algorithms used to implement SHM should be robust and adaptive while accommodating for missing information in the training data—such that new information can be included if it becomes available. By reviewing novel techniques for statistical learning (introduced in previous work), it is argued that probabilistic algorithms offer a natural solution to the modeling of SHM data in practice. In three case-studies, probabilistic methods are adapted for applications to SHM signals, including semisupervised learning, active learning, and multitask learning.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 American Society of Civil Engineers. This is an author-produced version of a paper subsequently published in ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Structural health monitoring (SHM); Statistical machine learning; Pattern recognition; Semisupervised learning; Active learning; Multitask learning; Transfer learning |
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: | 29 Mar 2021 14:56 |
Last Modified: | 29 Mar 2021 15:54 |
Status: | Published |
Publisher: | American Society of Civil Engineers (ASCE) |
Refereed: | Yes |
Identification Number: | 10.1061/ajrua6.0001106 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172668 |