Probabilistic inference for structural health monitoring: new modes of learning from data

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

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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:
  • Published (online): 27 November 2020
  • Published: March 2021
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: https://doi.org/10.1061/ajrua6.0001106
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