Automated masonry spalling severity segmentation in historic railway tunnels using deep learning and a block face plane fitting approach

Smith, J., Paraskevopoulou, C., Cohn, A.G. orcid.org/0000-0002-7652-8907 et al. (3 more authors) (2024) Automated masonry spalling severity segmentation in historic railway tunnels using deep learning and a block face plane fitting approach. Tunnelling and Underground Space Technology, 153. 106043. ISSN 0886-7798

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Item Type: Article
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Copyright, Publisher and Additional Information:

© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Keywords: Masonry, Condition assessment, Deep learning, Semantic segmentation, Historic tunnels
Dates:
  • Published: November 2024
  • Published (online): 2 September 2024
  • Accepted: 17 August 2024
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence
The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 10 Sep 2024 14:43
Last Modified: 10 Sep 2024 14:43
Published Version: https://www.sciencedirect.com/science/article/pii/...
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.tust.2024.106043
Open Archives Initiative ID (OAI ID):

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