A hierarchical DCNN-based approach for classifying imbalanced water inflow in rock tunnel faces

Chen, J, Huang, H, Cohn, AG orcid.org/0000-0002-7652-8907 et al. (3 more authors) (2022) A hierarchical DCNN-based approach for classifying imbalanced water inflow in rock tunnel faces. Tunnelling and Underground Space Technology, 122. 104399. ISSN 0886-7798

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2022 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Tunnelling and Underground Space Technology. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Water inflow; Rock tunnel; Image classification; Imbalanced images; Deep convolutional neural network
Dates:
  • Accepted: 20 January 2022
  • Published (online): 2 February 2022
  • Published: April 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 04 Feb 2022 15:05
Last Modified: 04 Feb 2022 15:05
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.tust.2022.104399

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