Deep learning for masonry lined tunnel condition assessment

Smith, J. orcid.org/0000-0001-5331-5266, Paraskevopoulou, C., Bedi, A. et al. (1 more author) (2023) Deep learning for masonry lined tunnel condition assessment. In: Anagnostou, G., Benardos, A. and Marinos, V.P., (eds.) Expanding Underground - Knowledge and Passion to Make a Positive Impact on the World. The ITA-AITES World Tunnel Congress 2023 (WTC 2023), 12-18 May 2023, Athens, Greece. CRC Press , pp. 2910-2917. ISBN 9781003348030

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Metadata

Item Type: Proceedings Paper
Authors/Creators:
Editors:
  • Anagnostou, G.
  • Benardos, A.
  • Marinos, V.P.
Copyright, Publisher and Additional Information:

© 2023 The Author(s). This is an open access conference paper, under the terms of the CC BY-NC-ND 4.0 license.

Dates:
  • Published: 12 April 2023
  • Published (online): 12 April 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 16 Sep 2024 15:40
Last Modified: 16 Sep 2024 15:40
Status: Published
Publisher: CRC Press
Identification Number: 10.1201/9781003348030-351
Open Archives Initiative ID (OAI ID):

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