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
Abstract
The condition assessment of masonry lined railway tunnels typically involves manually identifying lining defects from photographic and lidar surveys taken of the tunnel intrados. This process is time-consuming and subjective to the assessor’s judgement. However, recent developments in machine learning achieve the quality metrics required to automate the detection of defects from noisy and irregular tunnel data, offering the potential to reduce tunnel assessment and maintenance costs. This paper proposes a deep learning workflow for defect segmentation. The method is evaluated on the task of masonry block segmentation from lidar data. Acceptable performance is achieved on a sample tunnel section, suggesting that similar methods are applicable to other masonry lined tunnel defect segmentation tasks.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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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: |
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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): | oai:eprints.whiterose.ac.uk:217192 |
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