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
Abstract
Masonry lined tunnel condition assessment is a predominantly manual process. It consists primarily of a visual inspection followed by a lengthy and subjective manual defect labelling process. There is therefore much potential for automation. Masonry spalling is a key indicator of a masonry tunnel's condition. To obtain actionable detail about a tunnel's condition, it is also necessary to determine the spalling severity, defined by the depth of spalling. This study presents an automated workflow to identify the depth of spalling from masonry tunnel 3D point cloud data obtained by lidar. Firstly, a tunnel point cloud is unrolled using a cylindrical projection and the points are rasterised into a 2D image taking pixel values of the offset of each point from the cylinder. Then, a 2D U-Net pretrained on both real and synthetic masonry lining data, is used to segment masonry joint locations to isolate individual blocks. A separate U-Net is used to segment areas of masonry damage and data obstructions, which are then masked out before a surface plane representing the theoretical undamaged surface location is fitted to each masonry block from the remaining points. This allows the depth of spalling to be measured directly. As a result, this method can automatically determine the depth of spalling despite the curved and often deformed nature of a masonry tunnel profile. Experiments show results competitive with those obtained by human assessors.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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): | oai:eprints.whiterose.ac.uk:217032 |