Yang, S, Wang, Z, Wang, J et al. (5 more authors) (2022) Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network. Construction and Building Materials, 319. 125658. ISSN 0950-0618
Abstract
This work offers a defect segmentation approach for the nondestructive testing of tunnel lining internal defects using Ground Penetrating Radar (GPR) data. Given GPR synthetic data, it maps the internal defect structure, using a CNN named Segnet coupled with the Lovász softmax loss function, which enhances the accuracy, automation, and efficiency of defect identification. Experiments with both synthetic and actual data show that our innovative method overcomes problems in standard GPR data interpretation. A physical test model with a known defect was developed and manufactured, and GPR data was acquired and analyzed to verify the approach.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Construction and Building Materials. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Convolutional neural networks (CNNs); Ground Penetrating Radar (GPR); GPR data intelligent recognition; Tunnel lining defect |
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) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Jan 2022 14:25 |
Last Modified: | 21 Dec 2022 01:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.conbuildmat.2021.125658 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:182866 |