Defect segmentation: Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network

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

Metadata

Authors/Creators:
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:
  • Accepted: 9 November 2021
  • Published (online): 21 December 2021
  • Published: 14 February 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: 31 Jan 2022 14:25
Last Modified: 31 Jan 2022 14:25
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.conbuildmat.2021.125658
Related URLs:

Download

Accepted Version


Embargoed until: 21 December 2022

Filename: Defect segmentation Mapping tunnel lining internal defects with ground penetrating radar data using a convolutional neural network-author version.pdf

Licence: CC-BY-NC-ND 4.0

Request a copy

file not available

Share / Export

Statistics