Deep Learning For Estimating Low-range Concrete Sub-surface Boundary Depths Using Ground Penetrating Radar Signals

Don, SWM; Thiyagarajan, K; Kodagoda, S

Deep Learning For Estimating Low-range Concrete Sub-surface Boundary Depths Using Ground Penetrating Radar Signals

Don, SWM Thiyagarajan, K

Kodagoda, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Sensors Letters, 2022, 6, (3), pp. 1-4
Issue Date:: 2022-03-09

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Full metadata record

Field	Value	Language
dc.contributor.author	Don, SWM
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.date.accessioned	2023-01-16T03:22:07Z
dc.date.available	2023-01-16T03:22:07Z
dc.date.issued	2022-03-09
dc.identifier.citation	IEEE Sensors Letters, 2022, 6, (3), pp. 1-4
dc.identifier.issn	2475-1472
dc.identifier.issn	2475-1472
dc.identifier.uri	http://hdl.handle.net/10453/164993
dc.description.abstract	This letter proposes a deep learning approach for nondestructively detecting concrete subsurface boundaries between corroded and noncorroded layers using ground penetrating radar (GPR). We utilize a finite difference time domain technique to simulate GPR electromagnetic wave propagation on various concrete models mimicking corrosion situations. Following that, a deep learning method based on convolutional neural networks is utilized to estimate the bulk relative permittivity of the compound concrete structure, as well as a multilayer perceptron-based method for clutter removal through surface wave prediction. By estimating relative permittivity and removing clutter in GPR signals, the proposed approach can reliably detect the subsurface boundaries, which is demonstrated by the evaluation results.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Sensors Letters
dc.relation.isbasedon	10.1109/lsens.2022.3147470
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Deep Learning For Estimating Low-range Concrete Sub-surface Boundary Depths Using Ground Penetrating Radar Signals
dc.type	Journal Article
utslib.citation.volume	6
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2024-02-01T00:00:00+1000Z
dc.date.updated	2023-01-16T03:22:06Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	3

Abstract:

This letter proposes a deep learning approach for nondestructively detecting concrete subsurface boundaries between corroded and noncorroded layers using ground penetrating radar (GPR). We utilize a finite difference time domain technique to simulate GPR electromagnetic wave propagation on various concrete models mimicking corrosion situations. Following that, a deep learning method based on convolutional neural networks is utilized to estimate the bulk relative permittivity of the compound concrete structure, as well as a multilayer perceptron-based method for clutter removal through surface wave prediction. By estimating relative permittivity and removing clutter in GPR signals, the proposed approach can reliably detect the subsurface boundaries, which is demonstrated by the evaluation results.

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http://hdl.handle.net/10453/164993