Automated ultrasonic-based diagnosis of concrete compressive damage amidst temperature variations utilizing deep learning

Wang, L; Yi, S; Yu, Y; Gao, C; Samali, B

Automated ultrasonic-based diagnosis of concrete compressive damage amidst temperature variations utilizing deep learning

Wang, L Yi, S Yu, Y

Gao, C Samali, B

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Mechanical Systems and Signal Processing, 2024, 221
Issue Date:: 2024-12-01

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Field	Value	Language
dc.contributor.author	Wang, L
dc.contributor.author	Yi, S
dc.contributor.author	Yu, Y https://orcid.org/0000-0001-9592-8191
dc.contributor.author	Gao, C
dc.contributor.author	Samali, B
dc.date.accessioned	2025-01-21T04:16:40Z
dc.date.available	2025-01-21T04:16:40Z
dc.date.issued	2024-12-01
dc.identifier.citation	Mechanical Systems and Signal Processing, 2024, 221
dc.identifier.issn	0888-3270
dc.identifier.issn	1096-1216
dc.identifier.uri	http://hdl.handle.net/10453/183946
dc.description.abstract	Ultrasonic-based non-destructive testing technologies have been extensively applied for detection of internal damage in concrete. However, it is vulnerable to environmental temperature variations. An automated ultrasonic-based diagnosis approach integrating the continuous wavelet transform, and the transfer learning enhanced deep convolutional neural networks is proposed to evaluate compressive damage amidst temperature variations. The ultrasonic tests were conducted on pre-damaged concrete specimens, considering both temperature variations and damage levels as variables. The results indicate that the temperature fluctuations significantly influence the ultrasonic parameters of concrete compression damage. The proposed method effectively identifies the concrete damage state amidst temperature variations. Furthermore, it is recommended that the temperature range within the training set should uniformly cover the expected temperature range throughout the lifespan of concrete structures. This study offers novel perspectives for ultrasonic testing of concrete subjected to environmental variations.
dc.language	English
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	Mechanical Systems and Signal Processing
dc.relation.isbasedon	10.1016/j.ymssp.2024.111719
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0905 Civil Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Acoustics
dc.subject.classification	4006 Communications engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	Automated ultrasonic-based diagnosis of concrete compressive damage amidst temperature variations utilizing deep learning
dc.type	Journal Article
utslib.citation.volume	221
utslib.for	0905 Civil Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-01-21T04:16:38Z
pubs.publication-status	Published
pubs.volume	221

Abstract:

Ultrasonic-based non-destructive testing technologies have been extensively applied for detection of internal damage in concrete. However, it is vulnerable to environmental temperature variations. An automated ultrasonic-based diagnosis approach integrating the continuous wavelet transform, and the transfer learning enhanced deep convolutional neural networks is proposed to evaluate compressive damage amidst temperature variations. The ultrasonic tests were conducted on pre-damaged concrete specimens, considering both temperature variations and damage levels as variables. The results indicate that the temperature fluctuations significantly influence the ultrasonic parameters of concrete compression damage. The proposed method effectively identifies the concrete damage state amidst temperature variations. Furthermore, it is recommended that the temperature range within the training set should uniformly cover the expected temperature range throughout the lifespan of concrete structures. This study offers novel perspectives for ultrasonic testing of concrete subjected to environmental variations.

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http://hdl.handle.net/10453/183946