Deep neural networks for crack detection inside structures

Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline fr...
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Autor*in:	Fatahlla Moreh [verfasserIn] Hao Lyu [verfasserIn] Zarghaam Haider Rizvi [verfasserIn] Frank Wuttke [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Neural network Deep learning Crack detection Wavefield

Übergeordnetes Werk:	In: Scientific Reports - Nature Portfolio, 2011, 14(2024), 1, Seite 15
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:1 ; pages:15

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1038/s41598-024-54494-y

Katalog-ID:	DOAJ092149200

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spelling	10.1038/s41598-024-54494-y doi (DE-627)DOAJ092149200 (DE-599)DOAJ5f46368b04a8426a8cfc2ccfd765398e DE-627 ger DE-627 rakwb eng Fatahlla Moreh verfasserin aut Deep neural networks for crack detection inside structures 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. Neural network Deep learning Crack detection Wavefield Medicine R Science Q Hao Lyu verfasserin aut Zarghaam Haider Rizvi verfasserin aut Frank Wuttke verfasserin aut In Scientific Reports Nature Portfolio, 2011 14(2024), 1, Seite 15 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:14 year:2024 number:1 pages:15 https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/article/5f46368b04a8426a8cfc2ccfd765398e kostenfrei https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2024 1 15
allfields_unstemmed	10.1038/s41598-024-54494-y doi (DE-627)DOAJ092149200 (DE-599)DOAJ5f46368b04a8426a8cfc2ccfd765398e DE-627 ger DE-627 rakwb eng Fatahlla Moreh verfasserin aut Deep neural networks for crack detection inside structures 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. Neural network Deep learning Crack detection Wavefield Medicine R Science Q Hao Lyu verfasserin aut Zarghaam Haider Rizvi verfasserin aut Frank Wuttke verfasserin aut In Scientific Reports Nature Portfolio, 2011 14(2024), 1, Seite 15 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:14 year:2024 number:1 pages:15 https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/article/5f46368b04a8426a8cfc2ccfd765398e kostenfrei https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2024 1 15
allfieldsGer	10.1038/s41598-024-54494-y doi (DE-627)DOAJ092149200 (DE-599)DOAJ5f46368b04a8426a8cfc2ccfd765398e DE-627 ger DE-627 rakwb eng Fatahlla Moreh verfasserin aut Deep neural networks for crack detection inside structures 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. Neural network Deep learning Crack detection Wavefield Medicine R Science Q Hao Lyu verfasserin aut Zarghaam Haider Rizvi verfasserin aut Frank Wuttke verfasserin aut In Scientific Reports Nature Portfolio, 2011 14(2024), 1, Seite 15 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:14 year:2024 number:1 pages:15 https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/article/5f46368b04a8426a8cfc2ccfd765398e kostenfrei https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2024 1 15
allfieldsSound	10.1038/s41598-024-54494-y doi (DE-627)DOAJ092149200 (DE-599)DOAJ5f46368b04a8426a8cfc2ccfd765398e DE-627 ger DE-627 rakwb eng Fatahlla Moreh verfasserin aut Deep neural networks for crack detection inside structures 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved. Neural network Deep learning Crack detection Wavefield Medicine R Science Q Hao Lyu verfasserin aut Zarghaam Haider Rizvi verfasserin aut Frank Wuttke verfasserin aut In Scientific Reports Nature Portfolio, 2011 14(2024), 1, Seite 15 (DE-627)663366712 (DE-600)2615211-3 20452322 nnns volume:14 year:2024 number:1 pages:15 https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/article/5f46368b04a8426a8cfc2ccfd765398e kostenfrei https://doi.org/10.1038/s41598-024-54494-y kostenfrei https://doaj.org/toc/2045-2322 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_381 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2024 1 15
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author	Fatahlla Moreh
spellingShingle	Fatahlla Moreh misc Neural network misc Deep learning misc Crack detection misc Wavefield misc Medicine misc R misc Science misc Q Deep neural networks for crack detection inside structures
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title	Deep neural networks for crack detection inside structures
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title_sort	deep neural networks for crack detection inside structures
title_auth	Deep neural networks for crack detection inside structures
abstract	Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.
abstractGer	Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.
abstract_unstemmed	Abstract Crack detection is a long-standing topic in structural health monitoring. Conventional damage detection techniques rely on intensive, time-consuming, resource-intensive intervention. The current trend of crack detection emphasizes using deep neural networks to build an automated pipeline from measured signals to damaged areas. This work focuses on the seismic-wave-based technique of crack detection for plate structures. Previous work proposed an encoder–decoder network to extract crack-related wave patterns from measured wave signals and predict crack existence on the plate. We extend previous work with extensive experiments on different network components and a data preprocessing strategy. The proposed methods are tested on an expanded crack detection dataset. We found that a robust backbone network, such as Densely Connected Convolutional Network (DenseNet) can effectively extract the features characterizing cracks of wave signals, and by using the reference wave field for normalization, the accuracy of detecting small cracks can be further improved.
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title_short	Deep neural networks for crack detection inside structures
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