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View all- Moreh FLyu HRizvi ZWuttke F(2024)Deep neural networks for crack detection inside structuresScientific Reports10.1038/s41598-024-54494-y14:1Online publication date: 23-Feb-2024
Crack Detection and Localization based on Spatio-Temporal Data using Residual Networks
Authors: 
Fathalla Moreh, Hao Lyu, 
Christian Beth, Steffen Strohm, Zarghaam Haider Rizvi, Frank Wuttke, Matthias RenzAuthors Info & Claims
Fathalla Moreh, Hao Lyu, Christian Beth, Steffen Strohm, Zarghaam Haider Rizvi, Frank Wuttke, Matthias RenzAuthors Info & ClaimsArticle No.: 14, Pages 1 - 4
Abstract
Damage detection in materials and structures plays a critical role in engineering and science applications like structural health monitoring. A particular challenge is presented by micro-scale cracks, which are imperceptible to the naked eye or in images, but may ultimately evolve into larger, potentially dangerous cracks. In this work, we propose spatio-temporal pattern recognition techniques to enable the detection of such imperceptible micro-cracks. In order to make these cracks detectable, we generate seismic waves on the surface area of interest and monitor how cracks interfere with the spatial propagation of the wave over time. On the resulting propagation image series we then apply segmentation techniques using deep encoder-decoder CNNs to predict the location of cracks, which otherwise could not be directly observed. Our solution is evaluated through extensive experiments on highly-realistic finite element simulations, which were developed by domain experts.
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Index Terms
- Crack Detection and Localization based on Spatio-Temporal Data using Residual Networks
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Publication History
Published: 23 August 2022
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- Deutsche Forschungsgemeinschaft
- German Federation of Industrial Research Associations
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SSDBM 2022
SSDBM 2022: 34th International Conference on Scientific and Statistical Database Management
July 6 - 8, 2022
Copenhagen, Denmark
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Overall Acceptance Rate 56 of 146 submissions, 38%
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View all- Moreh FLyu HRizvi ZWuttke F(2024)Deep neural networks for crack detection inside structuresScientific Reports10.1038/s41598-024-54494-y14:1Online publication date: 23-Feb-2024
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