Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC)
In civil engineering, image recognition technology in artificial intelligence is widely used in structural damage detection. Traditional crack monitoring based on concrete images uses image processing, which requires high image preprocessing techniques, and the results of detection are vulnerable to...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/4117957 |
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| _version_ | 1832554965714862080 |
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| author | Dongling Wu Hongxiang Zhang Yiying Yang |
| author_facet | Dongling Wu Hongxiang Zhang Yiying Yang |
| author_sort | Dongling Wu |
| collection | DOAJ |
| description | In civil engineering, image recognition technology in artificial intelligence is widely used in structural damage detection. Traditional crack monitoring based on concrete images uses image processing, which requires high image preprocessing techniques, and the results of detection are vulnerable to factors, such as lighting and noise. In this study, the full convolutional neural networks FCN-8s, FCN-16s, and FCN-32s are applied to monitoring of concrete apparent cracks and according to the image characteristics of concrete cracks and experimental results. The FCN-8s model was tested with a correct crack monitoring rate of 0.6721, while the new network model had a correct crack detection rate of 0.7585, a significant improvement in the correct crack detection rate. |
| format | Article |
| id | doaj-art-7fc3cc655d6a4a96b598b1bdc7409894 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-7fc3cc655d6a4a96b598b1bdc74098942025-02-03T05:49:57ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/4117957Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC)Dongling Wu0Hongxiang Zhang1Yiying Yang2School of Civil EngineeringSchool of Civil EngineeringSchool of Business AdministrationIn civil engineering, image recognition technology in artificial intelligence is widely used in structural damage detection. Traditional crack monitoring based on concrete images uses image processing, which requires high image preprocessing techniques, and the results of detection are vulnerable to factors, such as lighting and noise. In this study, the full convolutional neural networks FCN-8s, FCN-16s, and FCN-32s are applied to monitoring of concrete apparent cracks and according to the image characteristics of concrete cracks and experimental results. The FCN-8s model was tested with a correct crack monitoring rate of 0.6721, while the new network model had a correct crack detection rate of 0.7585, a significant improvement in the correct crack detection rate.http://dx.doi.org/10.1155/2022/4117957 |
| spellingShingle | Dongling Wu Hongxiang Zhang Yiying Yang Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) Journal of Advanced Transportation |
| title | Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) |
| title_full | Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) |
| title_fullStr | Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) |
| title_full_unstemmed | Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) |
| title_short | Deep Learning-Based Crack Monitoring for Ultra-High Performance Concrete (UHPC) |
| title_sort | deep learning based crack monitoring for ultra high performance concrete uhpc |
| url | http://dx.doi.org/10.1155/2022/4117957 |
| work_keys_str_mv | AT donglingwu deeplearningbasedcrackmonitoringforultrahighperformanceconcreteuhpc AT hongxiangzhang deeplearningbasedcrackmonitoringforultrahighperformanceconcreteuhpc AT yiyingyang deeplearningbasedcrackmonitoringforultrahighperformanceconcreteuhpc |