Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing
In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of t...
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| Format: | Article |
| Language: | English |
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The Prognostics and Health Management Society
2024-10-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/3855 |
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| author | Takahiro Saitoh Tsuyoshi Kato Sohichi Hirose |
| author_facet | Takahiro Saitoh Tsuyoshi Kato Sohichi Hirose |
| author_sort | Takahiro Saitoh |
| collection | DOAJ |
| description | In recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens. |
| format | Article |
| id | doaj-art-5f428a0ce2b54e19958ab895b7e9a39d |
| institution | OA Journals |
| issn | 2153-2648 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-5f428a0ce2b54e19958ab895b7e9a39d2025-08-20T02:13:03ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482024-10-01153110https://doi.org/10.36001/ijphm.2024.v15i3.3855Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization TestingTakahiro Saitoh0Tsuyoshi Kato1Sohichi Hirose2Gunma UniversityGunma UniversityTokyo Institute of TechnologyIn recent years, nondestructive testing for civil engineering structures has become increasingly important. Ultrasonic testing is one of nondestructive inspection methods for civil structures. However, the inspection of civil engineering structures takes much time because of the extensive scope of the inspection. Moreover, in the field of nondestructive testing, there are also concerns about a future shortage of inspectors, so that an innovative effective nondestructive method needs to be developed. This study proposes an automatic defect detection approach using pre-trained convolutional neural network for laser ultrasonic visualization testing. The effectiveness of the proposed method is confirmed by applying it to a concrete structure with a surface defect. Grad-CAM demonstrates that the created CNN model in this study accurately predicts the position of a surface defect of concrete specimens.https://papers.phmsociety.org/index.php/ijphm/article/view/3855convolutional neural networknondestructive ultrasonic testingdefect detectiondeep learninggrad-cam |
| spellingShingle | Takahiro Saitoh Tsuyoshi Kato Sohichi Hirose Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing International Journal of Prognostics and Health Management convolutional neural network nondestructive ultrasonic testing defect detection deep learning grad-cam |
| title | Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing |
| title_full | Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing |
| title_fullStr | Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing |
| title_full_unstemmed | Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing |
| title_short | Automatic Detection of Concrete Surface Defects Using Pre-Trained CNN and Laser Ultrasonic Visualization Testing |
| title_sort | automatic detection of concrete surface defects using pre trained cnn and laser ultrasonic visualization testing |
| topic | convolutional neural network nondestructive ultrasonic testing defect detection deep learning grad-cam |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/3855 |
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