Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN
Ground penetrating radar (GPR), a widely used non-destructive testing technique for detecting grouting defects behind tunnel shield segments, faces challenges like steel rebar interference, low working efficiency, and expert interpretation reliance. To address these, this paper introduces an automat...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525000440 |
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| author | Dengyi Wang Ming Peng Liu Liu Xiongyao Xie Zhenming Shi Yaoying Liang Jian Shen Qiyu Wu |
| author_facet | Dengyi Wang Ming Peng Liu Liu Xiongyao Xie Zhenming Shi Yaoying Liang Jian Shen Qiyu Wu |
| author_sort | Dengyi Wang |
| collection | DOAJ |
| description | Ground penetrating radar (GPR), a widely used non-destructive testing technique for detecting grouting defects behind tunnel shield segments, faces challenges like steel rebar interference, low working efficiency, and expert interpretation reliance. To address these, this paper introduces an automated approach using wavelet coherence and a modified Res-RCNN. The approach employs wavelet coherence to transform the time-series GPR profile into the time-frequency images and reveal the weak defect reflections. Then, a modified Res-RCNN is applied to automatically extract the defect features from the wavelet coherence images. Finally, the post-processing and visualization automatically give an intuitive clear feature map that shows the location and probability of the grouting defects along the tunnel. The proposed methods are verified through full-size model tests with the aid of synthetic experiments to quantify their performance. The results show that wavelet coherence analysis improves the visibility of weak signals in (GPR) profiles, enabling their identification in the time-frequency domain by leveraging local coherence between adjacent signals and using phase information. The wavelet coherence analysis enables the observation of grouting defects behind tunnel shield segments with interferences of steel rebars. It can be applied even when the defect reflection is very weak, such as when the SNR is less than −40 dBs. The modified multi-task Res-RCNN, combined with post-processing and visualization, generates defect features including location and probability of existence. The network demonstrates superior training convergence and prediction accuracy due to information sharing between different task heads, compared to a two-classification network with the same Res-Net backbone. Through quantitative experiments in both model and synthetic tests, we recommend a trace interval of 15 to avoid the high coherence amplitude caused by two reflections out of same individual rebar. |
| format | Article |
| id | doaj-art-a097c9975ac34dfd9e4e7b284ae41e5d |
| institution | Kabale University |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-a097c9975ac34dfd9e4e7b284ae41e5d2025-01-18T05:04:40ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e04245Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNNDengyi Wang0Ming Peng1Liu Liu2Xiongyao Xie3Zhenming Shi4Yaoying Liang5Jian Shen6Qiyu Wu7Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaState Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; Corresponding author.Key Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaKey Laboratory of Geotechnical and Underground Engineering of Ministry of Education, Department of Geotechnical Engineering, Tongji University, Shanghai 200092, China; Department of Geotechnical Engineering, College of Civil Engineering, Tongji University, Shanghai 200092, ChinaGround penetrating radar (GPR), a widely used non-destructive testing technique for detecting grouting defects behind tunnel shield segments, faces challenges like steel rebar interference, low working efficiency, and expert interpretation reliance. To address these, this paper introduces an automated approach using wavelet coherence and a modified Res-RCNN. The approach employs wavelet coherence to transform the time-series GPR profile into the time-frequency images and reveal the weak defect reflections. Then, a modified Res-RCNN is applied to automatically extract the defect features from the wavelet coherence images. Finally, the post-processing and visualization automatically give an intuitive clear feature map that shows the location and probability of the grouting defects along the tunnel. The proposed methods are verified through full-size model tests with the aid of synthetic experiments to quantify their performance. The results show that wavelet coherence analysis improves the visibility of weak signals in (GPR) profiles, enabling their identification in the time-frequency domain by leveraging local coherence between adjacent signals and using phase information. The wavelet coherence analysis enables the observation of grouting defects behind tunnel shield segments with interferences of steel rebars. It can be applied even when the defect reflection is very weak, such as when the SNR is less than −40 dBs. The modified multi-task Res-RCNN, combined with post-processing and visualization, generates defect features including location and probability of existence. The network demonstrates superior training convergence and prediction accuracy due to information sharing between different task heads, compared to a two-classification network with the same Res-Net backbone. Through quantitative experiments in both model and synthetic tests, we recommend a trace interval of 15 to avoid the high coherence amplitude caused by two reflections out of same individual rebar.http://www.sciencedirect.com/science/article/pii/S2214509525000440Ground penetration radarNon-destructive testingSignal processingDefects automatic detectionMulti-task learningStructure maintenance |
| spellingShingle | Dengyi Wang Ming Peng Liu Liu Xiongyao Xie Zhenming Shi Yaoying Liang Jian Shen Qiyu Wu Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN Case Studies in Construction Materials Ground penetration radar Non-destructive testing Signal processing Defects automatic detection Multi-task learning Structure maintenance |
| title | Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN |
| title_full | Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN |
| title_fullStr | Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN |
| title_full_unstemmed | Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN |
| title_short | Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN |
| title_sort | automatic gpr detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified res rcnn |
| topic | Ground penetration radar Non-destructive testing Signal processing Defects automatic detection Multi-task learning Structure maintenance |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525000440 |
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