Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN
文献类型：期刊论文
| 作者 | Wang, Dengyi2,3; Peng, Ming2,3; Liu, Liu1; Xie, Xiongyao2,3; Shi, Zhenming2,3; Liang, Yaoying2,3; Shen, Jian2,3; Wu, Qiyu2,3 |
| 刊名 | CASE STUDIES IN CONSTRUCTION MATERIALS
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 22页码:26 |
| 关键词 | Ground penetration radar Non-destructive testing Signal processing Defects automatic detection Multi-task learning Structure maintenance |
| ISSN号 | 2214-5095 |
| DOI | 10.1016/j.cscm.2025.e04245 |
| 英文摘要 | 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. |
| 资助项目 | National Natural Science Foundation of China[U23A2044] ; National Natural Science Foundation of China[42172296] ; National Natural Science Foundation of China[4227211] ; National Natural Science Foundation of China[42061160480] ; National Natural Science Foundation of China[42477195] |
| WOS研究方向 | Construction & Building Technology ; Engineering ; Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001402738800001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/37444]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Liu, Liu |
| 作者单位 | 1.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 2.Tongji Univ, Coll Civil Engn, Dept Geotech Engn, Shanghai 200092, Peoples R China 3.Tongji Univ, Dept Geotech Engn, Key Lab Geotech & Underground Engn, Minist Educ, Shanghai 200092, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Dengyi,Peng, Ming,Liu, Liu,et al. Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN[J]. CASE STUDIES IN CONSTRUCTION MATERIALS,2025,22:26. |
| APA | Wang, Dengyi.,Peng, Ming.,Liu, Liu.,Xie, Xiongyao.,Shi, Zhenming.,...&Wu, Qiyu.(2025).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,22,26. |
| MLA | Wang, Dengyi,et al."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 22(2025):26. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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