Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN

Dengyi Wang; Ming Peng; Liu Liu; Xiongyao Xie; Zhenming Shi; Yaoying Liang; Jian Shen; Qiyu Wu

Case Studies in Construction Materials (Jul 2025)

Automatic GPR detection of grouting defects behind the tunnel shield segments based on wavelet coherence analysis combined with modified Res-RCNN

Dengyi Wang,
Ming Peng,
Liu Liu,
Xiongyao Xie,
Zhenming Shi,
Yaoying Liang,
Jian Shen,
Qiyu Wu

Affiliations

Dengyi Wang: 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, China
Ming Peng: 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, China
Liu Liu: State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China; Corresponding author.
Xiongyao Xie: 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, China
Zhenming Shi: 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, China
Yaoying Liang: 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, China
Jian Shen: 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, China
Qiyu Wu: 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, China

Journal volume & issue: Vol. 22
p. e04245

Abstract

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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.

Published in Case Studies in Construction Materials

ISSN: 2214-5095 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials
Website: https://www.journals.elsevier.com/case-studies-in-construction-materials

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