Abstract
Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.
Highlights
Void is a common defect in tunnel lining structure due to aging, environmental factors, inadequate or poor construction and maintenance
For the cases with rebar spacings of 10, 20, 30, and 40 cm, 100 ground penetrating radar (GPR) images are simulated for each case
Compared to detecting void echoes on original GPR images, the proposed method improves F1-score by an average of 16.9% and Mean average precision (mAP) value of 11.9%
Summary
Void is a common defect in tunnel lining structure due to aging, environmental factors, inadequate or poor construction and maintenance. Void in tunnel linings may pose a threat to the security, durability, and serviceability of tunnel structures. It is necessary to inspect void presence in tunnel lining during tunnel construction or operation stage. Compared with traditional methods such as core sampling and impact-echo, the ground penetrating radar (GPR) has gained widespread use in tunnel lining inspection due to its non-destructiveness and high-efficiency [1,2,3,4,5,6]. The obtained GPR data are reflected electromagnetic waveforms of the subsurface rather than direct imaging of the tunnel lining structure [7,8]. Further interpretation of GPR data is required to characterize the internal structure of tunnel linings
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