Injecting grout into the gaps between tunnel shield segments and surrounding rocks can reduce ground subsidence and prevent ground water penetration. However, insufficient grouting and grouting defects may cause serious geological disasters. Ground penetrating radar (GPR) is widely used as a nondestructive testing (NDT) method to evaluate grouting quality and determine the existence of defects. This paper provides an overview of GPR applications for grouting defect detection behind tunnel shield segments. State-of-the-art methodologies, field cases, experimental tests and signal processing methods are discussed. The reported field cases and model test results show that GPR can detect grouting defects behind shield tunnel segments by identifying reflected waves. However, some subsequent problems still exist, including the interference of steel bars and small differences in the dielectric constants among media. Recent studies have focused on enhancing the signal-to-noise ratio and imaging methods. Advanced GPR signal processing methods, including full waveform inversion and machine learning methods, are promising for detecting imaging defects. Additionally, we conduct a preliminary experiment to investigate environmental noise, antenna configuration and coupling condition influences. Some promising topics, including multichannel configuration, rapid evaluation methods, elastic wave method scanning equipment for evaluating grout quality and comprehensive NDT methods, are recommended for future studies.

Erecting underground structures in regions with unidentified weak layers, cavities, and faults is highly dangerous and potentially disastrous. An efficient and accurate near-surface exploration method is thus of great significance for guiding construction. In near-surface detection, imaging methods suffer from artifacts that the complex structure caused and a lack of efficiency. In order to realize a rapid, accurate, robust near-surface seismic imaging, a minimum variance spatial smoothing (MVSS) beamforming method is proposed for the seismic detection and imaging of underground geological structures under a homogeneous assumption. Algorithms such as minimum variance (MV) and spatial smoothing (SS), the coherence factor (CF) matrix, and the diagonal loading (DL) methods were used to improve imaging quality. Furthermore, it was found that a signal advance correction helped improve the focusing effect in near-surface situations. The feasibility and imaging quality of MVSS beamforming are verified in cave models, layer models, and cave-layer models by numerical simulations, confirming that the MVSS beamforming method can be adapted for seismic imaging. The performance of MVSS beamforming is evaluated in the comparison with Kirchhoff migration, the DAS beamforming method, and reverse time migration. MVSS beamforming has a high computational efficiency and a higher imaging resolution. MVSS beamforming also significantly suppresses the unnecessary components in seismic signals such as S-waves, surface waves, and white noise. Moreover, compared with basic delay and sum (DAS) beamforming, MVSS beamforming has a higher vertical resolution and adaptively suppresses interferences. The results show that the MVSS beamforming imaging method might be helpful for detecting near-surface underground structures and for guiding engineering construction.