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.

With the increasing number of deep rock engineering projects, many different types of tunnels have emerged, such as cross-tunnels. These tunnels intersect with each other in rock, which causes potential safety hazards. We must analyze the stability of the surrounding rock, to ensure worker safety. This article presents a method for dynamically assessing the stability of the surrounding rock in deep-buried cross-tunnels. The method consists of two main analysis steps: (1) P-wave velocity field inversion; and (2) Stability analysis of the surrounding rock. The P-wave velocity field inversion involves inverting the S-wave velocity field by Rayleigh wave and inverting the P-wave velocity field by adjoint state traveltime tomography. Then, a method of stability analysis is proposed which is used to update the mechanical properties of the rock (based on the continuously updated wave velocity field). The elastic modulus of the surrounding rock is approximated throughout the excavation process. CASRock V1.0 (Cellular Automation Software for engineering Rockmass fracturing processes) is used to assess rock damage via the equivalent plastic shear strain and local energy release rate. The new method is used to analyze the stability of a new tunnel excavated in Jinping (in China). The results reveal the severity and spatial distribution of the damage caused. The yield depth is concentrated near the sidewalls, while the top and bottom of the tunnel exhibit a smaller depth. The yield depths present a particular pattern of change (high–low–high–low) with increasing distance from tunnel #2. Finally, this research enriches our understanding of excavating deep cross-tunnels and makes an important contribution to improving worker safety in deep cross-tunnels.

The multichannel analysis of surface waves (MASW) method has been broadly used to investigate subsurface structures. The inversion of a single dispersion curve (DC) from an individual surface wave shot record can only generate a 1D velocity model that fails to represent accurately the real geological condition in the presence of lateral velocity heterogeneity. To improve imaging of sharp lateral variations along 2D profiles we propose a convolutional neural network-based windowed multichannel analysis of surface wave method (CNN-WMASW). The CNN-WMASW method uses multichannel surface wave data that are spatially windowed by Gaussian functions to extract local dispersion curves. The dispersion curves are inverted simultaneously by the CNN-based data-driven method, which maps the phase velocity to the S-wave velocity from end to end without any initial model. The relationship between adjacent dispersion curves (DCs) is captured and considered by the CNN model, which is trained on a dataset created by waveform simulation rather than the theoretical solutions of the surface wave propagating mode in the layered model. We tested the effectiveness and reliability of the proposed CNN- WMASW method using numerical examples and two field data examples. Both results show improved lateral resolution in the S-wave velocity maps retrieved by the CNN- WMASW method compared to the traditional multichannel method in which strong lateral heterogeneity is observed.

An improved meshfree method that considers cracking, contact behaviour and fluid–solid interaction (FSI) was developed and employed to shed light on the progressive failure of the water-resistant stratum and inrush process in a karst tunnel construction. Hydraulic fracturing tests considering different scenarios and inrush events of the field-scale Jigongling karst tunnel in three scenarios verify the feasibility of the improved meshfree method. The results indicate that the brittle fracture characteristics of the rock mass are captured accurately without grid re-meshing by improving the kernel function of the meshfree method. The complex contact behaviour of rock along the fracture surface during inrush is correctly captured through the introduction of Newton’s law-based block contact algorithms. FSI processing during inrush is accurately modelled by an improved two-phase adaptive adjacent method considering the discontinuous particles without coupling other solvers and additional artificial boundaries, which improves computational efficiency. Furthermore, the improved meshfree method simultaneously captures the fast inrush and rock failure in the Jigongling karst tunnel under varying thicknesses and strengths of water-resistant rocks and sizes of karst caves. As the thickness and strength of water-resistant rock increase, the possibility of an inrush disaster in the tunnel decreases, and a drop in the water level and an increase in the maximum flow velocity have significant delayed effects during the local inrush stage.