Structural health monitoring (SHM) is gradually replacing traditional manual detection and is becoming a focus of the research devoted to the operation and maintenance of tunnel structures. However, in the face of massive SHM data, the autonomous early warning method is still required to further reduce the burden of manual analysis. Thus, this study proposed a dynamic warning method for SHM data based on ARIMA and applied it to the concrete strain data of the Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel. First, wavelet threshold denoising was applied to filter noise from the SHM data. Then, the feasibility and accuracy of establishing an ARIMA model were verified, and it was adopted to predict future time series of SHM data. After that, an anomaly detection scheme was proposed based on the dynamic model and dynamic threshold value, which set the confidence interval of detected anomalies based on the statistical characteristics of the historical series. Finally, a hierarchical warning system was defined to classify anomalies according to their detection threshold and enable hierarchical treatments. The illustrative example of the HZMB immersed tunnel verified that a three-level (5.5 σ, 6.5 σ, and 7.5 σ) dynamic warning schematic can give good results of anomalies detection and greatly improves the efficiency of SHM data management of the tunnel.

Tunnels generally operate underground or underwater in a complex environment. As a result, the health monitoring system is inevitably affected by various environmental factors, which introduces noise to the system. However, the noise contained in the monitoring sequence may disrupt structural damage identification and health state assessment as the real structural response may be overwhelmed by the noise. To properly eliminate the noise in an objective way, this study proposed an improved wavelet threshold denoising method. Firstly, it adopts a quantitative factor, namely the Sparse Index, to assist the selection of the best wavelet basis in numerous wavelet packages. Then, the decomposition layer and threshold are optimized by a comprehensive evaluation based on a variation coefficient method. At last, the application of the concrete strain health monitoring data of the Hong Kong-Zhuhai-Macao Bridge immersed tunnel verified the effectiveness of the proposed method. It is found that the combination of sym12 and five decomposition layers can obtain the best denoising results within the selected wavelet families and decomposition levels. Moreover, the proposed method achieves good denoising results under different fluctuation levels. Thus, the proposed method is reliable, can solve the problem of optimal parameter selection such as decomposition level and wavelet basis in wavelet denoising, and can be applied in the structural health monitoring of critical infrastructures.

As the first waterproof component of the immersed tunnel, it is very important to ensure the remaining compression of the GINA gasket to resist external water intrusion. This paper proposed a method for determining warning thresholds for the remaining compression of the GINA gasket based on a material-to-mechanical analysis. In terms of material analysis, two factors that affect the GINA gasket are investigated: rubber hardness and cross-sectional shape, and they are adopted as the basis for subsequent mechanical analysis. In terms of mechanical analysis, uneven settlement during the operation period is considered to be the major cause of joint deformation, which is further divided into four modes: bending, shear, expansion, and torsion, with the computation model of the GINA gasket established to obtain the warning threshold. After that, a graded early warning method is adopted, and corresponding thresholds are given after an investigation of previous studies, which are validated by a three-dimensional finite element analysis. The deformation monitoring data between the E28 and E29 elements of the Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel are used to verify the proposed method. The results show that the GINA gasket of the Hong Kong–Zhuhai–Macao Bridge Immersed Tunnel is currently in a safe state, and its deformation is much lower than the minimum warning level.

Underground gas storage is an important technical measure for future natural gas storage. The stability of the surrounding rock during excavation and under ultra-high gas storage pressure is the key to the stable operation of gas storage reservoirs. A numerical calculation model for different surrounding rock conditions, different depth-span ratios, and different buried depth conditions was conducted to study the stability of surrounding rock after large section underground gas storage excavation and under an ultra-high gas storage pressure of 20 MPa. The results show that after construction is completed, the deformation of the rock surrounding the cavern increases with a decrease in the surrounding rock grade, and the deformation of the rock surrounding the cavern increases as the burial depth increases. In addition, the maximum vertical deformation of the surrounding rock decreases with the increase in the depth-span ratio of the cavern, and the maximum horizontal displacement increases with the increase in the depth-to-span ratio. While operating at 20 MPa gas storage pressure, the displacement of the rock surrounding the chamber tends to increase with the decrease in the surrounding rock grade and the deformation of the surrounding rock of the chamber decreases as the burial depth increases. Furthermore, the vertical displacement of the rock surrounding the chamber decreases with the increase in the depth-span ratio, while the horizontal displacement of the surrounding rock increases with the increase in the depth-span ratio. Considering the stability of the surrounding rock during construction and operation, gas storage chambers should be built in areas with better conditions, such as Grade II and Grade III surrounding rocks within a burial depth range of 200 m. Moreover, the stability of the surrounding rocks is better when the chamber depth-span ratio is 2.5~3.0. These research results can provide a theoretical reference for the design of large underground gas storage structures.

In the early times, restricted by the tunnel construction technology and technical difficulties, tunnel diseases, especially water leakage of lining are widespread in the tunnel engineering of China. Leakage disease would adversely affect the lining structure and operation environment, which may endanger the normal operation of tunnel projects. Existing leakage classification standards for tunnels are mainly based on the apparent view state, and the impacts of the actual operating environment are not fully taken into consideration. Based on the characteristics of leakage influences on tunnels, leakage in tunnels is analysed from the perspective of traffic safety, including the influence on driving safety and the influences of tunnel operation. Through investigation, point out the shortcomings of existing leakage classification methods, such as criteria are not complete, quantitative criteria are insufficient, disease treatment are not considered, and so on. The research results will provide important ideas for future research of tunnel lining leakage.