This paper proposes a quantitative approach for selecting effective maintenance strategies for metro tunnels in order to reduce maintenance cost and ensure tunnel safety in the presence of structural degradation during the service life. A non-stationary gamma process is used to simulate the tunnel gradual degradation and to investigate the effects of different maintenance measures on the life cycle maintenance cost of metro tunnels in soft soil. Two commonly applied reinforcement methods, Aramid Fibre Reinforced Polymer and bonded steel plate reinforcement, are investigated in this study. The feasibility of the proposed methodology is demonstrated with maintenance strategies based on periodic and aperiodic inspection policies. Numerical results show that the optimisation of maintenance strategy can reduce the life cycle maintenance cost of metro tunnels in soft soil while ensuring sufficient level of structural safety, and the maintenance strategy with aperiodic inspection policy produces lower maintenance cost.

Timely acquiring deformation of sectional profile is a proactive maintenance activity for ensuring the safety of tunnels. Metro tunnels, especially constructed by shield tunnelling method, are long span infrastructures need to inspect their deformations at many different positions. This is a vast amount of work if carried by traditional approaches manually. Some advanced technologies, such as terrestrial laser scanning, are also unable to carry out the work quickly and economically. However, only severe deformations that may lead to emergency action are required to be detected timely. Thus, the urgent demands for acquiring sectional profile of metro tunnel should be of fast in operation, accuracy under tolerable acceptance and competitive with cost efficient. This paper focuses on the method of acquiring sectional profile of metro tunnels by a cart equipped with charge-coupled device cameras. The geometric features of sectional profile are extracted from images by a series of algorithms based on photogrammetry. Using this method, the speed of acquisition is improved no less than 5 km/h with acceptable accuracy. An on-site application validates that this method is sufficient for screening out severe deformations in metro tunnel.

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.

Metro systems in urban cities demand rapid inspection methods, in order to identify critical structural defects in a timely manner. However, traditional inspection methods are only specific to one kind of structural defect, which reduces the overall efficiency of inspection. This study proposes an integrated solution for rapidly acquiring and identifying two kinds of structural defects (surface defects and cross-sectional deformation) in a metro tunnel, using a cart equipped with non-metric cameras. The integrity and rapidity are considered in formulating a systematic design for the development of the acquisition device. Methodologies based on image processing and photogrammetry are proposed to identify the structural defects of the metro tunnel. A series of on-site tests validate that the proposed method has enough speed and has acceptable accuracy in detecting critical structural defects of metro tunnels. The cost and efficiency analysis shows that the proposed method is competitive, which will greatly improve the efficiency and reduce the costs of the inspection of metro tunnels.