The common statistical methods for rail tunnel deformation and disease detection usually require a large amount of equipment and manpower to achieve full section detection, which are time consuming and inefficient. The development trend in the industry is to use laser scanning for full section detection. In this paper, a design scheme for a tunnel monitoring and measuring system with laser scanning as the main sensor for tunnel environmental disease and deformation analysis is proposed. The system provides functions such as tunnel point cloud collection, section deformation analysis, dislocation analysis, disease extraction, tunnel and track image generation, roaming video generation, etc. Field engineering indicated that the repeatability of the convergence diameter detection of the system can reach ±2 mm, dislocation repeatability can reach ±3 mm, the image resolution is about 0.5 mm/pixel in the ballast part, and the resolution of the inner wall of the tunnel is about 1.5 mm/pixel. The system can include human–computer interaction to extract and label diseases or appurtenances and support the generation of thematic disease maps. The developed system can provide important technical support for deformation and disease detection of rail transit tunnels.

With the ongoing developments in laser scanning technology, applications for describing tunnel deformation using rich point cloud data have become a significant topic of investigation. This study describes the independently developed a mobile tunnel monitoring system called the second version of Tunnel Scan developed by Capital Normal University (CNU-TS-2) for data acquisition, which has an electric system to control its forward speed and is compatible with various laser scanners such as the Faro and Leica models. A comparison with corresponding data acquired by total station data demonstrates that the data collected by CNU-TS-2 is accurate. Following data acquisition, the overall and local deformation of the tunnel is determined by denoising and 360° deformation analysis of the point cloud data. To enhance the expression of the analysis results, this study proposes an expansion of the tunnel point cloud data into a two-dimensional image via cylindrical projection, followed by an expression of the tunnel deformation through color difference to visualize the deformation. Compared with the three-dimensional modeling method of visualization, this method is easier to implement and facilitates storage. In addition, it is conducive to the performance of comprehensive analysis of problems such as water leakage in the tunnel, thereby achieving the effect of multiple uses for a single image.

Efficient, high-precision, and automatic measurement of tunnel structural changes is the key to ensuring the safe operation of subways. Conventional manual, static, and discrete measurements cannot meet the requirements of rapid and full-section detection in subway construction and operation. Mobile laser scanning technology is the primary method for tunnel detection. Herein, we propose a method to calculate shield tunnel displacements of a full cross-section tunnel. The point cloud data, obtained via a mobile tunnel deformation detection system, were fitted, projected, and interpolated to generate an orthophoto image. Combined with the cumulative characteristics of the tunnel gray gradient, the longitudinal ring seam of the tunnel was identified, while the Canny algorithm and Hough line detection algorithm identified the transverse seam. The symmetrical vertical foot method and cross-section superposition analysis were used to calculate the circumferential and radial displacements, respectively. The proposed displacement calculation method achieves automatic recognition of a ring seam, reduces human–computer interaction, and is fast, intelligent, and accurate. Furthermore, the description of the tunnel deformation location and deformation amount is more quantitative and specific. These results confirm the significance of shield tunnel displacement monitoring based on mobile monitoring systems in tunnel disease monitoring.

A rapid, precise and automated means for the regular inspection and maintenance of a large number of tunnels is needed. Based on the depth study of the tunnel monitoring method, the CNU-TS-1 mobile tunnel monitoring system (TS1) is developed and presented. It can efficiently obtain the cross-sections that are orthogonal to the tunnel in a dynamic way, and the control measurements that depend on design data are eliminated. By using odometers to locate the cross-sections and correcting the data based on longitudinal joints of tunnel segment lining, the cost of the system has been significantly reduced, and the interval between adjacent cross-sections can reach 1–2 cm when pushed to collect data at a normal walking speed. Meanwhile, the relative deformation of tunnel can be analyzed by selecting cross-sections from original data. Through the measurement of the actual tunnel, the applicability of the system for tunnel deformation detection is verified, and the system is shown to be 15 times more efficient than that of the total station. The simulation experiment of the tunnel deformation indicates that the measurement accuracy of TS1 for cross-sections is 1.1 mm. Compared with the traditional method, TS1 improves the efficiency as well as increases the density of the obtained points.

Accurate extraction of tunnel lining is crucial for deformation monitoring, damage detection, and tunnel modeling. However, the extraction of tunnel lining is still challenging due to the different tunnel shapes and complicated construction scenes. As for tunnel point clouds, inconsistent local density and non-structural features also pose challenges. Previous research has shown that utilizing the centerline of a tunnel is the preferred method for extracting tunnel lining. However, imprecision often arises due to noise points and point cloud hole. To address these challenges, we propose a novel extraction framework for various tunnel shapes that seldom depends on the centerline. First, the structure detection module is designed to detect the lining structure from the raw point cloud. Following that, multi-scale clustering filtering is applied for fine extraction. The global clustering filtering procedure is then implemented to process the projected point cloud. Finally, the local filtering approach is created for confusion clustering. Experimental results show that the highest Kappa coefficient of the proposed method is 95.5%. Compared with the elliptic cylinder method, the angle threshold method, and the template method, the extraction accuracy of our method is improved by 6.2%, 10.9%, and 9.7%, respectively.