Clogging constitutes a significant obstacle to shield tunneling in mudstone soils. Previous research has focused on investigating the influence of soils and slurry properties on clogging, although little attention has been paid to the impact of tunneling parameters on clogging, and particularly early clogging warning during tunneling. This paper contributes to developing a real-time clogging early-warning approach, based on a self-updating machine learning method. The clogging judgment criteria are based on the statistical characteristics of whole-ring tunneling parameters. The paper proposes the use of random forest (RF) for a real-time self-updating early warning strategy for clogging. The performance of this approach is illustrated through its application to a slurry-pressure-balanced shield tunneling construction of Nanning metro line 1. Results show that the RF-based approach can predict clogging during a ring construction with only four minutes of tunneling data, with an accuracy of 95%. The RF model provided the best performance compared with the other machine learning methods. Furthermore, the RF model can realize an accurate clogging prediction in one ring, using less tunneling data with the self-updating mechanism.

Under the dual effects of the rapid growth of tunnel mileage and operating years, the application and research of tunnel crack identification based on machine vision are increasing with the vigorous development of machine vision. However, due to the complex environment in tunnels, it is difficult to quickly obtain tunnel lining cracks via computer visions in the tunnel. Therefore, this paper presents the design of a fast acquisition system with the geometric feature analysis for tunnel lining cracks, which has been integrated into a tunnel fast inspection vehicle with a machine vision module. Through the research on the image acquisition system of the tunnel lining, the parameter selection of the crack shooting hardware system is determined, and the fast calculation method of shooting parameters is proposed. The geometric characteristic analysis of the tunnel lining crack image is employed to calculate crack width and determine the optimal gray value of crack extraction. Field tests have been conducted in the highway tunnels in Zhejiang and Yunnan provinces in China and the result indicates that the proposed approach yields much better performance in the detection efficiency, whose time of detection is only 1%, and the number of personnel required is only 40% of the traditional pure manual method. Compared with similar systems, it also has significant advantages in crack resolution and detection speed. This research provides a means of rapid acquisition of tunnel cracks and laying a foundation for the evaluation of the service performance of the tunnel.

In order to overcome the difficulty in rapid detection for expressway tunnels, the coherence calculation of dual-frequency radar signal in the time domain is proposed to suppress the interference. A dual-frequency (400 MHz and 900 MHz) GPR and a manipulator are developed. In the horizontal direction, it has a −90°~90° range, a vertical rotation range from 0 to 90°, a length range from 5~9.5 m, and an antenna rotation between −40°~40°. The mobile scanning has been realized in the expressway tunnel, taking the vehicle as the mobile carrier. The research shows that: This equipment realizes the rapid detection of the tunnel without affecting the expressway’s opening. The imaging algorithm recognizes the structural thickness, reinforcement distribution characteristics, and structural diseases. According to the detection carried out in Zhejiang, China, the spacing between reinforcement in the second lining is 4 cm, and the thickness of the structure is about 0.55 m. However, the reinforcement has deformed badly, and the defects are also discerned in the structure. Compared with the traditional handheld radar detection, the equipment dramatically reduces the labor demand, time, and cost and can meet engineering needs. With the proposed method, the detection time is reduced to 12 min/km from 0.5 d/km, and the cost is reduced by more than two times. Furthermore, during the detection, the traffic can be maintained normally.

Detection and mapping of rock discontinuities are important during excavation. For the acquisition of precise quantitative data, terrestrial laser scanning (TLS) technology is widely used. However, studies on the effect of discontinuities parameters on detection are limited. We created discontinuity models with various roughnesses and connectors with various angles using 3D printing technology. Therefore, we can control the variables in the scanning. Several open-source packages were employed to obtain the information from the point cloud acquired by TLS. The result shows that as the angle between discontinuities increases, the recognition effect decreases. Furthermore, the roughness of the discontinuity makes it more likely to cause incorrect classification during the detection process. The effectiveness and limitations of structural plane information algorithms based on TLS technology to guide the engineering application of the technology are discussed in this study.