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The number of tunnels is increasing sharply, especially in China, due to the development of urban rail transit networks. It is thus crucial to study rapid structural deformation detection methods for tunnels in operation. A dynamic tunnel monitoring system can directly obtain cross sections. However, using only odometers to locate the cross sections along the track of a tunnel results in insufficient accuracy. Furthermore, the measured cross sections are all in local coordinate systems, providing no indication of curved tunnels and restricting 3D display capabilities. This paper focuses on a relative positioning method based on mobile laser scanning to improve the accuracy, together with a unified positioning method to relate the measured cross sections in a single system. The accuracy and applicability of the proposed methods are verified by experiments which demonstrate that the solutions can fully meet accuracy requirements and 3D display purposes. [ABSTRACT FROM AUTHOR]

The detection of concrete spalling is critical for tunnel inspectors to assess structural risks and guarantee the daily operation of the railway tunnel. However, traditional spalling detection methods mostly rely on visual inspection or camera images taken manually, which are inefficient and unreliable. In this study, an integrated approach based on laser intensity and depth features is proposed for the automated detection and quantification of concrete spalling. The Railway Tunnel Spalling Defects (RTSD) database, containing intensity images and depth images of the tunnel linings, is established via mobile laser scanning (MLS), and the Spalling Intensity Depurator Network (SIDNet) model is proposed for automatic extraction of the concrete spalling features. The proposed model is trained, validated and tested on the established RSTD dataset with impressive results. Comparison with several other spalling detection models shows that the proposed model performs better in terms of various indicators such as MPA (0.985) and MIoU (0.925). The extra depth information obtained from MLS allows for the accurate evaluation of the volume of detected spalling defects, which is beyond the reach of traditional methods. In addition, a triangulation mesh method is implemented to reconstruct the 3D tunnel lining model and visualize the 3D inspection results. As a result, a 3D inspection report can be outputted automatically containing quantified spalling defect information along with relevant spatial coordinates. The proposed approach has been conducted on several railway tunnels in Yunnan province, China and the experimental results have proved its validity and feasibility. [ABSTRACT FROM AUTHOR]