Tunnel Section Extraction and Deformation Analysis Based on Mobile Laser Scanning

With the increase of tunnel operating pressure and the complex surrounding building structures, it is necessary to accurately, rapidly and automatically monitor the deformation of tunnels to ensure safe operation of subway trains. By taking into account the shortcomings of traditional detection methods, such as splicing of sub-stations, low detection efficiency and large amount of manual work, this paper proposes a tunnel section deformation detection method based on mobile laser scanning technology. Combined with tunnel shape characteristics, a denoising method based on point cloud data by ellipse model with the random sample consensus (RANSAC) algorithm is proposed. In the process of iteration, the adaptive threshold is set to filter out disturbing points. The section fitting is implemented in every point cloud segment by the Non-Uniform Rational B-Splines (NURBS) curve algorithm, and the fitting effect was evaluated by calculating the shortest distance from the point on the fitting curve to the original point. Finally, the accuracy of this method is verified by comparing it to the section data obtained by total station and applied to some circular shield tunnel. The tunnel detection railcar was used to obtain point cloud data, and the deformation of the tunnel was analyzed by comparing it with the theoretical section.