Automatic Monitoring of Tunnel Deformation Based on High Density Point Clouds Data

Du, Liming; Zhong, Rugang; Sun, Haichao; Wu, Qing

doi:10.5194/isprs-archives-xlii-2-w7-353-2017

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…The geometric parameters of the section point cloud are obtained using an algebraic ellipse fitting method to achieve convergence detection [16,17]. Du et al [18] used the iterative ellipse fitting method to filter the point cloud noise and fit the tunnel section contour, in which the ellipse fitting method used the random sample consensus algorithm. Walton et al [19] discussed the feasibility of the ellipse fitting method in tunnel profile deformation analysis using a ground laser scanner.…”

Section: Related Work On Ellipse Fitting Methodsmentioning

confidence: 99%

Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning

Gong

et al. 2022

Remote Sensing

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The convergence diameter of shield tunnels is detected by ellipse fitting or local curve fitting to cross-section points. However, the tunnel section, which is extruded by an external force, has an irregular elliptical shape, and the waist of the tunnel is often blocked by accessories, resulting in data loss. This study proposes a convergence diameter and radial dislocation detection method based on block-level fitting. The proposed method solves the accuracy degradation caused by the model error and point cloud incompletion. First, the noise points in the tunnel section point cloud are removed using the least trimmed squares method. Second, the tunnel transverse seam is then located using the image edge detection algorithm. Third, the endpoint of the convergence diameter is determined by making a specific segment the center and shifting the detector from the center to the pinpoint. Finally, the convergence diameter and radial dislocation are detected by the endpoints of the segments. The experimental results showed that the absolute detection accuracy of this method was better than 3 mm, and the repeated detection accuracy was better than 2 mm. This result is consistent with prior total station measurements, which are more suitable for practical engineering applications.

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Section: Related Work On Ellipse Fitting Methodsmentioning

confidence: 99%

Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning

Gong

et al. 2022

Remote Sensing

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“…in the tunnel result in the presence of noises in the tunnel cross-sectional points; thus, it is necessary to remove unused points [ 21 , 22 ]. An iterative elliptical fitting method is used here to filter the noises in the cross-sectional points [ 23 ]. In the filtering method, according to the shortest distances from the cross-sectional points to the fitted ellipse, the mean and standard deviation can be calculated.…”

Section: Methodsmentioning

confidence: 99%

Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System

Zhong

Sun

et al. 2018

Sensors

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Abstract: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.

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“…For example, the calculation of the tunnel convergence diameter based on point cloud data and the calculation of the shield tunnel convergence diameter by denoising, slicing, and ellipse fitting of the point cloud data. The calculation accuracy is approximately 2 mm [ 31 , 32 ]. Several studies have also explored the calculation of tunnel clearance, chord length, guide height, and pull-out value of the catenary based on mobile laser data and achieved a calculation accuracy of 3 mm [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System

Yue

Sun

Zhong

et al. 2021

Sensors

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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.

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Automatic Monitoring of Tunnel Deformation Based on High Density Point Clouds Data

Cited by 11 publications

References 7 publications

Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning

Shield Tunnel Convergence Diameter Detection Based on Self-Driven Mobile Laser Scanning

Study of the Integration of the CNU-TS-1 Mobile Tunnel Monitoring System

Method for Tunnel Displacements Calculation Based on Mobile Tunnel Monitoring System

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