A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Zhou, Mingliang; Cheng, Wen; Huang, Hongwei; Chen, Jiayao

doi:10.3390/s21175725

Cited by 34 publications

(15 citation statements)

References 64 publications

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“…It can assess the condition of the tunnel lining structure through the three-dimensional modeling of the tunnel. It is widely used in the detection of various surface defects, such as spalling defect [17], water leakage defect [18] and deformation [19]. The principle of the thermal imaging-based method is to convert the temperature distribution of the tunnel lining surface into a visual image through an infrared thermal imager, which can reflect some of the leakage and cavity areas on the shield tunnel.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Association Analysis of Multiple Surface Defects for Shield Subway Tunnel

Yin¹,

Lei²,

Ao³

et al. 2023

Preprint

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The surface defects of shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise the operation safety. To effectively detect the multiple surface defects, this research employs a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting the inspection of the shield tunnel on a metro line section, various surface defects are identified by the TIT, including water leakage defect, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, the association rules between different defects are calculated via an improved Apriori algorithm. Results show that: i) there are significant differences in different association rules of various surface defects of the shield tunnel; ii) the average confidence of the association rule “dislocation &amp; spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; iii) the weakest rule appears at “water leakage → spalling”, with the average confidence of 13%. The association analysis can be used in predicting the critical defects influencing the structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for the shield subway tunnel.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection and Association Analysis of Multiple Surface Defects for Shield Subway Tunnel

Yin¹,

Lei²,

Ao³

et al. 2023

Preprint

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“…Line-structured light is widely used in 3D measurement fields, such as computer vision, bridge tracking, construction monitoring, and unmanned vehicles 1 – 4 Generally, the structure of a light stripe can be divided into arcs, acute angles, and straight lines 5 . Existing line-structured light center extraction algorithms can be roughly divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

Tang,

Jiang,

Miao

et al. 2023

Opt. Eng.

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Common line-structured light center extraction algorithms have limitations in accuracy and robustness when extracting the center of line-structured light obtained from objects with complex surface structures. In this study, we propose a high-precision center extraction algorithm for multi-curvature line-structured light. This algorithm improves the enhanced parallel thinning algorithm to eliminate redundant points and burrs and fills for missing center points at the end of the light stripe via the improved internal propulsion center extraction algorithm. Finally, sub-pixel centers are recalculated in the direction of the normal vector. A comparative experiment using three classical algorithms was conducted based on a tooth model with a complex curved surface. The experimental results show that the mean absolute error of this algorithm is <0.1, which is only half that of the best-performing dual-threshold grayscale center-of-gravity method in the classical method. Extraction of the center can be performed for multi-curvature line-structured light over 20 curvatures in the range of ð0.0001; 4.1439Þ∕pixel −1 ; moreover, the end of the light stripe and the identification of the truncation can be improved. Therefore, the proposed algorithm is suitable for contour detection of multi-curvature targets and effectively improves the accuracy of high-precision object detection.

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“…While the inspection methods based on DIC usually requires the installation of equipment inside tunnels. Three-dimensional laser scanning technology, which can quickly obtain a large amount of point cloud data in the three-dimensional space of tunnel structure, is used to analyze tunnel structure, such as convergence [ 29 ], limiting boundary [ 30 ], water leakage [ 31 ], and falling blocks [ 32 ]. An error model of laser scanning have been used to analyze the spatial distribution of the errors in a point cloud of a circular tunnel section, which shows that the errors induced by the angle of incidence will be eliminated when it comes to surface fitting [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study

Lin

Xie

2022

Sensors

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Adjacent tunnel construction and environmental disturbances can lead to longitudinal deformation in pipe-jacking tunnels. The longitudinal deformation of the tunnel is closely related to the occurrence of joint dislocation, joint opening, and other defects. In view of the difficulty of obtaining 3D longitudinal deformation curves, a method is proposed to obtain 3D longitudinal deformation curves based on a large number of 3D point cloud data with high spatial resolution and large spatial dimensions. Combined with the mechanism of defects occurrence, a theoretical basis for tunnel defects assessment based on tunnel longitudinal deformation is proposed. Taking one pipe jacking tunnel as an example, the longitudinal settlement curve and the 3D longitudinal deformation curve are compared. The correlation between the 3D longitudinal deformation curve and defects such as mud leakage, cracks, and differential deformation is illustrated from the perspective of three indexes: deformation amount, bending deformation, and shearing deformation. The accuracy and reliability of the 3D longitudinal deformation curve in tunnel defects detection and assessment are verified.

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A Novel Approach to Automated 3D Spalling Defects Inspection in Railway Tunnel Linings Using Laser Intensity and Depth Information

Cited by 34 publications

References 64 publications

Automatic Detection and Association Analysis of Multiple Surface Defects for Shield Subway Tunnel

Automatic Detection and Association Analysis of Multiple Surface Defects for Shield Subway Tunnel

High-precision multi-curvature line-structured light center extraction algorithm based on a tooth model

A Novel Detection and Assessment Method for Operational Defects of Pipe Jacking Tunnel Based on 3D Longitudinal Deformation Curve: A Case Study

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