Deep learning based method of longitudinal dislocation detection for metro shield tunnel segment

Yu, Anbin; Wensheng, Mei; Han, Mulin

doi:10.1016/j.tust.2021.103949

Cited by 16 publications

(4 citation statements)

References 23 publications

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“…Operation efficiency is also difficult to accept in engineering applications. Mapping 3D point clouds to 2D grayscale images can improve processing efficiency by reducing data dimensionality [23,24]. The original data obtained by the laser scanner usually include the horizontal angle, vertical angle, spatial distance, and reflection intensity of the target in the scanner coordinate system.…”

Section: Data Dimension Reductionmentioning

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: Data Dimension Reductionmentioning

confidence: 99%

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

Gong

et al. 2022

Remote Sensing

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“…According to research [14,66], the extra depth information has proved to have positive effects in structural defect inspection and quantification. In theory, by employing the proposed approach, it should be able to segment and quantify all the target objects (bolt holes, segment joints, dislocations, etc.)…”

Section: Possible Applications Of the Proposed Approachmentioning

confidence: 99%

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

Zhou

Cheng

Huang

et al. 2021

Sensors

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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.

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“…Machine learning models can serve as an alternative method for predicting shield tunnel deformations. Yu et al [10] employed the convolution neural network to locate the position of the segment joint in the image and then determined the dislocation value between segments. Du et al [11] expanded the three-dimensional point cloud data of subway tunnels obtained by GRP5000 via cylindrical projection, removed the extra accessories with a filtering algorithm and computed the dislocation value between segments.…”

Section: Introductionmentioning

confidence: 99%

“…Yu et al. [10] employed the convolution neural network to locate the position of the segment joint in the image and then determined the dislocation value between segments. Du et al.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based identification of segment joint failure in underground tunnels

Jin

Yan

et al. 2023

Phil. Trans. R. Soc. A.

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Shield tunnels that reside deep within soft soil are subject to longitudinal differential settlement and structural deformation during long-term operation. Longitudinal deformation can be classified into two modes: bending and dislocation deformation. The failure of bolts and engineering treatment techniques differ between these two modes. Therefore, it is imperative to accurately identify the tunnel's longitudinal deformation mode to determine the validity of the segment joint and implement appropriate engineering treatment. Traditional methods for detecting dislocation or opening suffer from high labour costs. To address this issue, this study presents an innovative identification method using a back-propagation neural network (BPNN) to detect segment joint failure in underground tunnels. First, this study collects the tunnel settlement curves of various subways located in the East China soft soil area, and it calculates tunnel settlement-dislocation and settlement-opening datasets using the equivalent axial stiffness model. A corresponding BPNN regression model is subsequently established, and the new settlement curve is the input to this regression model to predict the dislocation and opening, thereby determining the validity of the segment joint. The efficiency of this method is demonstrated through its successful application to the Hangzhou Metro Tunnel. This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.

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Deep learning based method of longitudinal dislocation detection for metro shield tunnel segment

Cited by 16 publications

References 23 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

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

Machine learning-based identification of segment joint failure in underground tunnels

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