High-Resolution DEM Generation of Railway Tunnel Surface Using Terrestrial Laser Scanning Data for Clearance Inspection

Qiu, Wenge; Cheng, Yun-Jian

doi:10.1061/(asce)cp.1943-5487.0000611

Cited by 26 publications

(16 citation statements)

References 20 publications

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“…According to the planning requirements regarding tunnel safety, the tunnel generally includes linear parts and nonlinear/circular segments with fixed curvature [3]. Once the tunnel axis has been extracted, it should be further segmented into linear and nonlinear segments.…”

Section: Tunnel Axis Segmentationmentioning

confidence: 99%

“…Tunnel displacement and deformation monitoring are critical factors for engineers to evaluate the tunnel health and safety. Some indices such as the cross section's chord [1], convergence and dislocations [2], tunnel axis's settlement [2], and tunnel model's clearance [3] are important measures to evaluate the stability of tunnels. At the initial stage, some traditional methods are used to monitor the tunnel's health.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, this method is sensitive to the non-lining points, i.e., pipes and equipment attached to the lining. To overcome the drawback regarding the above centerline, the methods in [1,3] use multiple models, i.e., straight line, transition curve and curve to commonly fit the different segments of tunnel centerline, and then adopt the global least-squares adjustment to maintain the consistency between adjacent fitting models. To cope with missing data, Kang et al [1] recover the blank areas of tunnel profiles with a surficial interpolation algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the results in [16] demonstrate that the corrected intensity information is an effective physical criterion and a complementary data source to remove accessories that cannot be eliminated by sole geometric information. In another method, Yoon et al [8] use the radiometric characteristics for Mobile Laser Scanning (MLS) data to filter the accessories from the tunnel raw data, which cannot be directly used for processing the TLS point clouds because not only the scattering property of an object but also other variables, especially distance and incidence angle effects will influence backscattered energy in TLS data [3]. To address this deficiency, Tan et al [17] further investigate the effect of distance on the intensity of TLS data and propose a new correction method for different TLS scanners to overcome the deficiency of directly using the original intensity.…”

Section: Introductionmentioning

confidence: 99%

“…Nahangi and Haas [19] present an algorithm for automated quantification of discrepancies for components of assemblies by using skeletonized 3D models and TLS laser scans. Qiu and Cheng [3] propose a novel clearance inspection technique to generate the high-resolution digital surface model of a railway tunnel surface (bare lining) from TLS point clouds. By introducing a high-accuracy interpolation and filtering algorithm, this method can generate bare-lining models without limitation of tunnel cross section shapes.…”

Section: Introductionmentioning

confidence: 99%

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A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds

et al. 2019

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This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections’ radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density.

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Section: Tunnel Axis Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Flexible Architecture for Extracting Metro Tunnel Cross Sections from Terrestrial Laser Scanning Point Clouds

et al. 2019

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Reconstruction of tunnel lining rebars from terrestrial laser scanning data

Cheng

Zheng

Qiu

2023

Structural Concrete

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Incorrect rebar placement has led to serious concrete structural failures; thus, site inspectors must ensure that the rebars placed are compliant with asdesigned drawings before tunnel lining concreting. This paper aims to develop a methodology that automatically segments the semantics (e.g., main rebar and distribution rebar) and instances of curved rebars and reconstructs the tunnel lining rebars from the raw point clouds. There are five main parts in the proposed methodology: rebar mesh reshaping, rebar mesh extraction and refinement, semantic segmentation, instance labeling, and rebar mesh modeling. To validate the developed methodology, an experiment was conducted on the rebar point cloud of a high-speed railway tunnel. The evaluation of the accuracies of segmentation was conducted by comparisons with the existing methods and manually labeled ground truth data. The results show that the rebar segmentation has high precision values with the acceptable recall values, and that the rebar models reconstructed achieve millimeter-level (about 2 mm) accuracy.

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