Rapid Acquisition and Identification of Structural Defects of Metro Tunnel
Abstract
:1. Introduction
2. Solution for Rapid Acquisition
2.1. Integrity
2.2. Rapidity
- Acquiring data in a moving process, rather than setting up stations;
- Rapid speed of imaging by the cameras equipped on the integrated device; and
- Algorithm-based processing, rather than manual work.
3. Methods of Identifying Structural Defects
3.1. Surface Defects of Concrete Linings
3.1.1. Image Differencing Strategy
3.1.2. Image Preprocessing
3.1.3. Image Segmentation
3.1.4. Classification of Surface Defects
3.2. Cross-Sectional Profile of Metro Tunnel
3.2.1. Basic Principles of Transmissive Projection
3.2.2. Calibration of Imaging Subsystem
4. On-Site Application
4.1. Introduction of On-Site Application
4.2. Results of Identified Surface Defects
4.3. Results of Identified Cross-Sectional Deformation
4.4. Comparison with the State-of-the-Art Works
4.5. Analysis of Cost and Efficiency
5. Conclusions
- With a systematic consideration of integrity and rapidity, the design of the integrated device achieved the functional requirements, which were rapid and simultaneous acquisition of two kinds of structural defects during the moving process.
- The identification method, based on image differencing strategy, was able to rapidly extract leakages in the metro tunnel. However, the cracks and spallings were not able to be extracted, mainly due to the insufficient resolution of the cameras.
- The identification method, based on transmissive projection, was able to rapidly measure cross-sectional deformations of metro tunnel.
- The device and proposed method can greatly reduce labor costs, which improves the efficiency of inspection.
Author Contributions
Funding
Conflicts of Interest
References
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Type of defect | Leakage | Spalling | Crack |
---|---|---|---|
Area | Large | Medium | Small |
Length of long axis | Large | Medium | Large |
Length of short axis | Medium | Medium | Small |
Fill rate | Large | Large | Small |
Long axis/Short axis | Large | Small | Large |
Statistics | Experiment 1 | Experiment 2 | Experiment 3 |
---|---|---|---|
Mean | −3.21 | −1.59 | 2.35 |
Standard deviation | 8.97 | 8.10 | 8.94 |
Ranges of Cross-Sectional Deformation | Service State | Corresponding Measures |
---|---|---|
< | i,ii | No repair |
iii | Minor repair | |
iv | Medium repair | |
> | v | Major repair |
Methods | Sensors | Algorithms | Identified Defects | Reference |
---|---|---|---|---|
Proposed | CCD Camera | (1) Image differencing; (2) Transmissive projection | (1) Leakage; (2) Deformation | N.A. |
M-1 | CMOS Camera | Morphological image processing | Crack | [35] |
M-2 | CMOS Camera | Gabor filter invariant to rotation | Crack | [34] |
M-3 | CMOS Camera | Fully convolutional network (FCN) | Leakage | [36] |
M-4 | Laser scanner | Mesh modeling algorithm | Deformation | [23] |
M-5 | Laser scanner | Extraction of cross-sectional | Deformation | [37,44,45] |
M-6 | Laser scanner | Least squares adjustment | Deformation | [38,46] |
Methods | Technical Solution | Speed | Accuracy |
---|---|---|---|
Proposed | Removable cart equipped with cameras | >5 km/h | (1) Error rate ≈ 10%; (2) RMSE ≈ 9 mm |
M-1 | Train carriage equipped with cameras | N.A. | Error rate < 10% |
M-2 | Removable cart equipped with cameras | <0.9 m/s | Error rate < 5% |
M-3 | Removable cart equipped with cameras | 0–10 km/h | Error rate < 2% |
M-4 | Terrestrial Laser Scanning (TLS) -static mode | N.A. | Close to total station |
M-5 | Terrestrial Laser Scanning (TLS) -kinematic mode | Walking speed | RMSE: 0.8–4.8 mm |
M-6 | Train carriage equipped with Light Detection and Ranging (LiDAR) | 120 km/h | RMSE: 0.02–0.03 m |
Indicators | Manual Inspection | Total Station Method | Proposed Method |
---|---|---|---|
Labor cost (on-site) | 1.5 h | 30 h | 0.2 h |
Labor cost (in office) | 4.0 h | 0.1 h | 0.1 h |
Accuracy | Depend on manual qualification | 2 to 5 mm | about 27 mm |
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Ai, Q.; Yuan, Y. Rapid Acquisition and Identification of Structural Defects of Metro Tunnel. Sensors 2019, 19, 4278. https://doi.org/10.3390/s19194278
Ai Q, Yuan Y. Rapid Acquisition and Identification of Structural Defects of Metro Tunnel. Sensors. 2019; 19(19):4278. https://doi.org/10.3390/s19194278
Chicago/Turabian StyleAi, Qing, and Yong Yuan. 2019. "Rapid Acquisition and Identification of Structural Defects of Metro Tunnel" Sensors 19, no. 19: 4278. https://doi.org/10.3390/s19194278