Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response
Abstract
:1. Introduction
2. Theoretical Basis
2.1. Definition of Tunnel Damage
2.2. The Governing Equation of Coupling Vibration between the Train and Tunnel
2.3. Analytical Solution of the Governing Equation
3. Damage Localization Method
3.1. Wavelet Packet Decomposition
3.2. The Definition and Algorithm Flow of the Damage Index
4. Numerical Study and Validation
4.1. Verification of 3D Damage Model
4.2. Verification of the Algorithm
4.2.1. Single Damage Situation
4.2.2. Two Damages Situation
4.2.3. Influence of Different Noise Levels
5. Experimental Study and Validation
5.1. Establishment of Experimental Model
5.2. Verification of Single Damage
5.3. Verification of Two Damages
5.4. Influence of Vehicle Speed
5.5. Influence of Vehicle Mass
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
- Zhu, F.; Xu, D.; Tan, R.; Peng, B.; Huang, H.; Liu, Z. Development of optic-electric hybrid sensors for the real-time intelligent monitoring of subway tunnels. J. Sens. 2021, 2021, 8871893. [Google Scholar] [CrossRef]
- Xu, X.; Yang, H. Vision measurement of tunnel structures with robust modelling and deep learning algorithms. Sensors 2020, 20, 4945. [Google Scholar] [CrossRef] [PubMed]
- Yu, H.; Zhu, H.P.; Weng, S.; Gao, F.; Luo, H.; Ai, D.M. Damage detection of subway tunnel lining through statistical pattern recognition. Struct. Monit. Maint. 2018, 5, 231–242. [Google Scholar]
- Gao, J.; Gui, F.; Yuan, W.; Zhang, B. State of the art of techniques and equipment for defect remediation in existing railway tunnels. Mod. Tunn. Technol. 2018, 55, 7–16. [Google Scholar]
- Wang, S.; Gao, Y.; Qi, F.; Ke, Z.; Li, H.; Lei, Y.; Peng, Z. Review on inspection technology of railway operation tunnels. J. Traffic Transp. Eng. 2020, 20, 41–57. [Google Scholar]
- Wang, L.; Xu, S.; Qiu, J.; Wang, K.; Ma, E.; Li, C.; Guo, C. Automatic monitoring system in underground engineering construction: Review and prospect. Adv. Civ. Eng. 2020, 2020, 3697253. [Google Scholar] [CrossRef]
- Stent, S.; Gherardi, R.; Stenger, B.; Soga, K.; Cipolla, R. Visual change detection on tunnel linings. Mach. Vis. Appl. 2016, 27, 319–330. [Google Scholar] [CrossRef]
- Panella, F.; Loo, Y.; Kaushik, A.; Gonzalez, D.; Ollerhead, R.; Boehm, J. Smart Image Based Technology and Deep Learning for Tunnel Inspection and Asset Management. In Proceedings of the 16th World Conference Of the Associated Research Centers for the Urban Underground Space, Integrated Underground Solutions for Compact Metropolitan Cities, Hong Kong, China, 5–7 November 2018. [Google Scholar]
- Attard, L.; Debono, C.J.; Valentino, G.; Di Castro, M. Vision-based change detection for inspection of tunnel liners. Automat. Constr. 2018, 91, 142–154. [Google Scholar] [CrossRef]
- Attard, L.; Debono, C.J.; Valentino, G.; Di Castro, M. Vision-based tunnel lining health monitoring via bi-temporal image comparison and decision-level fusion of change maps. Sensors 2021, 21, 4040. [Google Scholar] [CrossRef] [PubMed]
- Huang, H.; Zhao, S.; Zhang, D.; Chen, J. Deep learning-based instance segmentation of cracks from shield tunnel lining images. Struct. Infrastruct. E 2020, 2020. [Google Scholar] [CrossRef]
- Huang, Z.; Fu, H.; Fan, X.D.; Meng, J.H. Rapid surface damage detection equipment for subway tunnels based on machine vision systems. J. Infrastruct. Syst. 2021, 27, 04020047. [Google Scholar] [CrossRef]
- Farahani, B.V.; Barros, F.; Sousa, P.J.; Cacciari, P.P.; Tavares, P.J.; Futai, M.M.; Moreira, P. A coupled 3D laser scanning and digital image correlation system for geometry acquisition and deformation monitoring of a railway tunnel. Tunn. Undergr. Space Technol. 2019, 91, 102995. [Google Scholar] [CrossRef]
- Chen, X.; Qin, F.; Xia, C.; Bao, J.; Huang, Y.; Zhang, X. An innovative detection method of high-speed railway track slab supporting block plane based on point cloud data from 3D scanning technology. Appl. Sci. 2019, 9, 3345. [Google Scholar] [CrossRef] [Green Version]
- Zhou, Y.; Wang, S.; Mei, X.; Yin, W.; Lin, C.; Hu, Q.; Mao, Q. Railway tunnel clearance inspection method based on 3d point cloud from mobile laser scanning. Sensors 2017, 17, 2055. [Google Scholar] [CrossRef] [PubMed]
- Sanchez-Rodriguez, A.; Riveiro, B.; Soilan, M.; Gonzalez-deSantos, L.M. Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets. Automat. Constr. 2018, 96, 171–179. [Google Scholar] [CrossRef]
- Zhou, M.; Cheng, W.; Huang, H.; Chen, J. A novel approach to automated 3d spalling defects inspection in railway tunnel linings using laser intensity and depth information. Sensors 2021, 21, 5725. [Google Scholar] [CrossRef]
- Lu, P.; Tang, C. Application of mobile 3D laser scanning technology in deformation monitoring of subway tunnels. Bull. Surv. Mapp. 2020, 160, 155–157. [Google Scholar]
- Huang, H.; Zhang, D. Non-intrusive inspection and real-time monitoring for tunnel structural resilience. In Proceedings of the 2016 International Workshop on Resiliency of Urban Tunnels, Reston, VA, USA, 1 September 2016. [Google Scholar]
- Sun, H.; Xu, Z.; Yao, L.; Zhong, R.; Du, L.; Wu, H. Tunnel monitoring and measuring system using mobile laser scanning: Design and deployment. Remote Sens. 2020, 12, 730. [Google Scholar] [CrossRef] [Green Version]
- Zhou, B.; Xie, X. Analysis of coupled response of track-tunnel-ground system based on FEM and TMM. J. Vib. Shock 2012, 31, 147–153. [Google Scholar]
- Feng, L.; Yi, X.; Zhu, D.; Xie, X.; Wang, Y. Damage detection of metro tunnel structure through transmissibility function and cross correlation analysis using local excitation and measurement. Mech. Syst. Signal Process. 2015, 60–61, 59–74. [Google Scholar] [CrossRef]
- Luo, H.; Hu, M.M.; Liu, Y.T.; Zhu, H.P. Damage identification of shield tunnel based on wavelet packet energy spectrum. J. Build. Struct. 2018, 39 (Suppl. 2), 306–314. [Google Scholar]
- Zhang, K.N.; Zhang, L.M.; Wu, X.G.; Cheng, H.Y. Subway tunnel health alarming and warning method based on wavelet packet energy spectrum. Railw. Stand. Des. 2018, 62, 130–135. [Google Scholar]
- Sadhu, A.; Sony, S.; Friesen, P. Evaluation of progressive damage in structures using tensor decomposition-based wavelet analysis. J. Vib. Control 2019, 25, 2595–2610. [Google Scholar] [CrossRef]
- Roveri, N.; Carcaterra, A. Damage detection in structures under traveling loads by Hilbert–Huang transform. Mech. Syst. Signal Process. 2012, 28, 128–144. [Google Scholar] [CrossRef]
- Das, S.; Saha, P. Performance of hybrid decomposition algorithm under heavy noise condition for health monitoring of structure. J. Civ. Struct. Health 2020, 10, 679–692. [Google Scholar] [CrossRef]
- Avci, O.; Abdeljaber, O.; Kiranyaz, S.; Hussein, M.; Gabbouj, M.; Inman, D.J. A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications. Mech. Syst. Signal Process. 2021, 147, 107077. [Google Scholar] [CrossRef]
- Yang, Y.B.; Chang, K.C. Extracting the bridge frequencies indirectly from a passing vehicle: Parametric study. Eng. Struct. 2009, 31, 2448–2459. [Google Scholar] [CrossRef]
- Yang, Y.B.; Chang, K.C. Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique. J. Sound Vib. 2009, 322, 718–739. [Google Scholar] [CrossRef]
- Yang, Y.B.; Chang, K.C.; Li, Y.C. Filtering techniques for extracting bridge frequencies from a test vehicle moving over the bridge. Eng. Struct. 2013, 48, 353–362. [Google Scholar] [CrossRef]
- Yang, Y.; Chen, W.; Yu, H.; Chan, C.S. Experimental study of a hand-drawn cart for measuring the bridge frequencies. Eng. Struct. 2013, 57, 222–231. [Google Scholar] [CrossRef]
- Kong, X.; Cai, C.S.; Deng, L.; Zhang, W. Using Dynamic Responses of Moving Vehicles to Extract Bridge Modal Properties of a Field Bridge. J. Bridge Eng. 2017, 22, 04017018. [Google Scholar] [CrossRef]
- Wan, L. Research on Damage Degree Determination Theory and Online Dynamic Monitoring of Shield Tunnel. Ph.D. Thesis, Tongji University, Shanghai, China, 2016. [Google Scholar]
- Zhai, W.M. Unified model of vehicle-track vertical system and its coupling dynamics principle. J. China Railw. Soc. 1992, 14, 10–21. [Google Scholar]
- Zhang, Z.; Wang, Y.; Wang, K. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. J. Intell. Manuf. 2013, 24, 1213–1227. [Google Scholar] [CrossRef]
- Zhang, Y.; Liu, B.; Ji, X.; Huang, D. Classification of EEG signals based on autoregressive model and wavelet packet decomposition. Neural Process. Lett. 2017, 45, 365–378. [Google Scholar] [CrossRef]
- Hu, G.S.; Zhu, F.F.; Ren, Z. Power quality disturbance identification using wavelet packet energy entropy and weighted support vector machines. Expert Syst. Appl. 2008, 35, 143–149. [Google Scholar] [CrossRef]
- Huang, Q. Free Field Response and Settlement of Saturated Soft Soil Tunnel under Subway Vibration Load. Ph.D. Thesis, Tongji University, Shanghai, China, 2018. [Google Scholar]
- Xu, L. Study on Longitudinal Settlement of Soft Soil Shield Tunnel. Ph.D. Thesis, Tongji University, Shanghai, China, 2005. [Google Scholar]
Parameter NAME | Parameter Value | Unit of Parameter |
---|---|---|
Outer diameter of the tunnel model | 6.6 | m |
Inner diameter of the tunnel model | 5.9 | m |
Buried depth of the tunnel model | 14.7 | m |
Width of each tunnel segment | 1.2 | m |
Elastic modulus of the segment concrete (Label C55) | 34,500 | MPa |
Elastic modulus of the track bed concrete (Label C35) | 31,500 | MPa |
Poisson’s ratio of the segment concrete | 0.2 | - |
Poisson’s ratio of the track bed concrete | 0.2 | - |
Density of the segment concrete | 2500 | Kg/m3 |
Density of the track bed concrete | 2500 | Kg/m3 |
The length of soil around the tunnel model | 60 | m |
The width of the soil around the tunnel model | 60 | m |
The height of soil around the tunnel model | 42 | m |
Soil Layer Number 1 | Soil Name | Thickness (m) | Gravity Density (KN/m3) | Dynamic Modulus (MPa) | Poisson Ratio | Damping Ratio |
---|---|---|---|---|---|---|
① | Miscellaneous fill | 1.7 | 18 | 30 | 0.3 | 0.03 |
②3 | Gray clay silt | 8 | 18.6 | 32.16 | 0.29 | 0.03 |
③ | Muddy silty clay | 12 | 18 | 20.16 | 0.29 | 0.03 |
④ | Gray mucky clay | 8 | 17.1 | 25.62 | 0.31 | 0.03 |
⑤1 | Grey clay | 5 | 17.3 | 39.48 | 0.3 | 0.03 |
⑤2 | Gray sandy silt | 8 | 17.8 | 73.56 | 0.28 | 0.03 |
Category | Tunnel Diameter (m) | Elasticity Modulus (MPa) | Density (Kg/m3) | Tunnel Wall Thickness (m) |
---|---|---|---|---|
Prototype | 6.6 | 34,500 | 2500 | 0.35 |
Test model | 0.33 | 1725 | 900 | 0.0175 |
Similarity ratio | 20 | 20 | 2.78 | 20 |
Serial Number | Purpose of Analysis | Damage Type | Preset Damage Interval | Damage Level | Vehicle Speed | Number of Counterweights |
---|---|---|---|---|---|---|
1 | Single damage verification | Health | / | / | 0.503 m/s | 12 |
2 | Additional mass damage | 2 | 1 | 0.503 m/s | 12 | |
3 | Additional mass damage | 2 | 2 | 0.503 m/s | 12 | |
4 | Additional mass damage | 2 | 3 | 0.503 m/s | 12 | |
5 | Stiffness damage | 2 | 1 | 0.503 m/s | 12 | |
6 | Stiffness damage | 2 | 2 | 0.503 m/s | 12 | |
7 | Stiffness damage | 2 | 3 | 0.503 m/s | 12 | |
8 | Two damage verification | Two additional mass damage | 2, 5 | 1, 3 | 0.503 m/s | 12 |
9 | Additional mass and stiffness damage | 2, 5 | 2, 2 | 0.503 m/s | 12 | |
10 | Two stiffness damage | 2, 5 | 1, 3 | 0.503 m/s | 12 | |
11 | Influence of train weight | Health | / | / | 0.503 m/s | 8 |
12 | Health | / | / | 0.503 m/s | 12 | |
13 | Health | / | / | 0.503 m/s | 16 | |
14 | Stiffness damage | 2 | 3 | 0.503 m/s | 8 | |
15 | Stiffness damage | 2 | 3 | 0.503 m/s | 12 | |
16 | Stiffness damage | 2 | 3 | 0.503 m/s | 16 | |
17 | Influence of train speed | Health | / | / | 0.251 m/s | 12 |
18 | Health | / | / | 0.741 m/s | 12 | |
19 | Stiffness damage | 2 | 3 | 0.251 m/s | 12 | |
20 | Stiffness damage | 2 | 3 | 0.741 m/s | 12 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, H.; Xie, X.; Zhang, Y.; Wang, Q. Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response. Sensors 2021, 21, 7197. https://doi.org/10.3390/s21217197
Li H, Xie X, Zhang Y, Wang Q. Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response. Sensors. 2021; 21(21):7197. https://doi.org/10.3390/s21217197
Chicago/Turabian StyleLi, Hongqiao, Xiongyao Xie, Yonglai Zhang, and Qiang Wang. 2021. "Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response" Sensors 21, no. 21: 7197. https://doi.org/10.3390/s21217197
APA StyleLi, H., Xie, X., Zhang, Y., & Wang, Q. (2021). Theoretical, Numerical, and Experimental Study on the Identification of Subway Tunnel Structural Damage Based on the Moving Train Dynamic Response. Sensors, 21(21), 7197. https://doi.org/10.3390/s21217197