Structural Monitoring and Maintenance

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Damage detection of subway tunnel lining through statistical pattern recognition

  • Yu, Hong (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Zhu, Hong P. (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Weng, Shun (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Gao, Fei (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Luo, Hui (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology) ;
  • Ai, De M. (School of Civil Engineering and Mechanics, Huazhong University of Science and Technology)
  • Received : 2018.01.31
  • Accepted : 2018.03.28
  • Published : 2018.06.25
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Abstract

Subway tunnel structure has been rapidly developed in many cities for its strong transport capacity. The model-based damage detection of subway tunnel structure is usually difficult due to the complex modeling of soil-structure interaction, the indetermination of boundary and so on. This paper proposes a new data-based method for the damage detection of subway tunnel structure. The root mean square acceleration and cross correlation function are used to derive a statistical pattern recognition algorithm for damage detection. A damage sensitive feature is proposed based on the root mean square deviations of the cross correlation functions. X-bar control charts are utilized to monitor the variation of the damage sensitive features before and after damage. The proposed algorithm is validated by the experiment of a full-scale two-rings subway tunnel lining, and damages are simulated by loosening the connection bolts of the rings. The results verify that root mean square deviation is sensitive to bolt loosening in the tunnel lining and X-bar control charts are feasible to be used in damage detection. The proposed data-based damage detection method is applicable to the online structural health monitoring system of subway tunnel lining.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China, Wuhan Urban and Rural Construction Commission, Central Universities

References

  1. Balsamo, L., Betti, R. and Beigi, H. (2014), "A structural health monitoring strategy using cepstral features", J. Sound Vib., 333, 4526-4542. https://doi.org/10.1016/j.jsv.2014.04.062
  2. Bennett, P.J., Soga, K., Wassell, I., Fidler, P., Abe, K., Kobayashi, Y. and Vanicek, M. (2010), "Wireless sensor networks for underground railway applications: case studies in Prague and London", Smart Struct. Syst., 6(5-6), 619-639. https://doi.org/10.12989/sss.2010.6.5_6.619
  3. Carden, E.P. and Brownjohn, J.M.W. (2008), "ARMA modeled time-series classification for structural health monitoring of civil infrastructures", Mech. Syst. Signal Pr., 22, 295-314. https://doi.org/10.1016/j.ymssp.2007.07.003
  4. Chen, B. and Xia, Y. (2017), "Advanced technologies in disaster prevention and mitigation", Adv. Struct. Eng., 20(8), 1141-1142. https://doi.org/10.1177/1369433216685992
  5. Chen, B., Weng, S., Zhi, L.H. and Li, D.M. (2017), "Response control of a large transmission-tower line system under seismic excitations by using friction dampers", Adv. Struct. Eng., 20(8), 1155-1173. https://doi.org/10.1177/1369433216679999
  6. Farrar, C.R. and Iii, G.H.J. (1997), "System identification from ambient vibration measurements on a bridge", J. Sound Vib., 205(1), 1-18. https://doi.org/10.1006/jsvi.1997.0977
  7. Farrar, C.R. and Worden, K. (2013), Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons, Chichester, West Sussex, United Kingdom.
  8. Farrar, C.R., Doebling, S. and Nix, D. (2001), "Vibration-based structural damage identification", Philos. T. R. Soc. A., 359, 131-149. https://doi.org/10.1098/rsta.2000.0717
  9. Feng, L., Yi, X.H., Zhu, D.P., Xie, X.Y. and Wang, Y. (2015), "Damage detection of metro tunnel structure through transmissibility function and cross correlation analysis using local excitation and measurement", Mech. Syst. Signal Pr., 60-61, 59-74. https://doi.org/10.1016/j.ymssp.2015.02.007
  10. Li, P.Y., Chen, B., Xie, W.P and Xiao, X. (2015), "A comparative study on frequency sensitivity of a transmission tower", J. Sensors, 2015(2), 1-14.
  11. Mosavi, A.A., Dickey, D., Seracino, R. and Rizkalla, S. (2012), "Identifying damage locations under ambient vibrations utilizing vector autoregressive models and mahalanobis distances", Mech. Syst. Signal Pr., 26, 254-267. https://doi.org/10.1016/j.ymssp.2011.06.009
  12. Noman, A.S., Deeba, F. and Bagchi, A. (2013), "Health monitoring of structures using statistical pattern recognition techniques", J. Perform. Constr. Fac., 27(5), 575-584. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000346
  13. Pandit, S.M. and Wu, S.M. (1983), Time Series and System Analysis with Applications, John Wiley & Sons, New York, NY, USA.
  14. Richards, J.A. (1998), "Inspection, maintenance and repair of tunnels: international lessons and practice", Tunn. Undergr. Sp. Tech., 13(4), 369-375. https://doi.org/10.1016/S0886-7798(98)00079-0
  15. Rzeszucinski, P.J., Sinha, J.K., Edwards, R., Starr, A. and Allen, B. (2012), "Normalised root mean square and amplitude of sidebands of vibration response as tools for gearbox diagnosis", Strain, 48(6), 445-452. https://doi.org/10.1111/j.1475-1305.2012.00839.x
  16. Sekine, M., Tamura, T., Yoshida, M., Suda, Y., Kimura, Y., Miyoshi, H. Kijima, Y., Higashi, Y. and Fujimoto, T. (2013), "A gait abnormality measure based on root mean square of trunk acceleration", J. Neuroeng. Rehabil., 10(1), 1-7.
  17. Sohn, H. and Farrar, C.R. (2001), "Damage diagnosis using time series analysis of vibration signals", Smart Mater. Struct., 10(3), 446-451. https://doi.org/10.1088/0964-1726/10/3/304
  18. Sohn, H., Czarnecki, J.A. and Farrar, C.R. (2000), "Structural health monitoring using statistical process control", J. Struct. Eng., 126(11), 1356-1363. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:11(1356)
  19. Vecer, P., Kreidl, M. and Smid, R. (2005), "Condition indicators for gearbox condition monitoring systems", Acta Polytech., 45(6), 35-43.
  20. Xiao, H.T., Lou, S. and Ogai, H. (2015), "A novel bridge structure damage diagnosis algorithm based on post-nonlinear ICA and statistical pattern recognition", IEEJ T. Electr. Electr., 10(3), 287-300. https://doi.org/10.1002/tee.22085
  21. Yi, T.H., Li, H.N. and Gu, M. (2011), "Optimal sensor placement for structural health monitoring based on multiple optimization strategies", Struct. Des. Tall Spec., 20(7), 881-900. https://doi.org/10.1002/tal.712
  22. Yi, T.H., Li, H.N. and Gu, M. (2013), "Recent research and applications of GPS-based monitoring technology for high-rise structures", Struct. Control Health., 20(5), 649-670. https://doi.org/10.1002/stc.1501
  23. Zhou, B., Xie, X.Y., Yang, Y.B. and Jiang, J.C. (2012), "A novel vibration-based structure health monitoring approach for the shallow buried tunnel", CMES-Comp. Model. Eng., 86(4), 321-348.
  24. Zhu, D.P., Yi, X.H., Wang, Y. and Sabra, K. (2010), "Structural damage detection through cross correlation analysis of mobile sensing data", Proceedings of the 5th World Conference on Structural Control and Monitoring, Tokyo, Japan, July.
  25. Zhu, H.P., Luo, H., Ai, D.M. and Wang, C. (2016), "Mechanical impedance-based technique for steel structural corrosion damage detection", Measurement, 88, 353-359. https://doi.org/10.1016/j.measurement.2016.01.041

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