Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Online damage detection using pair cointegration method of time-varying displacement

  • Zhou, Cui (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Li, Hong-Nan (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Li, Dong-Sheng (Faculty of Infrastructure Engineering, Dalian University of Technology) ;
  • Lin, You-Xin (Guangdong Electrical Company) ;
  • Yi, Ting-Hua (Faculty of Infrastructure Engineering, Dalian University of Technology)
  • Received : 2012.07.12
  • Accepted : 2012.12.26
  • Published : 2013.09.25
⟨ Previous Next ⟩

Abstract

Environmental and operational variables are inevitable concerns by researchers and engineers when implementing the damage detection algorithm in practical projects, because the change of structural behavior could be masked by the conditions in a large extent. Thus, reliable damage detection methods should have a virtue of immunity from environmental and operational variables. In this paper, the pair cointegration method was presented as a novel way to remove the effect of environmental variables. At the beginning, the concept and procedure of this approach were introduced, and then the theoretical formulation and numerical simulations were put forward to illustrate the feasibility. The jump exceeding the control limit in the residual indicates the occurrence of damage, while the direction and magnitude imply the most potential damage location. In addition, the simulation results show that the proposed method has strong ability to resist the noise.

Keywords

References

  1. Askegaard, V. and Mossing, P. (1988), "Long term observation of RC-bridge using changes in natural frequencies", Nord. Concrete Federation, 7, 20-27.
  2. Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994), Time series analysis-forecasting and control, 3rd Ed, Prentice-Hall: Englewood Cliffs, NJ.
  3. Chen, Q., Kruger, U. and Leung, A. (2009), "Cointegration testing method for monitoring nonstationary processes", Ind. Eng. Chem. Res., 48(7), 3533-3543. https://doi.org/10.1021/ie801611s
  4. Cross, E.J., Worden, K. and Chen, Q. (2011), "Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data", Proc. R. Soc. A., 467, 2712-2732. https://doi.org/10.1098/rspa.2011.0023
  5. Code for Design of Concrete Structures (GB 500102002), Beijing, 2002.
  6. Dewangan, U.K. (2011), "Structural damage existence prediction with few measurements", Int. J. Eng. Sci. Technol., 3 (10), 7587-7597.
  7. Dickey, D.A. and Fuller, W.A. (1979), "Distributions of the estimators for auto-regressive time series with a unit root", J. Am. Stat. Assoc., 74, 427-431.
  8. Dickey, D.A. and Fuller, W. (1981), "Likelihood ratio statistics for autoregressive time series with a unit root", Econometrica, 49, 1057-1072. https://doi.org/10.2307/1912517
  9. Doebling, S.W. and Farrar, C.R. (1997), "Using statistical analysis to enhance modal-based damage identification", Proceedings of the DAMAS 97: structural damage assessment using advanced signal processing procedures, University of Sheffield, UK.
  10. Doebling, S.W., Farrar, C.R. and Prime, M.B. (1998), "A summary review of vibration-based identification methods", Shock Vib., 30(2), 91-105. https://doi.org/10.1177/058310249803000201
  11. Engle, R.F. and Granger, C.W.J. (1987), "Cointegration and error-correction: representation, estimation and testing", Econometrica, 55, 251-276. https://doi.org/10.2307/1913236
  12. Fan, W. and Qiao, P.Z. (2011), "Vibration-based damage identification methods: a review and comparative study", Struct Health Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419
  13. Figueiredo, E., Park, G., Farrar, C.R., Worden, K. and Figueiras, J. (2010), "Machine learning algorithms for damage detection under operational and environmental variability", Struct Health Monit., 10(6), 559-572.
  14. Fuller, W. (1996), Introduction to statistical time series, Wiley-Interscience, New York.
  15. Fritzen, C.R., Mengelkamp, G. and Guemes, A. (2003), "Elimination of temperature effects on damage detection within a smart structure concept", Proceedings of the 4th Int. Workshop on Structural Health Monitoring, Stanford University, CA.
  16. Gao, T. M. (2009), Econometrical analysis methods and modelling, Qinghua Press, Beijing, China.
  17. Ghrib, F., Li, L. and Wilbur, P. (2012), "Damage identification of Euler-Bernoulli beams using static responses", J. Eng. Mech. - ASCE., 135(5), 405-415.
  18. Johansen, S. (1988), "Statistical analysis of cointegrating vectors", J. Econom. Dyn. Control, 12(2-3), 231-254. https://doi.org/10.1016/0165-1889(88)90041-3
  19. Johansen, S. and Juselius, K. (1990), "Maximum likelihood estimation and inference on cointegration with applications to the demand for money", Oxford Bull. Econom. Stat., 52(2), 169-210. https://doi.org/10.1111/j.1468-0084.1990.mp52002003.x
  20. Kim, C.Y., Jung, D.S., Kim, N.S., Kwon, S.D. and Feng, M.Q. (2003), "Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration", Earthq. Eng. Eng. Vib., 2(1), 109-115. https://doi.org/10.1007/BF02857543
  21. Lee, E.T. and Eun, H.C. (2008), "Damage detection of damaged beam by constrained displacement curvature", J. Mech. Sci. Technol., 22(6), 1111-1120. https://doi.org/10.1007/s12206-008-0310-3
  22. Li, Y.H. (2009). Study on structural modeling and damage identification methods of cracked beams, M.S. thesis, Dalian Univ. of Technol., Dalian, China.
  23. Loh, C.H., Chen, C.H. and Hsu, T.Y. (2011), "Application of advanced statistical methods for extracting long-term trends in static monitoring data from an arch dam", Struct Health Monit., 10(6), 587-601. https://doi.org/10.1177/1475921710395807
  24. Monson, H. (1996), Statistical digital signal processing and modeling, John Wiley & Sons, Manhattan.
  25. Moorty, S.S. and Roeder, C.W. (1992), "Temperature-dependent bridge movements", J. Struct. Eng.- ASCE, 118(4), 1090-1105. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:4(1090)
  26. Peeters, B. and De Roeck, G. (2000), "One year monitoring of the Z24-bridge: Environmental influences versus damage events", Proceedings of the IMAC-XVIII, San Antonio, TX.
  27. Sohn, H. (2007), "Effects of environmental and operational variability on structural health monitoring", Philos. T. R. Soc. A., 365, 539-560. https://doi.org/10.1098/rsta.2006.1935
  28. Wang, Y.L., Liu, X.L. and Fang, C.Q. (2012), "Damage detection of bridges by using displacement data of two symmetrical points", J. Perform. Constr. Fac., 25(3), 300-311.
  29. Yan, A.M., Kerschen, G., De Boe, P. and Golinval, J.C. (2005), "Structural damage diagnosis under varying environmental conditions - part I: a linear analysis", Mech. Syst. Signal Pr., 19, 847-864. https://doi.org/10.1016/j.ymssp.2004.12.002
  30. Zhou, H.F, Ni, Y.Q. and Ko, J.M. (2011), "Elimination temperature effect in vibration-based structural damage detection", J. Eng. Mech.- ASCE, 137(12), 785-796. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000273

Cited by

  1. Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures vol.14, pp.4, 2014, https://doi.org/10.12989/sss.2014.14.4.699
  2. Overall damage identification of flag-shaped hysteresis systems under seismic excitation vol.16, pp.1, 2015, https://doi.org/10.12989/sss.2015.16.1.163
  3. Dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator vol.17, pp.4, 2016, https://doi.org/10.12989/sss.2016.17.4.647
  4. Damage identification of a large cable-stayed bridge with novel cointegrated Kalman filter method under changing environments vol.25, pp.5, 2018, https://doi.org/10.1002/stc.2152
  5. Wavelet-like convolutional neural network structure for time-series data classification vol.22, pp.2, 2018, https://doi.org/10.12989/sss.2018.22.2.175