DOI QR코드

DOI QR Code

Covariance-driven wavelet technique for structural damage assessment

  • Sun, Z. (State Key Laboratory for Disaster Reduction in Civil Engineering, Tongji University) ;
  • Chang, C.C. (Department of Civil Engineering, Hong Kong University of Science and Technology)
  • Received : 2005.03.14
  • Accepted : 2005.12.07
  • Published : 2006.04.25

Abstract

In this study, a wavelet-based covariance-driven system identification technique is proposed for damage assessment of structures under ambient excitation. Assuming the ambient excitation to be a white-noise process, the covariance computation is shown to be able to separate the effect of random excitation from the response measurement. Wavelet transform (WT) is then used to convert the covariance response in the time domain to the WT magnitude plot in the time-scale plane. The wavelet coefficients along the curves where energy concentrated are extracted and used to estimate the modal properties of the structure. These modal property estimations lead to the calculation of the stiffness matrix when either the spectral density of the random loading or the mass matrix is given. The predicted stiffness matrix hence provides a direct assessment on the possible location and severity of damage which results in stiffness alteration. To demonstrate the proposed wavelet-based damage assessment technique, a numerical example on a 3 degree-of-freedom (DOF) system and an experimental study on a three-story building model, which are all under a broad-band excitation, are presented. Both numerical and experimental results illustrate that the proposed technique can provide an accurate assessment on the damage location. It is however noted that the assessment of damage severity is not as accurate, which might be due to the errors associated with the mode shape estimations as well as the assumption of proportional damping adopted in the formulation.

Keywords

References

  1. Chang, C.C., Sun, Z. and Li, N. (2003), 'Identification of structural dynamic properties using wavelet transform', The First International Conference on Structural Health Monitoring and Intelligent Infrastructure, November 13-15, 2003, Tokyo, Japan
  2. Delprat, N., Escudie, B., Guillemain, P., Kronland-Martinet, R., Tchamitchian, P. and Torresani, B. (1992), 'Asymptotic wavelet and Gabor analysis: Extraction of instantaneous frequencies', IEEE Trans. Inform. Theory, 38:2, 644-664 https://doi.org/10.1109/18.119728
  3. Kijewski, T. and Kareem, A. (2003), 'Wavelet transform for system identification in civil engineering', Computer-Aided Civil and Infrastructure Engineering, 18, 339-355 https://doi.org/10.1111/1467-8667.t01-1-00312
  4. Piombo, B. A. D., Fasana, A., Marchesiello, S. and Ruzzene, M. (2000), 'Modelling and identification of the dynamic response of a supported bridge', Mechanical Systems and Signal Processing, 14(1), 75-89 https://doi.org/10.1006/mssp.1999.1266
  5. Peeters, B. and Roeck, G.D. (2001). 'Stochastic system identification for operational modal analysis: a review.' J. Dynamic Systems, Measurement, and Control, 123, 659-667 https://doi.org/10.1115/1.1410370
  6. Ruzzene, M., Fasana, A., Garibaldi, L. and Piombo, B. (1997), 'Natural frequencies and dampings identification using wavelet transform: application to real data', Mechanical Systems and Signal Processing, 11:2, 207-218 https://doi.org/10.1006/mssp.1996.0078
  7. Staszewski, W. J. (1997), 'Identification of damping in MDOF systems using time-scale decomposition', J. Sound Vib., 203(2), 283-305 https://doi.org/10.1006/jsvi.1996.0864

Cited by

  1. Wavelet-based structural modal parameter identification vol.20, pp.2, 2013, https://doi.org/10.1002/stc.474
  2. Analysis of dynamic of two-phase flow in small channel based on phase space reconstruction combined with data reduction sub-frequency band wavelet vol.23, pp.6, 2015, https://doi.org/10.1016/j.cjche.2014.11.031
  3. Two-Stage Covariance-Based Multisensing Damage Detection Method vol.143, pp.3, 2017, https://doi.org/10.1061/(ASCE)EM.1943-7889.0001053
  4. Experimental investigation on multi-objective multi-type sensor optimal placement for structural damage detection pp.1741-3168, 2018, https://doi.org/10.1177/1475921718785182