Smart Structures and Systems

  • 제6권9호
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  • Pages.991-1005
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  • 2010
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Locating the damaged storey of a building using distance measures of low-order AR models

  • Xing, Zhenhua (Department of System Design Engineering, Keio University) ;
  • Mita, Akira (Department of System Design Engineering, Keio University)
  • 투고 : 2009.12.03
  • 심사 : 2010.03.26
  • 발행 : 2010.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

The key to detecting damage to civil engineering structures is to find an effective damage indicator. The damage indicator should promptly reveal the location of the damage and accurately identify the state of the structure. We propose to use the distance measures of low-order AR models as a novel damage indicator. The AR model has been applied to parameterize dynamical responses, typically the acceleration response. The premise of this approach is that the distance between the models, fitting the dynamical responses from damaged and undamaged structures, may be correlated with the information about the damage, including its location and severity. Distance measures have been widely used in speech recognition. However, they have rarely been applied to civil engineering structures. This research attempts to improve on the distance measures that have been studied so far. The effect of varying the data length, number of parameters, and other factors was carefully studied.

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참고문헌

  1. Akaike, H. (1974), "A new look at the statistical model identification", IEEE T. Automat. Contr., 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Assaleh, K.T. and Mammone, R.J. (1994), "New LP-derived features for speaker identification", IEEE T. Speech Audi. P., 2(4), 630-638. https://doi.org/10.1109/89.326621
  3. Bogert, B., Healy, M. and Tukey, J. (1963), "The quefrency alanysis of time series for echoes: cepstrum, pseudoautocovariance, cross-cepstrum, and saphe cracking", Proc. Symp. on Time Series Anal., Chapter 15, 209-243.
  4. Cawley, P. and Adams, R.D. (1979), "The location of defects in structures from measurements of natural frequencies", J. Strain Anal. Eng. Des., 14(2), 49-57. https://doi.org/10.1243/03093247V142049
  5. Doebling, S.W. and Farrar, C.R. (1997), "Using statistical analysis to enhance modal-based damage identification", Proceedings of the DAMAS '97, Sheffield, UK.
  6. Feng, M.Q. (2009), "Application of structural health monitoring in civil infrastructure", Smart Struct. Syst., 5(4), 469-482. https://doi.org/10.12989/sss.2009.5.4.469
  7. Itakura, F. and Umezaki, T. (1987), "Distance measure for speech recognition based on the smoothed group delay spectrum", Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '87), Nagoya University, Nagoya-shi, Japan, April.
  8. Kim, J.T., Park, J.H., Yoon, H.S. and Yi, J.H. (2007), "Vibration-based damage detection in beams using genetic algorithm", Smart Struct. Syst., 3(3), 263-280. https://doi.org/10.12989/sss.2007.3.3.263
  9. Kwon, S.J., Woo, S. and Shin, S. (2008), "Determination of optimal accelerometer locations using modal sensitivity for identifying a structure", Smart Struct. Syst., 4(5), 629-640. https://doi.org/10.12989/sss.2008.4.5.629
  10. Ljung, L. (1987), System identification: theory for the user, Prentice-Hall, Englewood Cliffs, NJ, USA.
  11. Martin, R.J. (2000), "A metric for ARMA processes", IEEE T. Signal Proces., 48(4), 1164-1170. https://doi.org/10.1109/78.827549
  12. Mayes, R. (1991), "Error localization using mode shapes: An application to a two link robot arm", Proceedings of the 10th International Modal Analysis Conference (IMAC), San Diego, CA, USA.
  13. Mita, A. (2003), Structural dynamics for health monitoring, SANKEISHA Co., Ltd.
  14. Oppenheim, A.V. and Schafer, R.W. (1975), Digital signal processing, Prentice-Hall, Englewood Cliffs, NJ, USA.
  15. Pandey, A.K., Biswas, M. and Samman, M.M. (1991), "Damage detection from changes in curvature mode shapes", J. Sound Vib., 145(2), 321-332. https://doi.org/10.1016/0022-460X(91)90595-B
  16. Quek, S.T. and Tua, P.S. (2008), "Using frequency response function and wave propagation for locating damage in plates", Smart Struct. Syst., 4(3), 343-365. https://doi.org/10.12989/sss.2008.4.3.343
  17. Stubbs, N. and Osegueda, R. (1990), "Global damage detection in solids- Experimental verification", Int. J. Anal. Exp. Modal Anal., 5(2), 81-97.
  18. Shin, S. and Oh, S.H. (2007), "Damage assessment of shear buildings by synchronous estimation of stiffness and damping using measured acceleration", Smart Struct. Syst., 3(3), 245-261. https://doi.org/10.12989/sss.2007.3.3.245
  19. Tohkura, Y. (1986), "A weighted cepstral distance measure for speech recognition", Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '86), Nippon Telegraph and Telephone Corporation, Tokyo, Japan, April.
  20. Wang, T., Saniie, J. and Jin, X. (1991), "Analysis of low-order autoregressive models for ultrasonic grain signal characterization", IEEE T. Ultrason. Ferr., 38(2), 116-124. https://doi.org/10.1109/58.68468
  21. West, W. (1986), "Illustration of the use of modal assurance criterion to detect structural changes in an orbiter test specimen", Proceedings of the 4th International Modal Analysis Conference, Los Angeles, CA, USA.
  22. Yoshimoto, R., Mita, A. and Morita, K. (2002), "Parallel identification of structural damages using vibration modes and sensor characteristics", J. Struct. Eng.-B, 48B, 487-492.
  23. Zhang, L., Quiong, W. and Link, M. (1998), "A structural damage identification approach based on element modal strain energy", Proceedings of the ISMA23, Noise and Vibration Engineering, Leuven, Belgium.
  24. Zheng, H. and Mita, A. (2007), "Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters", Smart Mater. Struct., 16(5), 1829-1836. https://doi.org/10.1088/0964-1726/16/5/038

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  3. STUDY ON OUTPUT ERROR OF SUBSTRUCTURAL SYSTEM INDUCED BY LOCAL DAMAGES OF STRUCTURES vol.81, pp.726, 2010, https://doi.org/10.3130/aijs.81.1187
  4. Structural nonlinear damage identification using penalty conversion index of GARCH model vol.12, pp.11, 2020, https://doi.org/10.1177/1687814020971173