Damage Identification Technique for Bridges Using Static and Dynamic Response

정적 및 동적 응답을 이용한 교량의 손상도 추정 기법

  • Published : 2005.06.01

Abstract

Load bearing structural members in a wide variety of applications accumulate damage over their service life. From a standpoint of both safety and performance, it is desirable to monitor the occurrence, location, and extent of such damage. Structures require complicated element models with a number of degrees of freedom in structural analysis. During experiment much effort and cost is needed for measuring structural parameters. The sparseness and errors of measured data have to be considered during the parameter estimation Of Structures. In this paper we introduces damage identification algorithm by a system identification(S.I) using static and dynamic response. To study the behaviour of the estimators in noisy environment Using Monte Carlo simulation and a data measured perturbation scheme is adopted to investigate the influence of measurement errors on identification results. The assessment result by static and dynamic response were compared, and the efficiency and applicabilities of the proposed algorithm are demonstrated through simulated static and dynamic responses of a truss bridge. The assessment results by each method were compared and we could observe that the 5.1 method is superior to the other conventional methods.

Keywords

damage identification;system identification;monte carlo simulation;perturbation scheme

References

  1. Banan M. R., and Hjelmstad K. D., Identification of structural systems from measured response, SRS report No. 579, Dept. of Civil Eng., Univ. of Illinois at Urbana-Champaign, May, 1993
  2. Loukas Papadopoulos and Ephrahim Garcia., Structural damage identification: A Probabilistic Approach, AIAA Journal, Vol. 36, No. 11, November, pp. 2137-2145, 1998 https://doi.org/10.2514/2.318
  3. Duan W., and Achintya Haldar., System identification with limited observations and without input, Journal of Engineering Mechanics, Vol. 123, No. 5, May, pp. 504-511, 1997 https://doi.org/10.1061/(ASCE)0733-9399(1997)123:5(504)
  4. Li, Y. Y., Yam, L. H., Sensitivity analysis of sensor location for vibration control and damage detection of thin-plate systems, Journal of Sound and Vibration, Vol. 240, No. 4, pp. 623-636, 2001 https://doi.org/10.1006/jsvi.2000.3265
  5. Shin, S, B., Comparison of input residual and output residual schemes in parameter estimation of structural system, Dept. of Civil Engineering, Univ. of Illinois at Urban-champaign, 1991
  6. Yun, C. B., and Bahng E. Y., 'Substructural identification using neural networks', Computers and Structures 77, pp. 41-52, 2000 https://doi.org/10.1016/S0045-7949(99)00199-6
  7. Lee, H. S., Kim, Y. H., Park C. J., and Park, H. W., A new spatial regularization scheme for the identification of the geometric shape of an inclusion in a finite body, International Journal for Numerical Methods in Engineering, Vol. 43, pp. 973-992, 1999