Earthquakes and Structures

  • 제8권4호
  • /
  • Pages.935-956
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  • 2015
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  • 2092-7614(pISSN)
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  • 2092-7622(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Bayesian structural damage detection of steel towers using measured modal parameters

  • Lam, Heung-Fai (Department of Architecture and Civil Engineering, City University of Hong Kong) ;
  • Yang, Jiahua (Department of Architecture and Civil Engineering, City University of Hong Kong)
  • 투고 : 2014.04.04
  • 심사 : 2014.09.18
  • 발행 : 2015.04.25
⟨ 이전 논문 다음 논문 ⟩

초록

Structural Health Monitoring (SHM) of steel towers has become a hot research topic. From the literature, it is impractical and impossible to develop a "general" method that can detect all kinds of damages for all types of structures. A practical method should make use of the characteristics of the type of structures and the kind of damages. This paper reports a feasibility study on the use of measured modal parameters for the detection of damaged braces of tower structures following the Bayesian probabilistic approach. A substructure-based structural model-updating scheme, which groups different parts of the target structure systematically and is specially designed for tower structures, is developed to identify the stiffness distributions of the target structure under the undamaged and possibly damaged conditions. By comparing the identified stiffness distributions, the damage locations and the corresponding damage extents can be detected. By following the Bayesian theory, the probability model of the uncertain parameters is derived. The most probable model of the steel tower can be obtained by maximizing the probability density function (PDF) of the model parameters. Experimental case studies were employed to verify the proposed method. The contributions of this paper are not only on the proposal of the substructure-based Bayesian model updating method but also on the verification of the proposed methodology through measured data from a scale model of transmission tower under laboratory conditions.

키워드

과제정보

연구 과제 주관 기관 : Council of the Hong Kong Special Administrative Region

참고문헌

  1. Au, S.K. (2011a), "Fast Bayesian FFT method for ambient modal identification with separated modes", J. Eng. Mech., ASCE, 137(3), 214-226. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000213
  2. Au, S.K. (2011b), "Assembling mode shapes by least squares", Mech. Syst. Sig. Proc., 25(1), 163-179. https://doi.org/10.1016/j.ymssp.2010.08.002
  3. Au, S.K., Ni, Y.C., Zhang, F.L. and Lam, H.F. (2012), "Full-scale dynamic testing and modal identification of a coupled floor slab system", Eng. Struct., 37, 167-178. https://doi.org/10.1016/j.engstruct.2011.12.024
  4. Au, S.K and Zhang, F.L. (2012), "Ambient modal identification of a primary-secondary structure using fast Bayesian FFT approach", Mech. Syst. Signal Pr., 28, 280-296. https://doi.org/10.1016/j.ymssp.2011.07.007
  5. Beck, J.L. (1978), "Determining models of structures from earthquake records", Earthquake Engineering Research Laboratory, California Institute of Technology, Pasadena.
  6. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties. I: Bayesian statistical framework", J. Eng. Mech., ASCE, 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  7. Burdekin, F.M. (1993), "Nondestructive testing of welded structural steelwork", Proc. Inst. Civ. Eng. Struct., 99(1), 89-95. https://doi.org/10.1680/istbu.1993.22557
  8. ChinaFotoPress/Getty Images AsiaPac (2011), Eight Dead in Sichuan Transmission Tower Collapse, http://www.zimbio.com/pictures/2FVBZhPqCwD/Eight+Dead+Sichuan+Transmission+Tower+Collapse/hOm95DMn5qb.
  9. Magix (2008), Energy deregulation forces wide scale distributed energy, http://mgx.com/blogs/2008/03/30/energy-deregulation-forces-wide-scale-distributed-energy/.
  10. Katafygiotis, L.S. and Beck, J.L. (1998), "Updating models and their uncertainties. II: Model identifiability", J. Eng. Mech., ASCE, 124(4), 463-467. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(463)
  11. Lam, H.F., Katafygiotis, L.S. and Mickleborough, N.C. (2004), "Application of a statistical model updating approach on Phase I of the IASC-ASCE structural health monitoring benchmark study", J. Eng. Mech., ASCE, 130(1), 34-48. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(34)
  12. Lam, H.F., Ng, C.T. and Leung, A.Y.T. (2008), "Multicrack detection on semirigidly connected beams utilizing dynamic data", J. Eng. Mech., ASCE, 134(1), 90-99. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:1(90)
  13. Lam, H.F. and Ng, C.T. (2008), "The selection of pattern features for structural damage detection using an extended Bayesian ANN algorithm", Eng. Struct., 30(10), 2762-2770. https://doi.org/10.1016/j.engstruct.2008.03.012
  14. Lam, H.F. and Yin, T. (2011), "Dynamic reduction-based structural damage detection of transmission towers: Practical issues and experimental verification", Eng. Struct., 33(5), 1459-1478. https://doi.org/10.1016/j.engstruct.2011.01.009
  15. Li, X.Y. and Law, S.S. (2010), "Matrix of covariance of covariance of acceleration responses for damage detection from ambient vibration measurements", Mech. Syst. Signal Pr., 24(4), 945-956. https://doi.org/10.1016/j.ymssp.2009.10.007
  16. Kassimali, A. (2011), Matrix analysis of structures, Cengage Learning.
  17. Kim, J.H., Jeon, H.S. and Lee, C.W. (1992), "Application of the modal assurance criteria for detecting and locating structural faults", Proceedings of the 10th International Modal Analysis Conference, SEM Society for Experimental Mechanics Inc.
  18. O'Callahan, J., Avitabile, P. and Riemer, R. (1989), "System equivalent reduction expansion process (SEREP)", Proceedings of the 7th international modal analysis conference, Schnectady, Union College, NY, 1, 29-37.
  19. Papadimitriou, C. and Papadioti, D.C. (2013), "Component mode synthesis techniques for finite element model updating", Comput. Struct., 126, 15-28. https://doi.org/10.1016/j.compstruc.2012.10.018
  20. Popovics, J.S. and Rose, J.L. (1994), "A survey of developments in ultrasonic NDE of concrete", Transactions on Ferroelectrics and Freq. Control, IEEE, 44(1), 140-143.
  21. Vanik, M.W., Beck, J.L. and Au, S.K. (2000), "Bayesian probabilistic approach to structural health monitoring", J. Eng. Mech., ASCE, 126(7), 738-745. https://doi.org/10.1061/(ASCE)0733-9399(2000)126:7(738)
  22. Yin, T., Lam, H.F., Chow, H.M. and Zhu, H.P. (2009), "Dynamic reduction-based structural damage detection of transmission tower utilizing ambient vibration data", Eng. Struct., 31(9), 2009-2019. https://doi.org/10.1016/j.engstruct.2009.03.004
  23. Yaglom, A.M. (1987), Correlation theory of stationary and related random functions, Springer-Verlag, Berlin, Germany.
  24. Yang, J.N., Lei, Y., Lin, S. and Huang, N. (2004), "Hilbert-Huang based approach for structural damage detection", J. Eng. Mech., 130(1), 85-95. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(85)
  25. Yuen, K.V., Katafygiotis, L.S. and Beck, J.L. (2002), "Spectral density estimation of stochastic vector processes", Prob. Eng. Mech., 17(3), 265-272. https://doi.org/10.1016/S0266-8920(02)00011-5
  26. Zhong, S.C. and Oyadiji, S.O. (2011), "Detection of cracks in simply-supported beams by continuous wavelet transform of reconstructed modal data", Comput. Struct., 89(1-2), 127-148. https://doi.org/10.1016/j.compstruc.2010.08.008
  27. Zimmerman, D.C. and Kaouk, M. (1994), "Structural damage detection using a minimum rank update theory", J. Vib. Acoust., 116, 222-231. https://doi.org/10.1115/1.2930416

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  14. Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection 2018, https://doi.org/10.1002/stc.2140
  15. An efficient adaptive sequential Monte Carlo method for Bayesian model updating and damage detection pp.15452255, 2018, https://doi.org/10.1002/stc.2260
  16. A procedure for the identification of multiple cracks on beams and frames by static measurements vol.25, pp.8, 2018, https://doi.org/10.1002/stc.2194
  17. Damage Identification of Periodically-Supported Structures Following the Bayesian Probabilistic Approach pp.1793-6764, 2018, https://doi.org/10.1142/S021945541940011X
  18. Dynamic Property Evaluation of a Long-Span Cable-Stayed Bridge (Sutong Bridge) by a Bayesian Method pp.1793-6764, 2018, https://doi.org/10.1142/S0219455419400108
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  20. Three-dimensional structural health monitoring based on multiscale cross-sample entropy vol.12, pp.6, 2015, https://doi.org/10.12989/eas.2017.12.6.673
  21. OMA of model steel structure retrofitted with CFRP using earthquake simulator vol.12, pp.6, 2017, https://doi.org/10.12989/eas.2017.12.6.689
  22. GFRP retrofitting effect on the dynamic characteristics of model steel structure vol.28, pp.2, 2015, https://doi.org/10.12989/scs.2018.28.2.223
  23. Vibration Anatomy and Damage Detection in Power Transmission Towers with Limited Sensors vol.20, pp.6, 2015, https://doi.org/10.3390/s20061731
  24. Modified gravitational search algorithm and its application to structural damage detection vol.1635, pp.None, 2015, https://doi.org/10.1088/1742-6596/1635/1/012018
  25. Role of Sensors in Error Propagation with the Dynamic Constrained Observability Method vol.21, pp.9, 2015, https://doi.org/10.3390/s21092918