DOI QR코드

DOI QR Code

Structural health monitoring of Canton Tower using Bayesian framework

  • Kuok, Sin-Chi (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau) ;
  • Yuen, Ka-Veng (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau)
  • 투고 : 2012.01.13
  • 심사 : 2012.03.15
  • 발행 : 2012.10.25

초록

This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.

키워드

참고문헌

  1. Askegaard, V. and Mossing, P. (1988), "Long term observation of RC-bridge using changes in natural stiffness parameters versus ambient conditions frequencies", Nord. Concr. Res., 7, 20-27.
  2. Beck, J.L. (2010), "Bayesian system identification based on probability logic", Struct.Health Monit., 17(7), 825-847. https://doi.org/10.1002/stc.424
  3. 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)
  4. Beck, J.L. and Yuen, K.V. (2004), "Model selection using response measurements: Bayesian probabilistic approach", J. Eng. Mech. - ASCE, 130(2), 192-203. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:2(192)
  5. Box, G.E.P. and Tiao, G.C. (1992), Bayesian inference in statistical analysis, John Wiley and Sons, New York.
  6. Brownjohn, J.M.W. (2007), "Structural health monitoring of civil infrastructure", Philos. T. R. Soc. A., 365(1851), 589-622. https://doi.org/10.1098/rsta.2006.1925
  7. Chen, W.H., Lu, Z.R., Lin, W., Chen, S.H., Ni, Y.Q., Xia, Y. and Liao, W.Y. (2011), "Theoretical and experimental modal analysis of the Guangzhou new TV tower", Eng. Struct., 33(12), 3628-3646. https://doi.org/10.1016/j.engstruct.2011.07.028
  8. Ching, J. and Beck, J.L. (2004), "Bayesian analysis of the phase II IASC-ASC structural health monitoring experimental benchmark data", J. Eng. Mech. - ASCE, 130(10), 1233-1244. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:10(1233)
  9. Clinton, J.F., Bradford, S.C., Heaton, T.H. and Favela, J. (2006), "The observed wander of the natural frequencies in a structure", B. Seismol. Soc. Am., 96(1), 237-257. https://doi.org/10.1785/0120050052
  10. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), Damage Identification and Health Monitoring of Structural and Mechanical Systems from Changes in Their Vibration Characteristics: A Literature Review, Los Alamos National Laboratory Report, LA-13070-MS.
  11. Farrar, C.R. and Doebling, S.W. (1997), An overview of modal-based damage identification methods, Los Alamos National Laboratory, Los Alamos, NM.
  12. Gaitanaros, S., Karaiskos, G., Papadimitriou, C. and Aravas, N. (2010), "A Bayesian methodology for crack identification in structures using strain measurements", Int. J. Reliability Saf., 4(2-3), 206-237. https://doi.org/10.1504/IJRS.2010.032446
  13. He, J.,Wu, X.P. and Yan, Z.C. (2011), "Anti-wind safety of Guangzhou new TV tower during construction", Appl. Mech. Mater., 94-96, 1912-1916. https://doi.org/10.4028/www.scientific.net/AMM.94-96.1912
  14. Heylen, W. and Janter, T. (1990), "Extensions of the modal assurance criterion", J. Vib. Acoust., 112(4), 468-472. https://doi.org/10.1115/1.2930130
  15. Jeary, A.P. (1986), "Damping in tall buildings-a mechanism and a predictor", Earthq. Eng. Struct. D., 14(5), 733-750. https://doi.org/10.1002/eqe.4290140505
  16. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2004), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech. - ASCE, 130(1), 3-15.
  17. Kareem, A. and Gurley, K. (1996), "Damping in structures: its evaluation and treatment of uncertainty", J. Wind Eng. Ind. Aerod., 59(2-3), 131-157. https://doi.org/10.1016/0167-6105(96)00004-9
  18. Katafygiotis, L.S. and Yuen, K.V. (2001), "Bayesian spectral density approach for modal updating using ambient data", Earthq. Eng. Struct. D., 30(8), 1103-1123. https://doi.org/10.1002/eqe.53
  19. Krishnaiah, P.R. (1976), "Some recent developments on complex multivariate distributions", J. Multivariate Anal., 6(1), 1-30. https://doi.org/10.1016/0047-259X(76)90017-8
  20. 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)
  21. Liu, C. and DeWolf, J.T. (2007), "Effect of temperature on modal variability of a curved concrete bridge under ambient loads", J. Struct. Eng. - ASCE, 133(12), 1742-1751. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:12(1742)
  22. Matlab (1994), Matlab User's Guide, The MathWorks, Inc., Natick, MA.
  23. Ni, Y.Q. and Zhou, H.F. (2010), "Guangzhou new TV tower: integrated structural health monitoring and vibration control", Proceedings of the 2010 Structure Congress, Orlando, USA, 3155-3164.
  24. Ni, Y.Q., Li, B., Lam, K.H., Zhu, D., Wang, Y., Lynch, J.P. and Law, K.H. (2011), "In-construction vibration monitoring of a super-tall structure using a long-range wireless sensing system", Smart Struct. Syst., 7(2), 83-102. https://doi.org/10.12989/sss.2011.7.2.083
  25. Ni, Y.Q., Xia, Y., Liao, W.Y. and Ko, J.M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou new TV tower", Struct. Health Monit., 16(1), 73-98. https://doi.org/10.1002/stc.303
  26. Ni, Y.Q., Xia, Y., Lin, W., Chen, W.H. and Ko, J.M. (2012), "SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data", Smart Struct. Syst., in this issue.
  27. Niu, Y., Kraemer, P. and Fritzen, C.P. (2011), "Operational modal analysis for the Guangzhou new TV tower", Proceedings of the 29th International Modal Analysis Conference, Jacksonville, Florida, USA.
  28. Sohn, H., Farrar, C.R., Hemez, F.M., Shunk, D.D., Stinemates, D.W. and Nadler, B.R. (2003), A Review of Structural Health Monitoring Literature: 1996-2001, Los Alamos National Laboratory Report LA-13976-MS.
  29. Tamura, Y. and Suganuma, S. (1996), "Evaluation of amplitude-dependent damping and natural frequency of buildings during strong winds", J. Wind Eng. Ind. Aerod., 59(2-3), 115-130. https://doi.org/10.1016/0167-6105(96)00003-7
  30. Vanik, M.W., Beck, J.L. and Au, S.K. (2000), "A 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)
  31. Xia, Y., Xu, Y.L., Wei, Z.L., Zhu, H.P. and Zhou, X.Q., (2011), "Variation of structural vibration characteristics versus nonuniform temperature distribution", Eng. Struct., 33(1), 146-153. https://doi.org/10.1016/j.engstruct.2010.09.027
  32. Ye, X., Yan, Q., Wang, W., Yu, X. and Zhu, T. (2011), "Output-only modal identification of Guangzhou new TV tower subject to different environment effects", Proceedings of the 6th International Workshop on Advanced Smart Materials and Smart Structures Technology, July.
  33. Yuen, K.V. (2010a), Bayesian methods for structural dynamics and civil engineering, John Wiley & Sons, New York.
  34. Yuen, K.V. (2010b), "Recent developments of Bayesian model class selection and applications in civil engineering", Struct. Saf., 32(5), 338-346. https://doi.org/10.1016/j.strusafe.2010.03.011
  35. Yuen, K.V. and Beck, J.L. (2003), "Updating properties of nonlinear dynamical systems with uncertain input", J. Eng. Mech. - ASCE, 129(1), 9-20. https://doi.org/10.1061/(ASCE)0733-9399(2003)129:1(9)
  36. Yuen, K.V. and Katafygiotis, L.S. (2006), "Substructure identification and health monitoring using response measurement only", Comput. Aided Civ. Inf. Eng., 21(4), 280-291. https://doi.org/10.1111/j.1467-8667.2006.00435.x
  37. Yuen, K.V., Katafygiotis, L.S. and Beck, J.L. (2002), "Spectral density estimation of stochastic vector processes", Probab. Eng. Mech., 17(3), 265-272. https://doi.org/10.1016/S0266-8920(02)00011-5
  38. Yuen, K.V. and Kuok, S.C. (2011), "Bayesian methods for updating dynamic models", Appl. Mech. Rev., 64(1), 010802-1 -- 010802-18. https://doi.org/10.1115/1.4004479
  39. Zhou, H.F., Ni, Y.Q., Ko, J.M. and Wong, K.Y. (2008), "Modeling of wind and temperature effects on modal frequencies and analysis of relative strength of effect", Wind Struct., 11(1), 35-50. https://doi.org/10.12989/was.2008.11.1.035

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