Smart Structures and Systems

  • 제15권3호
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  • Pages.751-767
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  • 2015
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Probabilistic condition assessment of structures by multiple FE model identification considering measured data uncertainty

  • Kim, Hyun-Joong (Department of Civil and Environmental Engineering, Seoul National University) ;
  • Koh, Hyun-Moo (Department of Civil and Environmental Engineering, Seoul National University)
  • 투고 : 2014.10.17
  • 심사 : 2015.01.22
  • 발행 : 2015.03.25
⟨ 이전 논문 다음 논문 ⟩

초록

A new procedure is proposed for assessing probabilistic condition of structures considering effect of measured data uncertainty. In this procedure, multiple Finite Element (FE) models are identified by using weighting vectors that represent the uncertainty conditions of measured data. The distribution of structural parameters is analysed using a Principal Component Analysis (PCA) in relation to uncertainty conditions, and the identified models are classified into groups according to their similarity by using a K-means method. The condition of a structure is then assessed probabilistically using FE models in the classified groups, each of which represents specific uncertainty condition of measured data. Yeondae bridge, a steel-box girder expressway bridge in Korea, is used as an illustrative example. Probabilistic condition of the bridge is evaluated by the distribution of load rating factors obtained using multiple FE models. The numerical example shows that the proposed method can quantify uncertainty of measured data and subsequently evaluate efficiently the probabilistic condition of bridges.

키워드

참고문헌

  1. Beck, J.L. and Au, S.K. (2002), "Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation", J. Eng. Mech. -ASCE, 128(4), 380-391. https://doi.org/10.1061/(ASCE)0733-9399(2002)128:4(380)
  2. Bishop, C.M. (2006), Pattern Recognition And Machine Learning, New York: springer, NY, USA.
  3. Ding, C. and He, X. (2004), "K-means clustering via principal component analysis", Proceedings of the 21th International Conference on Machine Learning, ACM.
  4. Friswell, M. and Mottershead, J.E. (1995), Finite Element Model Updating In Structural Dynamics, Kluwer Academic Publishers, Dordrecht, The Netherlands.
  5. Govers, Y. and Link, M. (2010), "Stochastic model updating-Covariance matrix adjustment from uncertain experimental modal data", Mech. Syst. Signal Pr., 24(3), 696-706. https://doi.org/10.1016/j.ymssp.2009.10.006
  6. Goulet, J.A., Kripakaran, P. and Smith, I.F. (2010), "Multimodel structural performance monitoring", J. Struct. Eng. - ASCE, 136(10), 1309-1318. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000232
  7. Jung, S., Ok, S.Y. and Song, J. (2010), "Robust structural damage identification based on multi-objective optimization", Int. J. Numer. Meth.Eng,, 81(6), 786-804.
  8. Katafygiotis, L.S., Papadimitriou, C. and Lam, H.F. (1998), "A probabilistic approach to structural model updating", Soil Dyn. Earthq. Eng., 17(7), 495-507. https://doi.org/10.1016/S0267-7261(98)00008-6
  9. KCI (2010), Guide Specification for Assessment of Concrete bridges, Korea Concrete Institute, Seoul, Korea.
  10. Kim, H.J., Kim, H.K. and Park, J.Y. (2013), "Reliability-based evaluation of load carrying capacity for a composite box girder bridge", J. Civil Eng. - KSCE, 17(3), 575-583.
  11. Kim, H.J., Park, W., Koh, H.M. and Choo, J.F. (2013), "Identification of structural performance of a steel-box girder bridge using machine learning technique", Proceedings of the IABSE Symposium Report, 99(12), 1313-1320, International Association for Bridge and Structural Engineering.
  12. Kim, H.J., Park, W. and Koh, H.M. (2014), "Probabilistic performance assessment of highway bridges using operational monitoring data", Proceedings of IABSE Symposium Report, 102(9), 2650-2657, International Association for Bridge and Structural Engineering.
  13. Mares, C., Mottershead, J.E. and Friswell, M.I. (2006), "Stochastic model updating: part 1-theory and simulated example", Mech. Syst. Signal Pr., 20(7), 1674-1695. https://doi.org/10.1016/j.ymssp.2005.06.006
  14. MLTM (2012), Highway Bridge Design Code, Korea Ministry of Land, Transportation and Maritime Affairs, Seoul, Korea.
  15. Mottershead, J.E., Mares, C., James, S. and Friswell, M.I. (2006), "Stochastic model updating: part 2-application to a set of physical structures", Mech. Syst. Signal Pr., 20(8), 2171-2185. https://doi.org/10.1016/j.ymssp.2005.06.007
  16. Mottershead, J.E., Link, M. and Friswell, M.I. (2011), "The sensitivity method in finite element model updating: a tutorial", Mech. Syst. Signal Pr., 25(7), 2275-2296. https://doi.org/10.1016/j.ymssp.2010.10.012
  17. Mukhopadhyay, S., Lus, H. and Betti, R. (2014), "Modal parameter based structural identification using input-output data: Minimal instrumentation and global identifiability issues", Mech. Syst. Signal Pr., 45(2), 283-301. https://doi.org/10.1016/j.ymssp.2013.11.005
  18. Mukhopadhyay, S., Lus, H., Hong, A.L. and Betti, R. (2012), "Propagation of mode shape errors in structural identification", J. Sound Vib., 331(17), 3961-3975. https://doi.org/10.1016/j.jsv.2012.04.012
  19. Park, I., Amarchinta, H.K. and Grandhi, R.V. (2010), "A Bayesian approach for quantification of model uncertainty", Reliab. Eng. Syst. Safe., 95(7), 777-785. https://doi.org/10.1016/j.ress.2010.02.015
  20. Park, W., Kim, H. K. and Jongchil, P. (2012), "Finite element model updating for a cable-stayed bridge using manual tuning and sensitivity-based optimization", Struct. Eng. Int., 22(1), 14-19. https://doi.org/10.2749/101686612X13216060212870
  21. Robert-Nicoud, Y., Raphael, B. and Smith, I.F.C. (2000), "Decision support through multiple models and probabilistic search", Proceedings of the Construction Information Technology, Icelandic Building Research Institute, Reykjavik.
  22. Saitta, S., Raphael, B. and Smith, I.F. (2007), "A bounded index for cluster validity", Proceedings of the 5th International Conference MLDM 2007, Leipzig, Germany, July.
  23. Schueller, G.I. and Jensen, H.A. (2008), "Computational methods in optimization considering uncertainties-an overview", Comput. Method. Appl. M., 198(1), 2-13. https://doi.org/10.1016/j.cma.2008.05.004
  24. Soize, C., Capiez-Lernout, E., Durand, J.F., Fernandez, C. and Gagliardini, L. (2008), "Probabilistic model identification of uncertainties in computational models for dynamical systems and experimental validation", Comput. Method. Appl. M., 198(1), 150-163. https://doi.org/10.1016/j.cma.2008.04.007
  25. Steenackers, G. and Guillaume, P. (2006), "Finite element model updating taking into account the uncertainty on the modal parameters estimates", J. Sound Vib., 296(4), 919-934. https://doi.org/10.1016/j.jsv.2006.03.023