Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Investigation of modal identification and modal identifiability of a cable-stayed bridge with Bayesian framework

  • Kuok, Sin-Chi (Department of Civil and Environmental Engineering, Cornell University) ;
  • Yuen, Ka-Veng (Department of Civil and Environmental Engineering, Faculty of Science and Technology, University of Macau)
  • Received : 2015.07.13
  • Accepted : 2015.12.17
  • Published : 2016.03.25
⟨ Previous Next ⟩

Abstract

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

Keywords

References

  1. Au, S.K. and Zhang, F.L. (2012), "Ambient modal identification of a primary-secondary structure by Fast Bayesian FFT method", Mech. Syst. Signal Pr., 28, 280-296. https://doi.org/10.1016/j.ymssp.2011.07.007
  2. Au, S.K., Ni, Y.C., Zhang, F.L. and Lam, H.F. (2012a), "Full-scale dynamic testing and modal idnetification of a coupled floor slab system", Eng. Struct., 37, 167-178. https://doi.org/10.1016/j.engstruct.2011.12.024
  3. Au, S.K., Zhang, F.L. and Ni, Y.C. (2013), "Bayesian operational modal analysis: Theory, computation, practice", Comput. Struct., 126, 3-14. https://doi.org/10.1016/j.compstruc.2012.12.015
  4. Au, S.K., Zhang, F.L. and To, P. (2012b), "Field observations on modal properties of two tall buildings under strong wind", J. Wind Eng. Ind. Aerod., 101, 12-23. https://doi.org/10.1016/j.jweia.2011.12.002
  5. Balageas, D., Fritzen, C.P. and Guemes, A. (2010), Structural Health Monitoring: Wiley.
  6. Beck, J.L. (2010), "Bayesian system identification based on probability logic", Struct. Control. Health., 17(7), 825-847. https://doi.org/10.1002/stc.424
  7. 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)
  8. 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)
  9. Box, G.E.P. and Tiao, G.C. (1973), Bayesian Inference in Statistical Analysis, Reading, Mass.,: Addison-Wesley Pub. Co.
  10. 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
  11. Chang, P.C., Flatau, A. and Liu, S.C. (2003), "Review paper: health monitoring of civil infrastructure", Struct. Health. Monit., 2(3), 257-267. https://doi.org/10.1177/1475921703036169
  12. Chiachio, M., Chiachio, J., Rus, G. and Beck, J.L. (2014), "Predicting fatigue damage in composites: A Bayesian framework", Struct. Saf., 51, 57-68. https://doi.org/10.1016/j.strusafe.2014.06.002
  13. Chiu, C.F., Yan, W.M. and Yuen, K.V. (2012), "Reliability analysis of soil-water characteristics curve and its application to slope stability analysis", Eng. Geol., 135, 83-91.
  14. 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, No. LA--13070-MS, Los Alamos National Lab., NM.
  15. Grigoriu, M. (2012), Stochastic Systems: Uncertainty Quantification and Propagation, Springer Science & Business Media.
  16. Grigoriu, M. and Field, R. (2008), "A solution to the static frame validation challenge problem using Bayesian model selection", Comput. Method Appl. M., 197(29), 2540-2549. https://doi.org/10.1016/j.cma.2007.09.023
  17. Gull, S.F. (1988), Bayesian inductive inference and maximum entropy, In Maximum-Entropy and Bayesian Methods in Science and Engineering, Springer Netherlands.
  18. Haldar, A. (2013), Health Assessment of Engineered Structures: Ridges, Buildings and Other Infrastructures, World Scientific Publishing Company Incorporated.
  19. Hoi, K.I., Yuen, K.V. and Mok, K.M. (2009), "Prediction of daily averaged PM10 concentrations by statistical time-varying model", Atmos. Environ., 43(16), 2579-2581. https://doi.org/10.1016/j.atmosenv.2009.02.020
  20. Hoi, K.I., Yuen, K.V. and Mok, K.M. (2013),"Improvement of the multilayer perceptron for air quality modelling through an adaptive learning scheme", Comput. Genosci., 59, 148-155. https://doi.org/10.1016/j.cageo.2013.06.002
  21. Johnson, E., Lam, H., Katafygiotis, L. and Beck, J. (2004), "Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data", J. Eng. Mech.-ASCE, 130(1), 3-15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3)
  22. Karbhari, V.M. and Ansari, F. (2009), Structural Health Monitoring of Civil Infrastructure Systems, Elsevier Science.
  23. 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
  24. Ko, J. and Ni, Y. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  25. Kuok, S.C. and Yuen, K.V. (2012), "Structural health monitoring of Canton Tower using Bayesian framework", Smart Struct. Syst., 10(4-5), 375-391. https://doi.org/10.12989/sss.2012.10.4_5.375
  26. Kuok, S.C. and Yuen, K.V. (2013), "Structural health monitoring of a reinforced concrete building during the severe typhoon Vicente in 2012", Sci. World. J., Article ID 509350.
  27. Li, H. and Ou, J. (2011), "Structural health monitoring: From sensing technology stepping to health diagnosis", Procedia Eng., 14, 753-760. https://doi.org/10.1016/j.proeng.2011.07.095
  28. Li, S., Li, H., Liu, Y., Lan, C., Zhou, W. and Ou, J. (2014), "SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge", Struct. Control. Hlth., 21(2), 156-172. https://doi.org/10.1002/stc.1559
  29. Li, S., Suzuki, Y. and Noori, M. (2004), "Improvement of parameter estimation for non-linear hysteretic systems with slip by a fast Bayesian bootstrap filter", Int. J. Nonlin. Mech., 39(9), 1435-1445. https://doi.org/10.1016/j.ijnonlinmec.2004.02.005
  30. Mu, H.Q., Xu, R.R. and Yuen, K.V. (2014), "Seismic attenuation relationship with homogeneous and heterogeneous prediction-error variance models", Earthq. Eng. Eng. Vib., 13(1), 1-11.
  31. Mu, H.Q. and Yuen, K.V. (2015), "Novel outlier-resistant extended Kalman filter for robust online structural identification", J. Eng. Mech.-ASCE, 141(1), doi: 10.1061/(ASCE)EM.1943-7889.0000810.
  32. Ng, I.T., Yuen, K.V. and Dong, L. (2015), "Probabilistic real-time updating for geotechnical properties evaluation", Struct. Eng. Mech., 54(2), 363-378. https://doi.org/10.12989/sem.2015.54.2.363
  33. Ni, Y., Wang, Y. and Xia, Y. (2015), "Investigation of mode identifiability of a cable-stayed bridge: comparison from ambient vibration responses and from typhoon-induced dynamic responses", Smart Struct. Syst., 15(2), 447-468. https://doi.org/10.12989/sss.2015.15.2.447
  34. Ni, Y., Wong, K. and Xia, Y. (2011), "Health checks through landmark bridges to sky-high structures", Adv. Struct. Eng., 14(1), 103-119. https://doi.org/10.1260/1369-4332.14.1.103
  35. Ni, Y., Xia, Y., Liao, W. and Ko, J. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Control. Health., 16(1), 73-98. https://doi.org/10.1002/stc.303
  36. Papadimitriou, C., Fritzen, C. P., Kraemer, P. and Ntotsios, E. (2011), "Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering", Struct. Control. Health., 18(5), 554-573. https://doi.org/10.1002/stc.395
  37. Sohn, H. (2004), A Review of Structural Health Monitoring Literature: 1996-2001, Los Alamos National Laboratory.
  38. Sohn, H. and Law, K.H. (1997), "A Bayesian probabilistic approach for structure damage detection", Earthq. Eng. Struct. D., 26(12), 1259-1281. https://doi.org/10.1002/(SICI)1096-9845(199712)26:12<1259::AID-EQE709>3.0.CO;2-3
  39. Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Control. Health., 11(2), 91-124. https://doi.org/10.1002/stc.33
  40. Wong, K.Y. (2007), "Design of a structural health monitoring system for long-span bridges", Struct. Infrastruct. E., 3(2), 169-185. https://doi.org/10.1080/15732470600591117
  41. Yan, W.M., Yuen, K.V. and Yoon, G.L. (2009), "Bayesian probabilistic approach for the correlations of compression index for marine clays", J. Geotech. Geoenviron., 135(12), 1932-1940. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000157
  42. Yuen, K.V. (2010), Bayesian Methods for Structural Dynamics and Civil Engineering, John Wiley & Sons, Singapore, Hoboken, NJ.
  43. 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)
  44. Yuen, K.V., Katafygiotis, L.S. and Beck, J.L. (2002), "Spectral density estimation of stochastic vector processes", Probabilist. Eng. Mech., 17(3), 265-272. https://doi.org/10.1016/S0266-8920(02)00011-5
  45. Yuen, K.V. and Kuok, S.C. (2010), "Modeling of environmental influence in structural health assessment for reinforced concrete buildings", Earthq. Eng. Eng. Vib., 9(2), 295-306. https://doi.org/10.1007/s11803-010-0014-4
  46. Yuen, K.V. and Kuok, S.C. (2011), "Bayesian methods for updating dynamic models", Appl. Mech. Rev., 64(1), Article number 010802.
  47. Yuen, K.V. and Kuok, S.C. (2015), "Efficient Bayesian sensor placement algorithm for structural identification: a general approach for multi-type sensory systems", Earthq. Eng. Struct. D., 44(5), 757-774. https://doi.org/10.1002/eqe.2486
  48. Yuen, K.V. and Kuok, S.C. (2016), "Online updating and uncertainty quantification using nonstationary output-only measurement", Mech. Syst. Signal Pr., 66-67, 62-77. https://doi.org/10.1016/j.ymssp.2015.05.019
  49. Yuen, K.V., Liang, P.F. and Kuok, S.C. (2013), "Online estimation of noise parameters for Kalman filter", Struct. Eng. Mech., 47(3), 361-381. https://doi.org/10.12989/sem.2013.47.3.361
  50. Yuen, K.V. and Mu, H.Q. (2015), "Real-time system identification: an algorithm for simultaneous model class selection and parametric identification", Comput.-Aided Civ. Inf., 30(10), 785-801. https://doi.org/10.1111/mice.12146
  51. Zellner, A., Keuzenkamp, H.A. and McAleer, M. (2001), Simplicity, Inference and Modelling: Keeping it Sophisticatedly Simple, Cambridge University Press.
  52. Zhou, L., Yan, G., Wang, L. and Ou, J. (2013), "Review of benchmark studies and guidelines for structural health monitoring", Adv. Struct. Eng., 16(7), 1187-1206. https://doi.org/10.1260/1369-4332.16.7.1187

Cited by

  1. Entropy-Based Optimal Sensor Placement for Model Identification of Periodic Structures Endowed with Bolted Joints vol.32, pp.12, 2017, https://doi.org/10.1111/mice.12309
  2. Comparing Structural Identification Methodologies for Fatigue Life Prediction of a Highway Bridge vol.3, 2018, https://doi.org/10.3389/fbuil.2017.00073
  3. Relevance feature selection of modal frequency-ambient condition pattern recognition in structural health assessment for reinforced concrete buildings vol.8, pp.8, 2016, https://doi.org/10.1177/1687814016662228
  4. Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data vol.18, pp.9, 2018, https://doi.org/10.3390/s18093057
  5. Identifiability-Enhanced Bayesian Frequency-Domain Substructure Identification vol.33, pp.9, 2018, https://doi.org/10.1111/mice.12377
  6. Structural Damage Localization and Quantification Based on Additional Virtual Masses and Bayesian Theory vol.144, pp.10, 2018, https://doi.org/10.1061/(ASCE)EM.1943-7889.0001523
  7. Bayesian network-based modal frequency–multiple environmental factors pattern recognition for the Xinguang Bridge using long-term monitoring data pp.2048-4046, 2018, https://doi.org/10.1177/1461348418786520
  8. 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
  9. Data-Interpretation Methodologies for Practical Asset-Management vol.8, pp.2, 2016, https://doi.org/10.3390/jsan8020036
  10. Bayesian Nonparametric Modeling of Structural Health Indicators under Severe Typhoons and Its Application to Modeling Modal Frequency vol.32, pp.4, 2016, https://doi.org/10.1061/(asce)as.1943-5525.0001023
  11. Self‐calibrating Bayesian real‐time system identification vol.34, pp.9, 2016, https://doi.org/10.1111/mice.12441
  12. An efficient approach for model updating of a large-scale cable-stayed bridge using ambient vibration measurements combined with a hybrid metaheuristic search algorithm vol.25, pp.4, 2016, https://doi.org/10.12989/sss.2020.25.4.487
  13. Bayesian Model-Updating Using Features of Modal Data: Application to the Metsovo Bridge vol.9, pp.2, 2016, https://doi.org/10.3390/jsan9020027
  14. Propagative broad learning for nonparametric modeling of ambient effects on structural health indicators vol.20, pp.4, 2016, https://doi.org/10.1177/1475921720916923
  15. Online probabilistic model class selection and joint estimation of structures for post-disaster monitoring vol.27, pp.15, 2016, https://doi.org/10.1177/1077546320949115
  16. Broad Bayesian learning (BBL) for nonparametric probabilistic modeling with optimized architecture configuration vol.36, pp.10, 2016, https://doi.org/10.1111/mice.12663