Smart Structures and Systems

  • 제24권4호
  • /
  • Pages.507-524
  • /
  • 2019
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Bayesian forecasting approach for structure response prediction and load effect separation of a revolving auditorium

  • Ma, Zhi (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Yun, Chung-Bang (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Shen, Yan-Bin (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Yu, Feng (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Wan, Hua-Ping (College of Civil Engineering and Architecture, Zhejiang University) ;
  • Luo, Yao-Zhi (College of Civil Engineering and Architecture, Zhejiang University)
  • 투고 : 2019.01.28
  • 심사 : 2019.05.31
  • 발행 : 2019.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China

참고문헌

  1. Beck, J.L. (2010), "Bayesian system identification based on probability logic", Struct. Control Health Monit., 17(7), 825-847. https://doi.org/10.1002/stc.424.
  2. Brownjohn, J.M.W., Moyo, P., Omenzetter, P. and Lu, Y. (2003), "Assessment of highway bridge upgrading by dynamic testing and finite-element model updating", J. Bridge Eng., 8(3), 162-172. https://doi.org/10.1061/(ASCE)1084-0702(2003)8:3(162).
  3. Carden, E.P. and Brownjohn, J.M. (2008), "ARMA modelled timeseries classification for structural health monitoring of civil infrastructure", Mech. Syst. Signal Pr., 22(2), 295-314. https://doi.org/10.1016/j.ymssp.2007.07.003.
  4. Catbas, F.N., Susoy, M. and Frangopol, D.M. (2008), "Structural health monitoring and reliability estimation: Long span truss bridge application with environmental monitoring data", Eng. Struct., 30(9), 2347-2359. https://doi.org/10.1016/j.engstruct.2008.01.013.
  5. Chen, G., Mu, H., Pommerenke, D. and Drewniak, J.L. (2004), "Damage detection of reinforced concrete beams with novel distributed crack/strain sensors", Struct. Health Monit., 3(3): 225-243. https://doi.org/10.1177/1475921704045625.
  6. Cho, S., Jo, H., Jang, S., Park, J., Jung, H.J., Yun, C.B., Spencer, B.F. and Seo, J.W. (2010), "Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses", Smart Struct. Syst., 6(5-6), 461-480. http://dx.doi.org/10.12989/sss.2010.6.5_6.461.
  7. Chung, T.T., Cho, S., Yun, C.B. and Sohn, H. (2012), "Finite element model updating of Canton Tower using regularization technique", Smart Struct. Syst., 10(4-5), 459-470. http://dx.doi.org/10.12989/sss.2012.10.4_5.459.
  8. Goulet, J.A. (2017), "Bayesian dynamic linear models for structural health monitoring", Struct. Control Health Monit., 24(12), e2035. https://doi.org/10.1002/stc.2035.
  9. Goulet, J.A. and Koo, K. (2018), "Empirical validation of Bayesian dynamic linear models in the context of structural health monitoring", J. Bridge Eng., 23(2), 05017017. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001190.
  10. Gul, M. and Catbas, F.N. (2009), "Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications", Mech. Syst. Signal Pr., 23(7), 2192-2204. https://doi.org/10.1016/j.ymssp.2009.02.013.
  11. Hua, X.G., Ni, Y.Q., Chen, Z.Q. and Ko, J.M. (2009), "Structural damage detection of cable-stayed bridges using changes in cable forces and model updating", J. Struct. Eng., 135 (9), 1093-1106. https://doi.org/10.1061/(ASCE)0733-9445(2009)135:9(1093).
  12. Jeffreys, H. (1961), Theory of Probability, Oxford University Press, New York, USA.
  13. Jin, C., Li, J., Jang, S., Sun, X. and Christenson, R. (2015), "Structural damage detection for in-service highway bridge under operational and environmental variability", Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 9435, 94353A. https://doi.org/10.1117/12.2084384.
  14. Jin, S.S. (2019), "Bayesian inference using generalized likelihood for non-traditional residuals", Eng. Struct., 188, 613-626. https://doi.org/10.1016/j.engstruct.2019.03.062.
  15. Kim, H., Ahn, E., Shin, M. and Sim, S.H. (2018), "Crack and Noncrack Classification from Concrete Surface Images Using Machine Learning", Struct. Health Monit., 1475921718768747.
  16. Kim, J. and Lynch, J.P. (2012), "Experimental analysis of vehicle-bridge interaction using a wireless monitoring system and a two-stage system identification technique", Mech. Syst. Signal Pr., 28, 3-19. https://doi.org/10.1016/j.ymssp.2011.12.008.
  17. Kim, R.E., Li, J., Spencer, B.F., Nagayama, T. and Mechitov, K.A. (2016), "Synchronized sensing for wireless monitoring of large structures", Smart Struct. Syst., 18(5), 885-909. http://dx.doi.org/10.12989/sss.2016.18.5.885.
  18. Kromanis, R. and Kripakaran, P. (2016), "SHM of bridges: characterising thermal response and detecting anomaly events using a temperature-based measurement interpretation approach", J. Civil Struct. Health Monit., 6(2), 237-254. https://doi.org/10.1007/s13349-016-0161-z
  19. Lee, J.J., Lee, J.W., Yi, J.H., Yun, C.B. and Jung, H.Y. (2005), "Neural networks-based damage detection for bridges considering errors in baseline finite element models", J. Sound Vib., 280(3-5), 555-578. https://doi.org/10.1016/j.jsv.2004.01.003.
  20. Lin, Y.Z., Nie, Z.H. and Ma, H.W. (2017), "Structural damage detection with automatic feature - extraction through deep learning", Comput. -Aided Civil Infrastruct. Eng., 32(12), 1025-1046. https://doi.org/10.1111/mice.12313.
  21. Lindley, D.V. (1972), Bayesian Statistics, a Review, SIAM, Philadephia.
  22. Luo, Y.Z., Yang, P.C., Shen, Y.B., Yu, F., Zhong, Z.N. and Hong, J. (2014), "Development of a dynamic sensing system for civil revolving structures and its field tests in a large revolving auditorium", Smart Struct. Syst., 13(6), 993-1014. http://dx.doi.org/10.12989/sss.2014.13.6.993.
  23. Min, J., Park, S., Yun, C.B. and Song, B. (2010), "Development of a low-cost multifunctional wireless impedance sensor node", Smart Struct. Syst., 6(5-6), 689-709. http://dx.doi.org/10.12989/sss.2010.6.5_6.689.
  24. Min, J., Park, S., Yun, C.B., Lee, C.G. and Lee, C. (2012), "Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity", Eng. Struct., 39, 210-220. https://doi.org/10.1016/j.engstruct.2012.01.012.
  25. 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. http://dx.doi.org/10.12989/sss.2011.7.2.083.
  26. Noh, H.Y., Nair, K.K., Kiremidjian, A.S. and Loh, C.H. (2009), "Application of time series based damage detection algorithms to the benchmark experiment at the National Center for Research on Earthquake Engineering (NCREE) in Taipei, Taiwan", Smart Struct. Syst., 5(1), 95-117. http://dx.doi.org/10.12989/sss.2009.5.1.095.
  27. Pakzad, S.N. (2010), "Development and deployment of large scale wireless sensor network on a long-span bridge", Smart Struct. Syst., 6(5-6), 525-543. http://dx.doi.org/10.12989/sss.2010.6.5_6.525.
  28. Rice, J.A., Mechitov, K., Sim, S.H., Nagayama, T., Jang, S., Kim, R., Spencer, B.F., Agha, G. and Fujino, Y. (2010), "Flexible smart sensor framework for autonomous structural health monitoring", Smart Struct. Syst., 6(5-6), 423-438 http://dx.doi.org/10.12989/sss.2010.6.5_6.423.
  29. Shen, Y., Yang, P., Zhang, P., Luo, Y., Mei, Y., Cheng, H., Jin, L., Liang, C., Wang, Q. and Zhong, Z. (2013), "Development of a multi-type wireless sensor network for the large-scale structure of the national stadium in china", Int. J. Distrib. Sens. N., 9(12), 709724. https://doi.org/10.1155/2013/709724.
  30. Shinozuka, M., Chou, P.H., Kim, S., Kim, H.R, Karmakar, D. and Lu, F. (2010), "Non-invasive acceleration-based methodology for damage detection and assessment of water distribution system", Smart Struct. Syst., 6(6), 545-559. http://dx.doi.org/10.12989/sss.2010.6.5_6.545
  31. Siringoringo, D.M. and Fujino, Y. (2009), "Noncontact operational modal analysis of structural members by laser Doppler vibrometer", Comput.-Aided Civil Infrastruct. Eng., 24(4), 249-265. https://doi.org/10.1111/j.1467-8667.2008.00585.x.
  32. Skolnik, D., Lei, Y., Yu, E. and Wallace, J.W. (2006), "Identification, model updating, and response prediction of an instrumented 15-story steel-frame building", Earthq. Spectra, 22(3), 781-802. https://doi.org/10.1193/1.2219487.
  33. Sohn, H. (2006), "Effects of environmental and operational variability on structural health monitoring", Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 365(1851), 539-560. https://doi.org/10.1098/rsta.2006.1935.
  34. Strauss, A., Frangopol, D.M. and Kim, S. (2008), "Use of monitoring extreme data for the performance prediction of structures: Bayesian updating", Eng. Struct., 30(12), 3654-3666. https://doi.org/10.1016/j.engstruct.2008.06.009.
  35. Wan, H.P. and Ni, Y.Q. (2018a), "Bayesian modeling approach for forecast of structural stress response using structural health monitoring data", J. Struct. Eng., 144(9), 04018130. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002085.
  36. Wan, H.P. and Ni, Y.Q. (2018b), "Bayesian multi-task learning methodology for reconstruction of structural health monitoring data", Struct. Health Monit., 1475921718794953.
  37. Wang, H., Zhang, Y.M., Mao, J.X., Wan, H.P., Tao, T.Y. and Zhu, Q.X. (2019), "Modeling and forecasting of temperature-induced strain of a long-span bridge using an improved Bayesian dynamic linear model", Eng. Struct., 192, 220-232. https://doi.org/10.1016/j.engstruct.2019.05.006.
  38. Wang, J. and Liu, X. (2010), "Evaluation and Bayesian dynamic prediction of deterioration of structural performance", Struct. Infrastruct. Eng., 6(6), 663-674. https://doi.org/10.1080/15732470701478503.
  39. Wang, Y.C., Luo, Y.Z., Sun, B., Liu, X., Jia, Z.X. and Shen Y.B. (2018), "Field measurement system based on a wireless sensor network for the wind load on spatial structures: Design, experimental, and field validation", Struct. Control Health Monit., 25(9), e2192. https://doi.org/10.1002/stc.2192.
  40. West, M. and Harrison, J. (1997), Bayesian Forecasting and Dynamic Models. Springer, New York, USA.
  41. Wu, J., Tang, Z., Lv, F., Yang, K., Yun, C.B. and Duan, Y. (2018), "Ultrasonic guided wave-based switch rail monitoring using independent component analysis", Measure. Sci. Technol., 29(11), 115102. https://doi.org/10.1088/1361-6501/aadc47
  42. Xia, Y., Chen, B., Weng, S., Ni, Y.Q. and Xu Y.L. (2012), "Temperature effect on vibration properties of civil structures: a literature review and case studies", J. Civil Struct. Health Monit., 2(1), 29-46. https://doi.org/10.1007/s13349-011-0015-7
  43. Xu, Y.L., Chen, B., Ng, C.L., Wong, K.Y. and Chan, W.Y. (2010). "Monitoring temperature effect on a long suspension bridge", Struct. Control Health Monit., 17(6), 632-653. https://doi.org/10.1002/stc.340.
  44. Yun, C.B. and Bahng, E.Y. (2000), "Substructural identification using neural networks", Comput. Struct., 77(1), 41-52. https://doi.org/10.1016/S0045-7949(99)00199-6.
  45. Zhang, L.H., Wang, Y.W., Ni, Y.Q. and Lai, S.K. (2018), "Online condition assessment of high-speed trains based on Bayesian forecasting approach and time series analysis", Smart Struct. Syst., 21(5), 705-713. https://doi.org/10.12989/sss.2018.21.5.705.
  46. Zhang, Z.Y. and Luo, Y.Z. (2017), "Restoring method for missing data of spatial structural stress monitoring based on correlation", Mech. Syst. Signal Pr., 91, 266-277. https://doi.org/10.1016/j.ymssp.2017.01.018.
  47. Zheng, H. and Mita, A. (2008), "Damage indicator defined as the distance between ARMA models for structural health monitoring", Struct. Control Health Monit., 15(7), 992-1005. https://doi.org/10.1002/stc.235.
  48. Zhu, B. and Frangopol, D.M. (2013), "Incorporation of structural health monitoring data on load effects in the reliability and redundancy assessment of ship cross-sections using Bayesian updating", Struct. Health Monit., 12(4), 377-392. https://doi.org/10.1177/1475921713495082.
  49. Zhu, Y., Ni, Y.Q., Jesus, A., Liu, J. and Laory, I. (2018), "Thermal strain extraction methodologies for bridge structural condition assessment", Smart Mater. Struct., 27(10), 105051. https://doi.org/10.1088/1361-665X/aad5fb
  50. Zhu, Y., Ni, Y.Q., Jin, H., Inaudi, D. and Laory, I. (2019), "A temperature-driven MPCA method for structural anomaly detection", Eng. Struct., 190, 447-458. https://doi.org/10.1016/j.engstruct.2019.04.004.
  51. Zonta, D., Wu, H., Pozzi, M., Zanon, P., Ceriotti, M., Mottola, L., Picco, G.P., Murphy, A.L., Guna, S. and Corra, M. (2010), "Wireless sensor networks for permanent health monitoring of historic buildings", Smart Struct. Syst., 6(5-6), 595-618. http://dx.doi.org/10.12989/sss.2010.6.5_6.595.