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Modal Frequency-Based Structural Damage Detection

  • Yang Deng
  • Aiqun Li
Chapter

Abstract

Over the past several decades, a significant research effort has been focused on the health monitoring and condition assessment for long-span bridges (Ko et al. in Eng Struct 27(12):1715–1725, 2005) [1], (Hsieh et al. in J Bridge Eng 11(6):707–715, 2006) [2]. How to explain the health condition of the bridge structure according to the collected structural responses remains a great challenge in the civil engineering community. It is well known that bridge structures are subject to varying environmental conditions such as traffic loadings and environmental temperature.

References

  1. 1.
    Ko JM, Ni YQ. Technology developments in structural health monitoring of large-scale bridges. Eng Struct. 2005;27(12):1715–25.CrossRefGoogle Scholar
  2. 2.
    Hsieh KH, Halling MW, Barr PJ. Overview of vibrational structural health monitoring with representative case studies. J Bridge Eng. 2006;11(6):707–15.CrossRefGoogle Scholar
  3. 3.
    Ni YQ, Hua XG, Fan KQ, Ko JM. Correlating modal properties with temperature using long-term monitoring data and support vector machine technique. Eng Struct. 2005;27(12):1762–73.CrossRefGoogle Scholar
  4. 4.
    Ding YL, Li AQ, Liu T. Environmental variability study on the measured responses of Runyang Cable-stayed Bridge using wavelet packet analysis. Sci China Ser E: Technol Sci. 2008;51(5):517–28.CrossRefGoogle Scholar
  5. 5.
    Cornwell P, Farrar CR, Doebling SW, Sohn H. Environmental variability of modal properties. Exp Tech. 1999;23(6):45–8.CrossRefGoogle Scholar
  6. 6.
    Peeters B, De Roeck G. One-year monitoring of the Z24-Bridge: environmental effects versus damage events. Earthquake Eng Struct Dynam. 2001;30(2):149–71.CrossRefGoogle Scholar
  7. 7.
    Sohn H, Dzwonczyk M, Straser EG, Kiremidjian AS, Law KH, Meng T. An experimental study of temperature effect on modal parameters of the Alamos Canyon Bridge. Earthquake Eng Struct Dynam. 1999;28(8):879–97.CrossRefGoogle Scholar
  8. 8.
    Wahab AM, De Roeck G. Effect of temperature on dynamic system parameters of a highway bridge. Struct Eng Int. 1997;7(4):266–70.CrossRefGoogle Scholar
  9. 9.
    Hua XG, Ni YQ, Ko JM, Wong KY. Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique. J Comput Civil Eng. 2007;21(2):122–35.CrossRefGoogle Scholar
  10. 10.
    Kim CY, Jung DS, Kim NS, Yoon JG. Effect of vehicle mass on measured dynamic characteristics of bridge from traffic-induced vibration test. In: Proceedings of the 19th international modal analysis conference, society for experimental mechanics, FEB 05-08, 2001. Bethel: Soc Experimental Mechanics Inc.; 2001.Google Scholar
  11. 11.
    Zhang QW, Fan LC, Yuan WC. Traffic-induced variability in dynamic properties of cable-stayed bridge. Earthquake Eng Struct Dyn. 2002;31(11):2015–21.CrossRefGoogle Scholar
  12. 12.
    Chen J, Xu YL, Zhang RC. Modal parameters identification of using Ma suspension bridge under typhoon Victor: EMD-HT method. J Wind Eng Ind Aerodyn. 2004;92(10):805–27.CrossRefGoogle Scholar
  13. 13.
    Ni YQ, Ko JM, Hua XG, Zhou HF. Variability of measured modal frequencies of a cable-stayed bridge under different wind conditions. Smart Struct Syst. 2007;3(3):341–56.CrossRefGoogle Scholar
  14. 14.
    Deng Y, Ding YL, Li AQ. Quantitative evaluation of variability in modal frequencies of a suspension bridge under environmental conditions. J Vib Shock. 2011;30(8):230–6.Google Scholar
  15. 15.
    Ding YL, Deng Y, Li AQ. Study on correlations of modal frequencies and environmental factors for a suspension bridge based on improved neural networks. Sci China-Technol Sci. 2010;53(9):2501–9.CrossRefGoogle Scholar
  16. 16.
    Hu WH, Moutinho C, Caetano E, Magalhaes F, Cunha A. Continuous dynamic monitoring of a lively footbridge for serviceability assessment and damage detection. Mech Syst Signal Process. 2012;33:38–55.CrossRefGoogle Scholar
  17. 17.
    Yan AM, Kerschen G, De Boe P, Golinval JC. Structural damage diagnosis under varying environmental conditions—part I: a linear analysis. Mech Syst Signal Process. 2005;19(4):847–64.CrossRefGoogle Scholar
  18. 18.
    Chen ZW, Cai QL, Lei Y, Zhu SY. Damage detection of long-span bridges using stress influence lines incorporated control charts. Sci China-Technol Sci. 2014;57(9):1689–97.CrossRefGoogle Scholar
  19. 19.
    Kosnik DE, Zhang WZ, Durango-Cohen PL. Application of statistical process control for structural health monitoring of a historic building. J Infrastruct Syst. 2014;20(1).  https://doi.org/10.1061/(asce)is.1943-555x.0000164.CrossRefGoogle Scholar
  20. 20.
    Kullaa J. Damage detection of the Z24 Bridge using control charts. Mech Syst Signal Process. 2003;17(1):163–70.CrossRefGoogle Scholar
  21. 21.
    Deraemaeker A, Reynders E, De Roeck G, Kullaa J. Vibration-based structural health monitoring using output-only measurements under changing environment. Mech Syst Signal Process. 2008;22(1):34–56.CrossRefGoogle Scholar
  22. 22.
    Magalhaes F, Cunha A, Caetano E. Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection. Mech Syst Signal Process. 2012;28:212–28.CrossRefGoogle Scholar
  23. 23.
    Wang ZR, Ong KCG. Autoregressive coefficients based Hotelling’s T2 control chart for structural health monitoring. Comput Struct. 2008;86(19–20):1918–35.CrossRefGoogle Scholar
  24. 24.
    Yi TH, Li HN, Song GB, Guo Q. Detection of shifts in GPS measurements for a long-span bridge using CUSUM chart. Int J Struct Stab Dyn. 2016;16(4):1640024.CrossRefGoogle Scholar
  25. 25.
    Montgomery DC. Introduction to statistical quality control. New York: Wiley; 2005.zbMATHGoogle Scholar
  26. 26.
    Quesenberry CP. On properties of binomial Q charts for variables. J Qual Technol. 1995;27(3):184–203.CrossRefGoogle Scholar
  27. 27.
    Quesenberry CP. SPC Q charts for start-up processes and short or long runs. J Qual Technol. 1991;23(3):213–24.CrossRefGoogle Scholar
  28. 28.
    Quesenberry CP. SPC Q charts for a binomial parameter p: short or long runs. J Qual Technol. 1991;23(3):239–46.CrossRefGoogle Scholar
  29. 29.
    Quesenberry CP. SPC Q charts for a Poisson parameter λ: short or long runs. J Qual Technol. 1991;23(4):296–303.MathSciNetCrossRefGoogle Scholar
  30. 30.
    Ren WX, Peng XL (2005) Baseline finite element modeling of a large span cable-stayed bridge through field ambient vibration tests. Comput Struct. 2005;83(8–9):536–50.CrossRefGoogle Scholar
  31. 31.
    Ding YL, Li AQ, Sun J, Deng Y. Experimental and analytical studies on static and dynamic characteristics of steel box girder for Runyang Cable-stayed Bridge. Adv Struct Eng. 2008;11(4):425–38.CrossRefGoogle Scholar
  32. 32.
    Macdonald JHG. Identification of the dynamic behaviour of a cable-stayed bridge from full-scale testing during and after construction. Bristol: University of Bristol; 2000.Google Scholar
  33. 33.
    Li AQ, Miao CQ, Li ZX, Han XL, Wu SD, Ji L, Yang YD. Health monitoring system for the Runyang Yangtse River Bridge. J Southeast Univ (Natural Science Edition). 2003;33(5):544–8.Google Scholar
  34. 34.
    Yourstone SA, Zimmer WJ. Non-normality and the design of control charts for averages. Decis Sci. 1992;23(5):1099–113.CrossRefGoogle Scholar
  35. 35.
    Bowman AW, Azzalini A. Applied smoothing techniques for data analysis: the Kernel approach with S-Plus illustrations. New York: Oxford University Press; 1997.zbMATHGoogle Scholar
  36. 36.
    Scott DW. Multivariate density estimation: theory, practice, and visualization. New York: Wiley; 1992.CrossRefGoogle Scholar
  37. 37.
    Sohn H, Czarneck JA, Farrar CR. Structural health monitoring using statistical process control. J Struct Eng ASCE. 2000;126(11):1356–63.CrossRefGoogle Scholar
  38. 38.
    Mackay DJC. Bayesian interpolation. Neural Comput. 1992;4(3):415–47.CrossRefGoogle Scholar
  39. 39.
    Deng Y, Li AQ, Feng DM. Probabilistic damage detection of long-span bridges using measured modal frequencies and temperature. Int J Struct Stab Dyn. 2018;18(10):1850126.CrossRefGoogle Scholar

Copyright information

© Science Press and Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Beijing Advanced Innovation Center for Future Urban DesignBeijing University of Civil Engineering and ArchitectureBeijingChina

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