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Structural change monitoring of a cable-stayed bridge by time-series modeling of the global thermal deformation acquired by GPS

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Abstract

In this study, the use of ARIMA model coefficients extracted from the response of global thermal deformation, which can be acquired by the GPS monitoring, was proposed for the structural change monitoring of the long-span bridge. The daily periodic air temperature change causes the characteristic global thermal deformation, which is suitable to be acquired by the GPS in the long-span cable-stayed bridge. The pattern of this global thermal deformation was then expected to have sensitivities to changes on global structural properties. The procedures of feature extraction based on the ARIMA model estimation and the Mahalanobis distance comparison were presented, and their applicabilities were verified both by the numerical study and by the application to actual GPS monitoring data. In the numerical study, a FE model of a cable-stayed bridge was constructed, and the time-series displacements under the periodic temperature load were obtained with some cases of structural conditions by varying the boundary conditions and the cable tensions. The procedures of feature extraction and comparison were then applied to the obtained displacement time-series. In the results, the Mahalanobis distance of feature vector, which was configured by estimated AR and MA coefficients, showed significant changes both in the two cases of boundary condition and stayed-cable tension changes. The procedure was then applied to year-round GPS data acquired in the actual cable-stayed bridge. It was shown that the Mahalanobis distance comparison could provide proper assessment to the structural changes that was consistent with the actual structural condition.

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References

  1. Cross EJ, Koo KY, Brownjohn JMW, Worden K (2010) Long-term monitoring and data analysis of the Tamar bridge. Mech Syst Signal Process 35(1–2):16–34

    Google Scholar 

  2. Zhou Y, Sun L, Peng Z (2015) Mechanisms of thermally induced deflection of a long-span cable-stayed bridge. Smart Struct Syst 15(3):505–522

    Article  Google Scholar 

  3. Rolands K, Prakash K, Bill H (2015) Long-term structural health monitoring of the Cleddau bridge: evaluation of quasi-static temperature effects on bearing movements. Struct Infrastruct Eng 12(10):1342–1355

    Google Scholar 

  4. Fujino Y, Murata M, Okano S, Takeguchi M (2000) Monitoring system of the Akashi Kaikyo Bridge and displacement measurement using GPS. In: Nondestructive Evaluation of Highways, Utilities, and Pipelines IV, 3995. International Society for Optics and Photonics, pp 229–237

  5. Celebi M (2000) GPS in dynamic monitoring of long-period structures. Soil Dyn Earthq Eng 20(5–8):477–483

    Article  Google Scholar 

  6. Kaloop MR, Li H (2009) Monitoring of bridge deformation using GPS technique. KSCE J Civ Eng 13(6):423–431

    Article  Google Scholar 

  7. Le VH, Nishio M (2015) Time-series analysis of GPS monitoring data from a long-span bridge considering the global deformation due to air temperature changes. J Civ Struct Health Monitor 5(4):415–425

    Article  Google Scholar 

  8. Sohn H, Farrar CR (2001) Damage diagnosis using time series analysis of vibration signals. Smart Mater Struct 10(3):446–451

    Article  Google Scholar 

  9. Kaloop MR, Hussan M, Kim D (2019) Time-series analysis of GPS measurements for long-span bridge movements using wavelet and model prediction techniques. Adv Space Res. https://doi.org/10.1016/j.asr.2019.02.027

    Article  Google Scholar 

  10. Omenzetter P, Brownjohn JMW (2006) Application of time series analysis for bridge monitoring. Smart Mater Struct 15(1):129–138

    Article  Google Scholar 

  11. Shi H, Worden K, Cross EJ (2019) A cointegration approach for heteroscedastic data based on a time series decomposition: an application to structural health monitoring. Mech Syst Signal Process 120:16–31

    Article  Google Scholar 

  12. Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton

    MATH  Google Scholar 

  13. Vikas AC, Prashanth MH, Indrani G, Channappa TM (2013) Effect of cable degradation on dynamic behavior of cable stayed bridges. J Civ Eng Res 3(1):35–45

    Google Scholar 

  14. Worden K, Manson G, Fieller NR (2000) Damage detection using outlier analysis. J Sound Vib 229(3):647–667

    Article  Google Scholar 

Download references

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Correspondence to Mayuko Nishio.

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Le, H.V., Nishio, M. Structural change monitoring of a cable-stayed bridge by time-series modeling of the global thermal deformation acquired by GPS. J Civil Struct Health Monit 9, 689–701 (2019). https://doi.org/10.1007/s13349-019-00360-9

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  • DOI: https://doi.org/10.1007/s13349-019-00360-9

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