Skip to main content
Log in

Damage alarming of long-span suspension bridge based on GPS-RTK monitoring

  • Published:
Journal of Central South University Aims and scope Submit manuscript

Abstract

Structure damage identification and alarming of long-span bridge were conducted with three-dimensional dynamic displacement data collected by GPS subsystem of health monitoring system on Runyang Suspension Bridge. First, the effects of temperature on the main girder spatial position coordinates were analyzed from the transverse, longitudinal and vertical directions of bridge, and the correlation regression models were built between temperature and the position coordinates of main girder in the longitudinal and vertical directions; then the alarming indices of coordinate residuals were conducted, and the mean-value control chart was applied to making statistical pattern identification for abnormal changes of girder dynamic coordinates; and finally, the structural damage alarming method of main girder was established. Analysis results show that temperature has remarkable correlation with position coordinates in the longitudinal and vertical directions of bridge, and has weak correlation with the transverse coordinates. The 3% abnormal change of the longitudinal coordinates and 5% abnormal change of the vertical ones caused by structural damage are respectively identified by the mean-value control chart method based on GPS dynamic monitoring data and hence the structural abnormalities state identification and damage alarming for main girder of long-span suspension bridge can be realized in multiple directions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. KO J M, NI Y Q. Technology developments in structural health monitoring of large-scale bridges [J]. Engineering Structures, 2005, 27(12): 1715–1725.

    Article  Google Scholar 

  2. LI Ai-qun, DING You-liang, WANG Hao, GUO Tong. Analysis and assessment of bridge health monitoring mass data-Progress in research/development of “Structural Health Monitoring” [J]. Technological Sciences, 2012, 55(8): 2212–2224.

    Article  Google Scholar 

  3. SONG Zi-shou, LI Hu-sheng. Research on wavelet de-noising for bridge alarming [C]// 2010 International Conference on Measuring Technology and Mechatronics Automation. Changsha, 2010: 235–239.

    Chapter  Google Scholar 

  4. NI Y Q, HUA X G, FAN K Q, KO J M. Correlating modal properties with temperature using long-term monitoring data and support vector machine technique [J]. Engineering Structures, 2005, 27(12): 1762–1773.

    Article  Google Scholar 

  5. DING You-liang, LI Ai-qun, LIU Tao. Environmental variability study on the measured responses of Runyang Cable-stayed Bridge using wavelet packet analysis [J]. Science in China Series E: Technological Sciences, 2008, 51(5): 517–528.

    Article  MATH  Google Scholar 

  6. PATJAWIT W. KANOK-NUKULCHAI W. Health monitoring of highway bridges based on a global flexibility index [J]. Engineering Structures, 2005, 27(9): 1385–1391.

    Article  MATH  Google Scholar 

  7. CHEN Zhi-wei, CAI Qin-lin, LEI Ying, ZHU Song-ye. Damage detection of long-span bridges using stress influence lines incorporated control charts [J]. Science China Technological Sciences, 2014, 57(9): 1689–1697.

    Article  Google Scholar 

  8. NI Y Q, HUA X G, WONG K Y, KO J M. Assessment of bridge expansion joints using long-term displacement and temperature measurement [J]. Journal of Performance of Constructed Facilities, 2007, 21(2): 143–151.

    Article  MATH  Google Scholar 

  9. MIAO Chang-qing, DENG Yang, DING You-liang, LI Ai-qun. Damage alarming for bridge expansion joints using novelty detection technique based on long-term monitoring data [J]. Journal of Central South University, 2013, 20(1): 226–235.

    Article  Google Scholar 

  10. DENG Yang, LI Ai-qun, DING You-liang, SUN Peng. Damage identification of expansion joints in long span bridge using long-term monitoring data [J]. Journal of Southeast University: Natural Science Edition, 2011, 41(2): 336–341. (in Chinese)

    Google Scholar 

  11. MIAO Chang-qing, LI Ai-qun, HAN Xiao-lin, LI Zhao-xia, JI Lin, YANG Yu-dong. Monitor strategy for the structural health monitoring system of Runyang bridge [J]. Journal of Southeast University: Natural Science Edition, 2005, 35(5): 780–785. (in Chinese).

    Google Scholar 

  12. SOHN S, CZARNECKI J A, FARRAR C R. Structural health monitoring using statistical process control [J]. Journal of Structural Engineering, 2000, 126(11): 1356–1363.

    Article  Google Scholar 

  13. FUGATE M L, SOHN H, FARRAR C R. Vibration-based damage detection using statistical process control [J]. Mechanical Systems and Signal Processing, 2001, 15(4): 707–721.

    Article  Google Scholar 

  14. YI Ting-hua, GUO Qing, LI Hong-nan. The research on detection methods of GPS abnormal monitoring data based on control chart [J]. Engineering Mechanics, 2013, 30(8): 133–141. (in Chinese)

    MathSciNet  MATH  Google Scholar 

  15. ZHANG Q W, FAN L C, YUAN W C. Traffic-induced variability in dynamic properties of cable-stayed bridge [J]. Earthquake Engineering and Structural Dynamics, 2002, 31(11): 2015–2021.

    Article  Google Scholar 

  16. DENG Yang, DING You-liang, LI Ai-qun. Structural condition assessment of long-span suspension bridges using long-term monitoring data [J]. Earthquake Engineering and Engineering Vibration, 2010, 9(1): 123–131.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chang-qing Miao  (缪长青).

Additional information

Foundation item: Project(51078080) supported by the National Natural Science Foundation of China; Project(20130969010 ) supported by Aeronautical Science Foundation of China; Project(2011Y03-6) supported by Traffic Transportation Technology Project of Jiangsu Province, China; Project(BK2012562) supported by the Natural Science Foundation of Jiangsu Province, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miao, Cq., Wang, M., Tian, Hj. et al. Damage alarming of long-span suspension bridge based on GPS-RTK monitoring. J. Cent. South Univ. 22, 2800–2808 (2015). https://doi.org/10.1007/s11771-015-2811-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-015-2811-4

Key words

Navigation