Journal of Civil Structural Health Monitoring

, Volume 8, Issue 4, pp 555–567 | Cite as

Study on the dynamic properties of a suspended bridge using monocular digital photography to monitor the bridge dynamic deformation

  • Guojian Zhang
  • Guangli GuoEmail author
  • Long Li
  • Chengxin Yu
Original Paper


This study makes use of monocular digital photography, based on the IM-STBP (image matching-space time baseline parallax) method, to monitor bridge dynamic deformation to study bridge dynamic properties. A bridge was first photographed when traffic light was red (i.e., when the bridge was not influenced by dynamic vehicle load) to generate the zero image (a.k.a. the reference image), and then photographed every 3 s when the traffic light was green (i.e., when the bridge was influenced by dynamic vehicle load) to produce image sequences as successive images. Relative deformation values of deformation points were obtained based on the IM-STBP method. The results show that the measurement accuracy of the IM-STBP method reaches a sub-pixel level (0.445, 0.470 and 0.705 pixels in the X, Z and comprehensive directions, respectively) and that maximal deflections of the bridge monitored by cameras 1 and 3 (37.22 and 47.40 mm, respectively) are within bridge deflection tolerance (75 mm). The monocular digital photography presented in this study has proved effective in monitoring bridge dynamic deformation even when the photographing direction is not perpendicular to the bridge plane and useful in assessing the situation of a bridge by monitoring the instantaneous dynamic global deformation of a bridge when the traffic light is green. Deformation curves in real time can also provide warning of any possible danger on the bridge. These global deformation curves of a bridge play a key role in studying the dynamic properties of a bridge influenced by dynamic vehicle load.


Monocular digital photography Bridge dynamic properties IM-STBP (image matching-time baseline parallax) method Instantaneous dynamic global deformation Image sequences 



This study was supported by the National Natural Science Foundation of China (Grant no. 51674249) and the Science and Technology project of the Shandong province of China (Grant no. 2010GZX20125).


  1. 1.
    Qiang P, Xun G, Zheng ZM (2003) A review of health monitoring and damage detection of bridge structures. Earthq Eng Eng Vibration 23(2):61–67Google Scholar
  2. 2.
    He YG, Zhao CJ (2014) Large-scale bridge distortion measuring technique discussion. In: International conference on mechanics and civil engineering, Atlantis Press, pp 691–694Google Scholar
  3. 3.
    He XL, Zhao LZ (2013) Based on inclinometer to measure dynamic deflection of high-speed railway bridge. Appl Mech Mater 405–408:3019–3026CrossRefGoogle Scholar
  4. 4.
    Lipták I, Kopáčik A, Erdélyi J, Kyrinovič P (2013) Dynamic Deformation Monitoring of Bridge Structure. Sel Sci Papers J Civ Eng 8(2):13–20Google Scholar
  5. 5.
    Psimoulis PA, Stiros SC (2013) Measuring deflections of a short-span railway bridge using a robotic total station. J Bridge Eng 18(2):182–185CrossRefGoogle Scholar
  6. 6.
    Anigacz W, Beben D, Kwiatkowski J (2018) Displacements monitoring of suspension bridge using geodetic techniques. In: Conte J, Astroza R, Benzoni G, Feltrin G, Loh K, Moaveni B (eds) Experimental vibration analysis for civil structures EVACES 2017. Lecture notes in civil engineering, vol 5. Springer, ChamGoogle Scholar
  7. 7.
    Abudayyeh O, Aktan H, Abdelqader I, Attanayake U (2012) Development and validation of a sensor-based health monitoring model for the Parkview bridge deck. DurabilityGoogle Scholar
  8. 8.
    Porco F, Fiore A, Porco G, Uva G (2013) Monitoring and safety for prestressed bridge girders by SOFO sensors. J Civ Struct Health Monit 3(1):3–18CrossRefGoogle Scholar
  9. 9.
    Yehuda B, Diego M (2016) Physical applications of GPS geodesy: a review. Rep Prog Phys 79(10):106801CrossRefGoogle Scholar
  10. 10.
    Yi TH, Li HN, Gu M (2013) Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge. Measurement 46(1):420–432CrossRefGoogle Scholar
  11. 11.
    Sinha TK, Dawant BM, Duay V, Cash DM, Weil RJ, Thompson RC, Weaver KD, Miga MI (2005) A method to track cortical surface deformations using a laser range scanner. IEEE Trans Med Imaging 24(6):767–781. CrossRefGoogle Scholar
  12. 12.
    Beben D, Anigacz W (2014) Interferometric radar application for dynamic testing of bridge structures. In: Proceedings of the 7th international conference on bridge maintenance, safety management, ShanghaiGoogle Scholar
  13. 13.
    Xu Y, Wang P, Zhou X, Xing C (2013) Research on dynamic deformation monitoring of bridges using ground-based interferometric radar IBIS-S. Geomat Inf Sci Wuhan Univ 38(7):845–849Google Scholar
  14. 14.
    Cooper MAR, Robson S (2006) High precision photogrammetric monitoring of the deformation of a steel bridge. Photogramm Record 13(76):505–510CrossRefGoogle Scholar
  15. 15.
    Forno C, Brown S, Hunt RA, Kearney AM, Oldfield S (2008) Measurement of deformation of a bridge by Moire photography and photogrammetry. Strain 27(3):83–87CrossRefGoogle Scholar
  16. 16.
    Maas HG (1998) Photogrammetric techniques for deformation measurements on reservoir walls. In: the proceedings of the IAG symposium on geodesy for geotechnical and structural engineering, Eisenstadt, Austria, pp 319–324Google Scholar
  17. 17.
    Yu YH, Vo-Ky C, Kodagoda S, Ha QP (2010) FPGA-based relative distance estimation for indoor robot control using monocular digital camera. J adv Comput Intell Intell Inf 14(6):714–721CrossRefGoogle Scholar
  18. 18.
    Gorbatsevich V, Vizilter Y, Knyaz V, Zheltov S (2014) Face pose recognition based on monocular digital imagery and stereo-based estimation of its precision. Int Arch Photogramm Remote Sens S XL-5:257–263CrossRefGoogle Scholar
  19. 19.
    Samuel RRPD (2014) close-range photogrammetry. Springer, BerlinGoogle Scholar
  20. 20.
    Bales FB (1984) Close-range photogrammetry for bridge measurement. Trans Res Record 950:39–44Google Scholar
  21. 21.
    Leitch K (2010) Close-range photogrammetric measurement of bridge deformations.
  22. 22.
    Remondino F (2002) Image sequence analysis for human body reconstruction. Arch P Rs 34:590–595zbMATHGoogle Scholar
  23. 23.
    D’Apuzzo N (2002) Surface measurement and tracking of human body parts from multi-image video sequences. ISPRS J Photogramm Remote Sens 56(5–6):360–375. CrossRefGoogle Scholar
  24. 24.
    Jiang R, Jáuregui DV, White KR (2008) Close-range photogrammetry applications in bridge measurement: literature review. Measurement 41(8):823–834CrossRefGoogle Scholar
  25. 25.
    Zhang G, Yu C (2016) The application of digital closerange photogrammetry in bridge deformation observation. GNSS World China 41(1):91–95 (in Chinese) Google Scholar
  26. 26.
    Zhang G, Yu X, Yu C (2015) Research of bridge vibration deformation monitoring with digital photography. Shandong Sci 28(05):85–90 (in Chinese) Google Scholar
  27. 27.
    Fujimoto K, Sun J, Takebe H, Suwa M, Naoi S (2007) Shape from parallel geodesics for distortion correction of digital camera document images. Electron Imaging Int Soc Opt Photonics 6500:286–290Google Scholar
  28. 28.
    Yanagi H, Chikatsu H (2012) Factors and estimation of accuracy in digital close range photogrammetry using digital cameras. J Jpn Soc Photogramm 50(1):4–17Google Scholar
  29. 29.
    Fang XU (2001) The monitor of steel structure bend deformation based on digital photogrammetry. Editorial Board of Geomatics and Information Science of Wuhan University, WuhanGoogle Scholar
  30. 30.
    Jeong JI, Moon SY, Choi SG, Rho DH (2002) A study on the flexible camera calibration method using a grid type frame with different line widths. In: Sice 2002. Proceedings of the sice conference, vol 1312, pp 1319–1324Google Scholar
  31. 31.
    Mingzhi C, ChengXin Y, Na X, YongQian Z, WenShan Y (2008) Application study of digital analytical method on deformation monitor of high-rise goods shelf. In: 2008 IEEE international conference on automation and logistics, vol 108, pp 2084–2088Google Scholar
  32. 32.
    Hsu CCJ, Lu MC, Lu YY (2010) Distance and angle measurement of objects on an oblique plane based on pixel number variation of CCD images. IEEE Trans Instrum Measurement 60(5):1779–1794CrossRefGoogle Scholar
  33. 33.
    Lu MC, Hsu CC, Lu YY (2010) Distance and angle measurement of distant objects on an oblique plane based on pixel variation of CCD image. In: Instrumentation and measurement technology conference (I2MTC), 2010 IEEE, pp 318–322Google Scholar
  34. 34.
    Kilgore AS, Mittleman AD (2016) Camera stand having an unlimited range of motion along an axis of rotation. US9377157Google Scholar
  35. 35.
    Institute of urban construction of ministry of construction (1998) Urban bridge design load standard:CJJ77–98. China building industry press (in Chinese) Google Scholar
  36. 36.
    Zhang G, Liu S, Zhao T, Yu C (2018) Exploring of PST-TBPM in monitoring bridge dynamic deflection in vibration. In: IOP Conference series: earth and environmental science, p 022045Google Scholar
  37. 37.
    Highway planning and design institute of the Ministry of Communication (1999) Standards of the Ministry of Communication of the People’s Republic of China JTJ 022-85: design specification for highway masonry and concrete bridge. China communication press (in Chinese) Google Scholar
  38. 38.
    Tian RL, Yang XW, Ren LF (2011) Study on mid-span deflection of beam bridge under moving loads by the recently proposed oscillator with time-dependent stiffness. Adv Mater Res 179–180:1096–1101CrossRefGoogle Scholar
  39. 39.
    Nishio M, Marin J, Fujino Y (2012) Uncertainty quantification of the finite element model of existing bridges for dynamic analysis. J Civ Struct Health Monit 2(3–4):163–173CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.NASG Key Laboratory of Land Environment and Disaster MonitoringChina University of Mining and TechnologyXuzhouPeople’s Republic of China
  2. 2.School of Environmental Science and Spatial InformaticsChina University of Mining and TechnologyXuzhouPeople’s Republic of China
  3. 3.Department of GeographyEarth System Science, Vrije Universiteit BrusselBrusselsBelgium
  4. 4.Business SchoolShandong Jianzhu UniversityJinanPeople’s Republic of China

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