Journal of Civil Structural Health Monitoring

, Volume 5, Issue 4, pp 457–468 | Cite as

Eulerian-based virtual visual sensors to detect natural frequencies of structures

Original Paper


Natural frequency of vibration data are often used to study the behavior of structures. They are also used to calibrate finite element models and some studies have proposed that the presence and location of damage can be estimated using these data. Along this line, we earlier proposed the concept of Eulerian-based virtual visual sensors to estimate natural frequencies of structural vibrations based on the change of pixel intensity captured in a digital video. Benefits of this approach are that it allows for distributed sensing and is contactless. However, as intensity does not reflect any physical quantity, such as displacement, and the range of values is difficult to control, the signal-to-noise ratio (SNR) can be relatively low. Furthermore, impulsive changes of intensity caused by large deformations compared to the pixel size can result in an impulse train in the frequency domain which leads to ambiguity in determining peak frequencies. As a result, it is often only possible to estimate the first fundamental mode of vibration. In this paper, we present strategies using targets mounted to the structure combined with signal processing methods that significantly improve the SNR and allow for detecting higher natural frequencies of vibration. The concepts, their mathematical background, laboratory tests to prove the accuracy and enhancement of SNR, as well as an example of an in-service pedestrian bridge are presented and discussed.


Natural frequencies Structural vibrations Structural health monitoring Video analysis Eulerian-based virtual visual sensors Linear gradient pattern targets 


  1. 1.
    Alvandi A, Cremona C (2006) Assessment of vibration-based damage identification techniques. J Sound Vib 292:179–202. doi:10.1016/j.jsv.2005.07.036 CrossRefGoogle Scholar
  2. 2.
    Bagheri A, Amiri GG, Razzaghi SAS (2009) Vibration-based damage identification of plate structures via curvelet transform. J Sound Vib 327:593–603. doi:10.1016/j.jsv.2009.06.019 CrossRefGoogle Scholar
  3. 3.
    Carden EP (2004) Vibration based condition monitoring: a review. Struct Health Monit 3:355–377. doi:10.1177/1475921704047500 CrossRefGoogle Scholar
  4. 4.
    Reynders E, Roeck G (2011) Subspace identification for operational modal analysis. New Trends Vib Based Struct Heal Monit SE—3. doi:10.1007/978-3-7091-0399-9_3 Google Scholar
  5. 5.
    Doebling SWS, Farrar CRC, Prime MBM, Shevitz DWD (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Distribution. doi:10.2172/249299 MATHGoogle Scholar
  6. 6.
    Farrar CR, Worden K (2007) An introduction to structural health monitoring. Philos Trans A Math Phys Eng Sci 365:303–315. doi:10.1098/rsta.2006.1928 CrossRefGoogle Scholar
  7. 7.
    Friswell MI (2007) Damage identification using inverse methods. Philos Trans A Math Phys Eng Sci 365:393–410. doi:10.1098/rsta.2006.1930 CrossRefGoogle Scholar
  8. 8.
    Fritzen C (2005) Vibration-based structural health monitoring—concepts and applications. Key Eng Mater 294:3–18. doi:10.4028/ CrossRefGoogle Scholar
  9. 9.
    Montalvao D (2006) A review of vibration-based structural health monitoring with special emphasis on composite materials. Shock Vib Dig 38:295–324. doi:10.1177/0583102406065898 CrossRefGoogle Scholar
  10. 10.
    Ostachowicz W, Güemes A (2013) New trends in structural health monitoring. Springer, LondonCrossRefGoogle Scholar
  11. 11.
    Pandey AK, Biswas M, Samman MM (1991) Damage detection from changes in curvature mode shapes. J Sound Vib 145:321–332. doi:10.1016/0022-460X(91)90595-B CrossRefGoogle Scholar
  12. 12.
    Salawu OS (1997) Detection of structural damage through changes in frequency: a review. Eng Struct 19:718–723. doi:10.1016/S0141-0296(96)00149-6 CrossRefGoogle Scholar
  13. 13.
    Sun Z, Chang C-C (2007) Vibration based structural health monitoring: wavelet packet transform based solution. Struct Infrastruct Eng 3:313–323. doi:10.1080/15732470500473598 CrossRefGoogle Scholar
  14. 14.
    Zou Y, Tong L, Steven GP (2000) Vibration-based model-dependent damage (delamination) identification and health monitoring for composite structures—a review. J Sound Vib 230:357–378. doi:10.1006/jsvi.1999.2624 CrossRefGoogle Scholar
  15. 15.
    Nassif HH, Gindy M, Davis J (2005) Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration. NDT E Int 38:213–218. doi:10.1016/j.ndteint.2004.06.012 CrossRefGoogle Scholar
  16. 16.
    Ciang CC, Lee J-R, Bang H-J (2008) Structural health monitoring for a wind turbine system: a review of damage detection methods. Meas Sci Technol 19:122001. doi:10.1088/0957-0233/19/12/122001 CrossRefGoogle Scholar
  17. 17.
    Kim S-W, Jeon B-G, Kim N-S, Park J-C (2013) Vision-based monitoring system for evaluating cable tensile forces on a cable-stayed bridge. Struct Health Monit 12:440–456. doi:10.1177/1475921713500513 CrossRefGoogle Scholar
  18. 18.
    Song Y-Z, Bowen CR, Kim AH et al (2014) Virtual visual sensors and their application in structural health monitoring. Struct Health Monit 13:1475921714522841. doi:10.1177/1475921714522841 CrossRefGoogle Scholar
  19. 19.
    Zaurin R, Necati Catbas F (2011) Structural health monitoring using video stream, influence lines, and statistical analysis. Struct Health Monit 10:309–332. doi:10.1177/1475921710373290 CrossRefGoogle Scholar
  20. 20.
    Zaurin R, Catbas FN (2009) Integration of computer imaging and sensor data for structural health monitoring of bridges. Smart Mater Struct 19:015019. doi:10.1088/0964-1726/19/1/015019 CrossRefGoogle Scholar
  21. 21.
    Catbas FN, Zaurin R, Gul M, Gokce HB (2012) Sensor networks, computer imaging, and unit influence lines for structural health monitoring: case study for bridge load rating. J Bridge Eng 17:662–670. doi:10.1061/(ASCE)BE.1943-5592.0000288 CrossRefGoogle Scholar
  22. 22.
    Fraser M, Elgamal A, He X, Conte J (2009) Sensor network for structural health monitoring of a highway bridge. J Comput Civil Eng 24:11–24CrossRefGoogle Scholar
  23. 23.
    Ehrhart M, Lienhart W (2015) Image-based dynamic deformation monitoring of civil engineering structures from long ranges. IS\&T/SPIE electron. Imaging. Int Soc Optics Photonics pp 94050J–94050JGoogle Scholar
  24. 24.
    Martins LL, Rebordão J, Ribeiro AS (2013) Conception and development of an optical methodology applied to long-distance measurement of suspension bridges dynamic displacement. J Phys Conf Ser, p 012055 (IOP Publishing)Google Scholar
  25. 25.
    Ehrhart M, Lienhart W (2015) Development and evaluation of a long range image-based monitoring system for civil engineering structures. SPIE Smart Struct Mater Nondestruct Eval Heal Monit Int Soc Optics Photonics pp 94370K–94370KGoogle Scholar
  26. 26.
    Kim SW, Kim NS (2011) Multi-point displacement response measurement of civil infrastructures using digital image processing. Procedia Eng 14:195–203CrossRefGoogle Scholar
  27. 27.
    Olaszek P (1999) Investigation of the dynamic characteristic of bridge structures using a computer vision method. Measurement 25:227–236. doi:10.1016/S0263-2241(99)00006-8 CrossRefGoogle Scholar
  28. 28.
    Caetano E, Silva S, Bateira J (2011) A vision system for vibration monitoring of civil engineering structures. Exp Tech 35:74–82. doi:10.1111/j.1747-1567.2010.00653.x CrossRefGoogle Scholar
  29. 29.
    Lee JJ, Shinozuka M (2006) Real-time displacement measurement of a flexible bridge using digital image processing techniques. Exp Mech 46:105–114. doi:10.1007/s11340-006-6124-2 CrossRefGoogle Scholar
  30. 30.
    Wahbeh AM, Caffrey JP, Masri SF (2003) A vision-based approach for the direct measurement of displacements in vibrating systems. Smart Mater Struct 12:785–794. doi:10.1088/0964-1726/12/5/016 CrossRefGoogle Scholar
  31. 31.
    Choi H-S, Cheung J-H, Kim S-H, Ahn J-H (2011) Structural dynamic displacement vision system using digital image processing. NDT E Int 44:597–608. doi:10.1016/j.ndteint.2011.06.003 CrossRefGoogle Scholar
  32. 32.
    Schumacher T, Shariati A (2013) Monitoring of structures and mechanical systems using virtual visual sensors for video analysis: fundamental concept and proof of feasibility. Sensors (Switzerland) 13:16551–16564. doi:10.3390/s131216551 CrossRefGoogle Scholar
  33. 33.
    Wu H-Y, Rubinstein M, Shih E et al (2012) Eulerian video magnification for revealing subtle changes in the world. ACM Trans Graph. doi:10.1145/2185520.2335416 Google Scholar
  34. 34.
    Chen J, Wadhwa N, Cha Y et al (2014) Structural modal identification through high speed camera video : motion magnification. Top Modal Anal I. doi:10.1007/978-3-319-04753-9 Google Scholar
  35. 35.
    Shariati A, Schumacher T, Ramanna N (2014) Exploration of video-based structural health monitoring techniques. No. CAIT-UTC-038.
  36. 36.
    Widrow B, Kollár I (2008) Quantization noise. Cambridge Univ Press, Cambridge. doi:10.1017/CBO9780511754661 CrossRefGoogle Scholar
  37. 37.
    Shariati A, Schumacher T (2014) Oversampling in virtual visual sensors as a means to recover higher modes of vibration. In: AIP conference proceedings (Proceedings QNDE 2014, July 20–25, Boise, ID.)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.University of DelawareNewarkUSA

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