Laser Speckle Correlation Method in Diagnostics of Bridge Span Vibrations

Automobile bridge and overpasses are essential elements of transport infrastructure in every country. Rapid diagnostics and subsequent monitoring of critical bridge and overpass components’ performance increases their service life. The aim of this study was to design a method and laser system for remote diagnostics of beam vibrations caused by dynamic loading and to apply them to a laboratory bridge model. The paper discusses a diagnostic method for bridge span vibrations based on laser-speckle correlation together with high-speed video recording. Using a laboratory model of a bridge, bearing structures under shock loading were studied by means of the proposed method. Relative surface displacements induced by different loading conditions were determined herein. The program for calculating correlation functions was optimized taking into account characteristics of the bridge span. In particular, surface displacements were analyzed with regard to the frame frequency and size of laser speckle patterns. Research technique was tested on the laboratory bridge model with beams made of different materials (steel, concrete, wood). For these beams, various shapes and amplitudes of displacements caused by shock loads are shown. Experiments demonstrate remote monitoring of bridge vibration under real shock loads caused by vehicle movement. This will be useful for further development of the proposed technique for bridge vibrations monitoring in real conditions.

1. Fp. Chiang, Jd. Yu. Structural health monitoring using digital speckle photography. Optical Measurements, Modeling, and Metrology. Conference Proceedings of the Society for Experimental Mechanics Series, ed. T. Proulx. New York, Springer, 2011;5:393-399. DOI: 10.1007/978-1-4614-0228-2_47

2. Martins LL, Rebordão JM, Ribeiro AS. Optical metrology applied to 3D displacement measurement of long-span suspension bridge dynamics. Proceedings of the 9th International Conference on Structural Dynamics, EURODYN 2014, eds. A. Cunha, E. Caetano, P. Ribeiro, G. Müller. Portugal, Porto, University of Porto, 2014, pp. 2135–2142. Available at: https://www.academia.edu/111054039/ (accessed 28.10.2025).

3. Eremin VG, Cao Van Lam. Perfection of management of the condition of bridge constructions on the basis of constant monitoring. Engineering systems and structures. 2012;1(6):92-97. (In Russ.).

4. Nesterova DN, Chernykh VK. Monitoring of bridge structures. Technical regulation in transport construction. 2015;1(9):49-56. (In Russ.).

5. Lyakhevich GD, Grechukhin VA. Continuous monitoring of bridge structure health. World of roads. 2019;122:40-47. (In Russ.).

6. Elugachev PA, Gubarev FA, Bannikov AA, Mostovshchikov AV. Road bridge monitoring using digital image correlation. Journal of Construction and Architecture. 2023;25(2):191-206. (In Russ.). DOI: 10.31675/1607-1859-2023-25-2-191-206

7. Verstrynge E, De Wilder K, Drougkas A, Voet E, Van Balen K, Wevers M. Crack monitoring in historical masonry with distributed strain and acoustic emission sensing techniques. Construction and Building Materials. 2018;(162):898-907. DOI: 10.1016/j.conbuildmat.2018.01.103

8. Yapar O, Basu PK, Volgyesi P, Ledeczi A. Structural health monitoring of bridges with piezoelectric AE sensors. Engineering Failure Analysis. 2015;(56):150169. DOI: 10.1016/j.engfailanal.2015.03.009

9. Moreno-Gomez A, Perez-Ramirez CA, Dominguez-Gonzalez A, Valtierra-Rodriguez M, ChavezAlegria M, Amezquita-Sanchez JP. Sensors used in structural health monitoring. Archives of Computational Methods in Engineering. 2018;(25):901-918. DOI: 10.1007/s11831-017-9217-4

10. Arvindan Sivasuriyan Vijayan DS, LeemaRose A, Revathy J, Gayathri Monicka S, Adithya UR, Jebasingh Daniel J. Development of smart sensing technology approaches in structural health monitoring of bridge structures. Advances in Materials Science and Engineering. 2021;2021(4):2615029. DOI: 10.1155/2021/2615029

11. Cho S, Jo H, Jang S, Park J, Jung H.-J, Yun C.-B, Spencer BF. Jr, Seo J-W. Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses. Smart Structures and Systems. 2010;6(5-6):461-480. DOI: 10.12989/sss.2010.6.5_6.461

12. Barros F, Aguiar S, Sousa PJ, Cachaço A, Tavares PJ, Moreira PMGP, Ranzal D, Cardoso N, Fernandes N, Fernandes R, Henriques R, Cruz PM, Cannizzaro A. Displacement monitoring of a pedestrian bridge using 3D digital image correlation. Procedia Structural Integrity. 2022;(37):880-887. DOI: 10.1016/j.prostr.2022.02.022

13. Ferrer B, Acevedo P, Espinosa J, Mas D. Targetless image-based method for measuring displacements and strains on concrete surfaces with a consumer camera. Construction and Building Materials. 2015;(75):213-219. DOI: 10.1016/j.conbuildmat.2014.11.019

14. Lee H, Oh J. 3D Displacement measurement of railway bridge according to cyclic loads of different types of railcars with sequential photogrammetry. Applied Sciences. 2023;(13):art. no. 1359. DOI: 10.3390/app13031359

15. Acikgoz S, DeJong MJ, Soga K. Sensing dynamic displacements in masonry rail bridges using 2D digital image correlation. Structural Control and Health Monitoring. 2018;(25):art. no. e2187. DOI: 10.1002/stc.2187

16. Vegas ST, Lafdi K. A literature review of noncontact tools and methods in structural health monitoring. Engineering Technology Open Access Journal. 2021;4(1): art. no. 555626. DOI: 10.19080/ETOAJ.2021.04.555626

17. Payawal JMG, Kim D-K. Image-based structural health monitoring: A systematic review, Applied Sciences. 2023;(13):art. no. 968. DOI: 10.3390/app13020968

18. Goodman JW. Speckle phenomena in optics: Theory and applications. USA, Englewood, Roberts and Company Publishers. 2007:387 p.

19. Peiponen KE, Myllylä R, Priezzhev AV. Optical measurement techniques: Innovations for industry and the life sciences. Springer Series in Optical Sciences. 2009;(136):158 p. DOI: 10.1007/978-3-540-71927-4

20. Ryabukho VP, Tuchin VV. Coherent optical methods in measurement technology and biophotonics. Saratov, Satellit, 2009. (In Russ.).

21. Hild F, Roux S. Digital image correlation: from displacement measurement to identification of elastic properties – a review. Strain. 2006;42(2):69-80. DOI: 10.1111/j.1475-1305.2006.00258.x

22. Park KT, Torbol M, Kim S. Vision-based natural frequency identification using laser speckle imaging and parallel computing. Computer-Aided Civil and Infrastructure Engineering. 2018;33(1):51-63. DOI: 10.1111/mice.12312

23. Li L, Sytnik ID, Gubarev FA, Pekker YS. Evaluation of blood plasma coagulability by laser speckle correlation. Biomedical Engineering. 2018;52(3):177-180. DOI: 10.1007/s10527-018-9808-x

24. Li L, Gubarev FA, Cao Y, Liushnevskaya ID, Mostovshchikov AV. Laser speckle correlation technique application for remote characterization of metal nanopowders combustion. Applied Optics. 2021;60(22):65856592. DOI: 10.1364/AO.425696

25. Li L, Gubarev FA, Klenovskii MS, Bloshkina AI. Vibration measurement by means of digital speckle correlation. 2016 International Siberian Conference on Control and Communications (SIBCON). 2016;1–5 р. DOI: 10.1109/SIBCON.2016.7491753

26. Bloshkina AI, Li L, Gubarev FA, Klenovskii MS. Investigation of extracting information from vibrating objects by digital speckle correlation. 2016 17th International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices (EDM). 2016;637-641р. DOI: 10.1109/EDM.2016.7538813

27. Yuan Y, Huang J, Peng X, Xiong C, Fang J, Yuan А. Accurate displacement measurement via a selfadaptive digital image correlation method based on a weighted ZNSSD criterion. Optics and Lasers in Engineering. 2014;(52):75-85. DOI: 10.1016/j.optlaseng.2013.07.016

28. Gubarev FA, Li L, Klenovskii MS, Glotov A. Speckle pattern processing by digital image correlation. MATEC Web of Conferences. 2016;(48):art. no. 04003. https://doi.org/10.1051/matecconf/20164804003