Article Article
Comprehensive Structural Health Monitoring at Local and Global Level with Vision-based Technologies

Condition assessment of aging structures has reached at a critical level in the US. Evaluation of accurate and fast methods without creating even further damage has become a necessity in structural health monitoring area. This study tries to explain the new methods and frameworks that may be replaced with conventional evaluation methods to conduct the same studies faster, requiring less cost and labor and with the same or better accuracy. The main idea is to implement computer vision and infrared imaging based technologies into local and global structural health monitoring practices. Brief explanations of their working principle along with the results from past studies and future recommendations are given. Finally, a possible decision-making framework combining the explained methods is proposed.


[1] FHWA and FTA, “2013 Status of the Nation’s Highways, Bridges, and Transit: Conditions & Performance,” 2014.

[2] FHWA, “Tables of Frequently Requested NBI Information (Deficient Bridges by Year Built & Bridges by Deck Structure Type),” 2017. [Online]. Available: [Accessed: 20- Feb-2017].

[3] ASCE, “2013 Report Card for America’s Infrastructure,” 2013. [Online]. Available:

[4] GPO, “Electronic Code of Federal Regulations,” U.S. Government Publishing Officem, 2015. .

[5] M. C. Brown, J. P. Gomez, M. L. Hammer, and J. M. Hooks, “Long-Term Bridge Performance High Priority Bridge Performance Issues,” McLean, VA, 2014.

[6] N. Gucunski, S. Kee, H. La, B. Basily, and A. Maher, “Delamination and concrete quality assessment of concrete bridge decks using a fully autonomous RABIT platform,” Struct. Monit. Maint., vol. 2, no. 1, pp. 19–34, 2015.

[7] S. Hiasa, R. Birgul, and F. N. Catbas, “Infrared thermography for civil structural assessment: demonstrations with laboratory and field studies,” J. Civ. Struct. Heal. Monit., vol. 6, no. 3, pp. 619–636, Jul. 2016.

[8] S. Hiasa, F. N. Catbas, M. Matsumoto, and K. Mitani, “Considerations and Issues in the Utilization of Infrared Thermography for Concrete Bridge Inspection at Normal Driving Speeds,” J. Bridg. Eng., vol. in press, 2017.

[9] S. Hiasa, F. N. Catbas, M. Matsumoto, and K. Mitani, “Monitoring Concrete Bridge Decks using Infrared Thermography with High Speed Vehicles,” Struct. Monit. Maintenance, An Int. J., vol. 3, no. 3, pp. 277–296, 2016.

[10] S. Hiasa, R. Birgul, and F. N. Catbas, “Effect of Interior Defect Size on Infrared Thermography for Concrete Bridge Deck Inspection,” TRB 96th Annu. Meet., no. 17–04071, 2017.

[11] S. Hiasa, R. Birgul, and F. Necati Catbas, “A data processing methodology for infrared thermography images of concrete bridges,” Comput. Struct., vol. 190, pp. 205–218, Oct. 2017.

[12] S. Hiasa, “Investigation of Infrared Thermography for Subsurface Damage Detection of Concrete Structures,” Electronic Theses and Dissertations. Paper 5063. <>, 2016.

[13] A. Watase, R. Birgul, S. Hiasa, M. Matsumoto, K. Mitani, and F. N. Catbas, “Practical identification of favorable time windows for infrared thermography for concrete bridge evaluation,” Constr. Build. Mater., vol. 101, pp. 1016–1030, 2015.

[14] S.-H. Kee, T. Oh, J. S. Popovics, R. W. Arndt, and J. Zhu, “Nondestructive Bridge Deck Testing with Air-Coupled Impact-Echo and Infrared Thermography,” J. Bridg. Eng., vol. 17, no. 6, pp. 928–939, 2012.

[15] C. Maierhofer, A. Brink, M. Ro, and H. Wiggenhauser, “Quantitative impulse-thermography as non-destructive testing method in civil engineering – Experimental results and numerical simulations,” vol. 19, pp. 731–737, 2005.

[16] S. Hiasa, R. Birgul, and F. N. Catbas, “Investigation of effective utilization of infrared thermography (IRT) through advanced finite element modeling,” Constr. Build. Mater., vol. 150, pp. 295–309, 2017.

[17] ASTM, Standard Test Method for Detecting Delaminations in Bridge Decks Using Infrared Thermography, D4788-3rd ed., no. Reapproved 2013. West Conshohocken, PA, USA: ASTM International, 2014.

[18] N. Gucunski, S. Nazarian, D. Yuan, and D. Kutrubes, “Nondestructive Testing to Identify Concrete Bridge Deck Deterioration,” Transportation Research Board, SHRP 2 Report S2-R06A-RR-1, Washington, D.C., USA, 2013.

[19] T. Oh, S. Kee, R. W. Arndt, J. S. Popovics, M. Asce, and J. Zhu, “Comparison of NDT Methods for Assessment of a Concrete Bridge Deck,” J. Eng. Mech., vol. 139, no. March, pp. 305–314, 2013.[20] F. N. Catbas, M. Gul, R. Zaurin, H. B. Gokce, T. Terrell, T. Dumlupinar, and D. Maier, “Long term bridge maintenance monitoring demonstration on a movable bridge,” Final Rep. Res. Proj., no. June, p. 186, 2010.

[21] Z. Aktan, A.E.; Catbas, Necati; Turer, Ahmet; Zhang, “STRUCTURAL IDENTIFICATION: ANALYTICAL ASPECTS By Emin Aktan,” ASCE J. Struct. Eng., vol. 124, no. July, pp. 817–829, 1998.

[22] F. N. Catbas and  a. E. Aktan, “Condition and Damage Assessment: Issues and Some Promising Indices,” J. Struct. Eng., vol. 128, no. 8, pp. 1026–1036, 2002.

[23] R. Zaurin and F. N. Catbas, “Integration of computer imaging and sensor data for structural health monitoring of bridges,” Smart Mater. Struct., vol. 19, pp. 1–15, 2010.

[24] R. Zaurin and F. Necati Catbas, “Structural health monitoring using video stream, influence lines, and statistical analysis,” Struct. Heal. Monit., vol. 10, no. 3, pp. 309–332, 2010.

[25] N. Catbas, R. Zaurin, M. Gul, and H. B. Gokce, “Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating,” J. Bridg. Eng., vol. 17, no. 4, pp. 662–670, 2012.

[26] F. N. Catbas, M. Gul, H. B. Gokce, R. Zaurin, D. M. Frangopol, and K. A. Grimmelsman, “Critical issues, condition assessment and monitoring of heavy movable structures: emphasis on movable bridges,” Struct. Infrastruct. Eng., vol. 10, no. 2, pp. 261–276, 2014.

[27] T. Khuc and F. Catbas, “Non-target displacement measurement of structures using vision based approaches,” in Bridge Maintenance, Safety, Management and Life Extension, no. July 2014, CRC Press, 2014, pp. 668–675.

[28] M. Gul, F. N. Catbas, and H. Hattori, “Image-Based Monitoring of Open Gears of Movable Bridges for Condition Assessment and Maintenance Decision Making,” J. Comput. Civ. Eng., vol. 29, no. 2, p. 4014034, 2015.

[29] R. Zaurin, T. Khuc, F. N. Catbas, and F. Asce, “Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge,” J. Bridg. Eng., vol. 21, no. 6, pp. 1–27, 2015.

[30] T. Khuc, “Computer Vision Based Structural Identification Framework for Bridge Health Mornitoring,” University of Central Florida, 2016.

[31] T. Khuc and F. N. Catbas, “Computer vision-based displacement and vibration monitoring without using physical target on structures,” Struct. Infrastruct. Eng., vol. 2479, no. February, pp. 1–12, 2016.

[32] T. Khuc and F. N. Catbas, “Completely contactless structural health monitoring of real-life structures using cameras and computer vision,” Struct. Control Heal. Monit., vol. 24, no. 1, p. e1852, Jan. 2017.

[33] V. Racic, J. M. W. Brownjohn, and A. Pavic, “Reproduction and application of human bouncing and jumping forces from visual marker data,” J. Sound Vib., vol. 329, no. 16, pp. 3397–3416, 2010.

[34] O. Celik, F. N. Catbas, N. T. Do, M. Gul, O. Abdeljaber, A. Younis, and O. Avci, “Issues, Codes and Basic Studies for Stadium Dynamics,” in Proceedings of the Second International Conference on Infrastructure Management, Assessment and Rehabilitation Techniques, 2016, no. April.

[35] O. Celik, N. T. Do, O. Abdeljaber, M. Gul, O. Avci, and F. N. Catbas, “Recent issues on stadium monitoring and serviceability: A review,” Conf. Proc. Soc. Exp. Mech. Ser., vol. 4, pp. 411–416, 2016.

[36] X. W. Ye, T. Yi, C. Z. Dong, T. Liu, and H. Bai, “Multi-point displacement monitoring of bridges using a vision-based approach,” Wind Struct., vol. 20, no. 2, pp. 315–326, 2015.

[37] T. Khuc and F. N. Catbas, “Computer vision-based displacement and vibration monitoring without using physical target on structures,” Struct. Infrastruct. Eng., pp. 1–12, 2016.

[38] M. Rashidi, B. Samali, and P. Sharafi, “A new model for bridge management: Part A: condition assessment and priority ranking of bridges,” Aust. J. Civ. Eng., vol. 14, no. 1, pp. 35–45, 2016.

[39] K. L. Rens, C. L. Nogueira, and D. J. Transue, “Bridge management and nondestructive evaluation,” J. Perform. Constr. Facil., vol. 19, no. 1, pp. 3–16, 2005.

[40] M. Rashidi, B. Samali, and P. Sharafi, “A new model for bridge management: Part B: decision support system for remediation planning,” Aust. J. Civ. Eng., vol. 14, no. 1, pp. 46–53, 2016.

[41] FDOT, “Florida DOT Bridge Inspection Field Guide,” 2016.

[42] Z. Lounis and D. J. Vanier, “Optimization of Bridge Maintenance Management Using Markovian Models,” Proc. Int. Conf. Short Mediu. Span Bridg., vol. 2, pp. 1045–1053, 1998.

[43] C. Liu, A. Hammad, and Y. Itoh, “Multiobjective optimization of bridge deck rehabilitation using a genetic algorithm,” Comput. Aided Civ. Infrastruct. Eng., vol. 12, no. 6, pp. 431–443, 1997.

[44] S. Abu Dabous and S. Alkass, “Decision support method for multi‐criteria selection of bridge rehabilitation strategy,” Constr. Manag. Econ., vol. 26, no. 786929861, pp. 883–893, 2008.

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