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Infrastructure assessment post-disaster: Remotely sensing bridge structural damage by unmanned aerial vehicle in low-light conditions

Christopher A. Baker, MPA, PhD Student, Randy R. Rapp, DMgt, PE, CCP, MASCE, Emad Elwakil, PhD, PE, CCP, PMP, Jiansong Zhang, PhD


The initial experiment explores the viability of using a low-cost unmanned aerial vehicle equipped with thermal imaging and lowlight camera to assess structural damage to steel girders. Damage assessments following natural disasters are daunting and arduous tasks that are resources intensive and dangerous. Unmanned aerial vehicles with remote sensing (UAV-RS) technology have been used in recent largescale disaster events such as Hurricanes Katerina, Harvey, Irma, and Maria as well as others. Current assessment methods of structures include inspectors physically conducting detailed and rapid surveys of damage with or without the assistance of special equipment, use of helicopters, satellite imagery, and new innovative methods using UAV-RS technology.

The initial experiment utilized the Steel Bridge Research, Inspection, Training, and Engineering and Training Center (S-BRITE) facility at Purdue University and a small building in Lafayette, Indiana. Two steel girders located at S-BRITE were used in the experiment with damages that render them structurally deficient. The small building was used for semiautonomous inspection during hours of darkness.

Most scientific studies have focused on using UAV-RS during hours of daylight. In this article, the authors explore the use of UAV-RS during low-light conditions (ie, early evening nautical twilight and night) for detecting global damage to steel girders. The authorsÆ goal is to present evidence for further study in the use of UAV-RS during low-light conditions for inspecting structures to include primary load bearing members. The authors conclude that while the UAV-RS can detect global damage in low visibility conditions, further experiments in varying low-light conditions including 3D imaging and semiautonomous inspection are necessary for structural damage assessments.


UAV-RS, disaster, infrastructure, bridges, thermal imaging, low-light

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NOAA National Centers for Environmental Information (NCEI): U.S. billion-dollar weather and climate disasters. 2018. Available at Accessed July 20, 2018.

Smith A, Katz R: U.S. billion-dollar weather and climate disasters: Data sources, trends, accuracy and biases. Nat Hazards. 2013; 67(2): 387-410. doi:10.1007/s11069-013-0566-5.

Lee G, Mohan S, Huang C, et al.: A Study of U.S. Bridge Failures (1980-2012). Buffalo, NY: MCEER, 2013. Technical Report MCEER-13-0008.

US Department of Transportation, Bureau of Transportation Statistics: Freight facts and figures. 2010. Available at Accessed June 4, 2018.

Ironcore: ASCEÆs 2017 American Infrastructure Report Card: GPA: D+. ASCEÆs 2017 Infrastructure Report Card. Available at Accessed May 12, 2018.

Ryan T, Mann E, Chill Z, et al.: Bridge InspectorÆs Manual (BIRM). Arlington, VA: Federal Highway Administration, National Highway Institute, 2012.

Otero, Luis D, Gagliardo N, et al.: Proof of Concept for Using Unmanned Aerial Vehicles for High Mast Pole and Bridge Inspections (Publication No. BDV28 TWO 977-02). Florida Department of Transportation, 2015.

Adams S, Friedland C, Levitan M: Unmanned aerial vehicle data acquisition for damage assessment in hurricane events. 8th International Workshop on Remote Sensing for Disaster Management, Tokyo, Japan. 2010; 7: 1-8. Available at Accessed June 2, 2018.

Adams S, Friedland C: A survey of unmanned aerial vehicle (UAV) usage for imagery collection in disaster research and management. In Proceedings of the 9th International Workshop on Remote Sensing for Disaster Response, Stanford, CA. 2011: 1-9. Available at Accessed June 2, 2018.

Pratt K, Murphy R, Stover S: Overview of requirements for semi-autonomous flight in miniature UAVs for structural inspection. AUVSIÆs Unmanned Systems North America. Orlando, FL: Association for Unmanned Vehicle Systems International, 2006. Available at Accessed June 1, 2018.

Murphy R, Stover S, Pratt K, et al.: Cooperative damage inspection with unmanned surface vehicle and micro unmanned aerial vehicle at Hurricane Wilma. IEEE/RSJ International Conference on Intelligent Robots and Systems 2006. doi:10.1109/iros.2006.282304.

Murphy R, Steimle E, Griffin C, et al.: Cooperative use of unmanned sea surface and micro aerial vehicles at Hurricane Wilma. J. Field Robot. 2008; 25(3): 164-180. doi:10.1002/rob.20235.

Chou T, Yeh M, Chen Y, et al.: Disaster monitoring and management by the unmanned aerial vehicle technology. In: ISPRS Technical Commission VII Symposium, Vol. 38(7B), July 5-7, 2010; Vienna.

Omar T, Nehdi ML: Remote sensing of concrete bridge decks using unmanned aerial vehicle infrared thermography. Autom Constr. 2017; 83: 360-371. doi:10.1016/j.autcon.2017.06.024.

Lovelace B, Wells J: Unmanned Aircraft System Bridge Inspection Demonstration Project Phase II Final Report. Saint Paul, MN: Minnesota Department of Transportation, 2017: 1-174. Technical Report MN/RC 2017-18.

Siebert S, Teizer J: Mobile 3-D mapping for surveying earthwork project using an unmanned aerial vehicle (UAV) system. Autom Constr., 2014; 41: 1-14. doi:10.1016/j.autcon.2014.01.004.

Duverneuil B. Unmanned Aerial Vehicles in Response to Natural Disasters, 2016. Available at Accessed June 12, 2018.

Vaghefi K, Oats RC, Harris DK, et al.: Evaluation of commercially available remote sensors for highway bridge condition assessment. J Bridge Eng. 2012; 17(6): 886-895.

Erdelj M, Natalizio E, Chowdhury K, et al.: Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Comput. 2017; 16(1): 24-32. doi:10.1109/mprv.2017.11.

United States Naval Observatory: Rise, set, and twilight definitions. Naval Oceanography Portal. 2018. Available at Accessed June 2, 2018.

ASTM: Standard Specification for Structural Steel for Bridges. West Conshohocken, PA: American Society for Testing and Materials, 2013. A709/A709M.

Optotherm: Emissivity in the infrared. Emissivity values. Available at Accessed June 20, 2018.



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