Publicado

2021-05-10

Use of Unmanned Aircraft Systems for Bridge Inspection: A Review

Uso de sistemas de aeronaves no tripuladas para la inspección de puentes: una revisión

DOI:

https://doi.org/10.15446/dyna.v88n217.91879

Palabras clave:

Bridges; unmanned aircraft system; 3D reconstruction, infrared thermography, structural health monitoring (en)

Autores/as

This review describes the use of Unmanned Aircraft Systems (UAS) for bridge inspection, with an emphasis on Multi-rotor UAS. It depicts the different levels of automation and autonomy during UAS operation and what levels are achieved during inspections. A description of the payload of UAS consisting of the equipment required to acquire data and images is included. It also contains a compendium of the techniques used to create models from images in order to detect failures and perform Structural Health Monitoring (SHM) through techniques, such as: 3D reconstruction, infrared thermography, Structure From Motion (SFM), Convolutional Neural Network (CNN) and others. The software required to apply the mentioned techniques is also mentioned. It subsequently explains the generation of mathematical models to characterize the multirotor and generate efficient trajectories. Finally, the review concludes by describing the operational limitations of UAS and future challenges.

Esta revisión describe el uso de los Unmanned Aircraft Systems (UAS) para la inspección de puentes, haciendo énfasis en los UAS Multirrotor. Relaciona los diferentes niveles de automatización y autonomía durante la operación de los UAS y cuáles de esos niveles se logran durante la inspección. Hay una descripción de la carga paga del UAS compuesta por los equipos requeridos para adquirir datos e imágenes. Se incluye un compendio de las técnicas que se usan para la creación de modelos a partir de imágenes, con el propósito de detectar fallas y realizar Structural Health Monitoring (SHM) mediante técnicas como: reconstrucción 3D, termografía infrarroja, Structure From Motion (SFM), Convolutional Neural Network (CNN) entre otras, así como el software requerido para aplicarlas. Posteriormente explica la generación de modelos matemáticos para caracterizar los multirrotores y generar trayectorias eficientes. Finaliza describiendo las limitaciones operacionales de los UAS y los retos futuros.

Referencias

Morgenthal, G. and Hallermann, N., Quality assessment of Unmanned Aerial Vehicle (UAV) based visual inspection of structures. Advances in Structural Engineering, 17(3), pp. 289-302, 2014. DOI: 10.1260/1369-4332.17.3.289

Hallermann, N., Morgenthal, G. and Rodehorst, V., Unmanned Aerial Systems (UAS)-Survey and monitoring based on high-quality airborne photos, IABSE Symposium Report International Association for Bridge and Structural Engineering, pp. 1-8, 2015. DOI: 10.2749/222137815818358583

Hallermann, N., Morgenthal, G. and Rodehorst, V., Vision-based deformation monitoring of large-scale structures using Unmanned Aerial Systems, IABSE Symposium Report International Association for Bridge and Structural Engineering, pp. 2852-2859, 2014. DOI: 10.2749/222137814814070343

Doc 10019 AN/507 Manual sobre sistemas de aeronaves pilotadas a distancia (RPAS). In: International Civil Aviation Organization - OACI 2015. Montréal, [online]. 2015. [date of reference February, 4th of 2020]. Available at: https://www.icao.int/isbn/Lists/Publications/DispForm .aspx?ID=2757

A-NPA, No. 16/2005, policy for unmanned aerial vehicle (UAV) certification, European Aviation Safety Agency – EASA, K ̈oln, Germany, [online]. 2005. [date of reference February 4th of 2020]. Available at: https://www.easa.europa.eu/document-library/notices-of-proposed-amendments/npa-16-2005

UAEAC, U.A. Reglas generales de vuelo y de operación - RAC 91. [online]. Bogotá, Colombia. [date of reference February 5th of 2020]. Available at: https://www.aerocivil.gov.co/autoridad-de-la-aviacion-civil/reglamentacion/rac

Adabo, G.-J., Unmanned aircraft system for high voltage power transmission lines of Brazilian electrical system, AUVSI Unmanned Systems, pp.1556-1563, 2013.

Valavanis, K., Vachtsevanos, G., Handbook of unmanned aerial vehicles.1st ed, Netherlands, Springer. 2014, pp. 82-92.

Ułanowicz, L., Jóźko, M. and Szczepaniak, P., Controlling the operation process of the unmanned aerial system, Journal of KONBiN, 44(1), pp. 5-36. 2018. DOI:10.1515/jok-2017-0059.

Standard J3016_201806, Taxonomy and definitions for terms related to driving automation systems, NHTSA/SAE, [online]. 2014, [date of reference February 5th of 2020] Available at: https://www.sae.org/standards/content/j3016_201806/

Miranda, C.R., Garrido, M.R., Aguilar, B.L. and Guerrero, E.J., Drones modelado y control de cuadricópteros. 1ra ed., España, Alfaomega, 2020, pp. 3-36.

Pratt, K.S., CONOPS and autonomy recommendations for VTOL small unmanned aerial system based on Hurricane Katrina operations. Journal of Field Robotics 26(8), pp 636-650, 2009. DOI: 10.1002/rob.20304

Sa, I., Hrabar, S. and Corke, P., Inspection of pole-like structures using a vision-controlled VTOL UAV and shared autonomy, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4819-4826, 2014. DOI: 10.1109/IROS.2014.6943247

Cho, O.H., Ban, K.J. and Kim, E.K., Stabilized UAV flight system design for structure safety inspection, Conference on Advanced Communication Technology, pp. 1312-1316, 2014. DOI: 10.1109/ICACT.2014.6779172

Gaponov, I. and Razinkova, A., Quadcopter design and implementation as a multidisciplinary engineering course, in: Proceedings of IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), pp. H2B-16-H2B-19 2012. DOI: 10.1109/TALE.2012.6360335

Javir, A.V., Pawar, K., Dhudum, S., Patale, N. and Patil, S., Design, analysis and fabrication of quadcopter. Journal of Advance Research in Mechanical & Civil Engineering, 2(3), pp. 16-27, 2015.

Juniper, A., The complete guide to drones, 1st ed, Octopus publishing Group, Hachette, UK, 2015. pp. 106-127.

Elliott, A., Build your own drone manual: owners' workshop manual, 1st ed, Haynes North America Incorporated, Haynes, USA, 2017, pp. 98-151.

Pérez, J., Revisión sistemática de la literatura en ingeniería. Medellín, 2019.

Morgenthal, G, et al., Framework for automated UAS-based structural condition assessment of bridges. Automation in Construction, 97, pp. 77-95, 2019. DOI: 10.1016/j.autcon.2018.10.006

Tomiczek, AP., et al., Bridge inspections with small, unmanned aircraft systems: case studies. Journal of Bridge Engineering, 24(4), art. 05019003, 2019. DOI: 10.1061/(ASCE)BE.1943-5592.0001376

Khan, F., et al., Investigation on bridge assessment using unmanned aerial systems, Structures Congress, pp.404-413, 2015. DOI: 10.1061/9780784479117.035

Tarek, O. and Moncef, L.N., Thermal detection of subsurface delaminations in reinforced concrete bridge decks using unmanned aerial vehicle. American Concrete Institute, ACI Special Publication, 331, pp. 1-14, 2019.

Bolourian, N., et al., High-level framework for bridge inspection using LiDAR-equipped UAV, in: Proceedings of the International Symposium on Automation and Robotics in Construction, pp. 683-688, 2017. DOI: 10.22260/ISARC2017/0095

Cotua, O. y Causil, L., Diseño y ensamble de la arquitectura física de un Dron, para dosimetría ambiental en los cultivos bioenergéticos. BSc. Thesis, Department System Engineering, Cooperativa de Colombia University, Bogotá, Colombia, 2019.

Chiu, W.K., et al., Large structures monitoring using unmanned aerial vehicles. Procedia Engineering, 188, pp. 415-423, 2017. DOI: 10.1016/j.proeng.2017.04.503

Xiao, X, et al., Multi-view stereo matching based on self-adaptive patch and image grouping for multiple unmanned aerial vehicle imagery. Remote Sensing, 8(2), pp. 89, 2016. DOI: 10.3390/rs8020089

Kim, In-Ho, et al., Application of crack identification techniques for an aging concrete bridge inspection using an unmanned aerial vehicle. Sensors, 18(6), pp. 1881, 2018. DOI: 10.3390/s18061881

Kang, D., and Cha. Y.-J., Damage detection with an autonomous UAV using deep learning, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, art. 1059804, 2018. DOI: 10.1117/12.2295961

Liu, L., et al., CNN based automatic coating inspection system. Advances in Science, Technology and Engineering Systems Journal, 3(12), pp: 469-478, 2018. DOI: 10.25046/aj030655

Jin-Hwan, L. et al., Diagnosis of crack damage on structures based on image processing techniques and R-CNN using unmanned aerial vehicle (UAV), Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, art. 1059811, 2018. DOI: 10.1117/12.2296691

Rodríguez, J., Landassuri, V. y Flores, J., Reconocimiento de patrones numéricos para vuelo controlado de un AR Drone utilizando redes neuronales artificiales. Res. Comput. Sci, 107, pp. 61-71, 2015. DOI: 10.13053/RCS-107-1-6

Khaloo, A, et al., Unmanned aerial vehicle inspection of the Placer River Trail Bridge through image-based 3D modelling. Structure and Infrastructure Engineering, 14(1), pp. 124-136, 2018. DOI: 10.1080/15732479.2017.1330891

Sanchez-L.J.L. et al., A vision based aerial robot solution for the mission 7 of the international aerial robotics competition, in: International Conference on Unmanned Aircraft Systems (ICUAS). pp. 1391-1400, 2015. DOI: 10.1109/ICUAS.2015.7152435

Livyatan, H., et al., Dense structure from motion. U.S. Patent No. 9,959,595. May 1st of 2018.

Khaloo, A. and Lattanzi, D., Extracting structural models through computer vision, Structures Congress 2015, pp. 538-548, 2015. DOI: 10.1061/9780784479117.047

Dorafshan, S., Robert J.T. and Maguire, M., Fatigue crack detection using unmanned aerial systems in fracture critical inspection of steel bridges. Journal of Bridge Engineering, 23(10), art. 04018078, 2018. DOI: 10.1061/(ASCE)BE.19435592.0001291

Gillins, M., Gillins, D. and Parrish, C., Cost-effective bridge safety inspections using unmanned aircraft systems (UAS), Geotechnical and Structural Engineering Congress, pp. 1931-1940, 2016. DOI: 10.1061/9780784479742.165

Kang, D. and Cha, Y-J., Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo‐tagging. Computer‐Aided Civil and Infrastructure Engineering. 33(10), pp. 885-902, 2018. DOI: 10.1111/mice.12375

Cha, Y‐J., Wooram, C. and Büyüköztürk, O., Deep learning‐based crack damage detection using convolutional neural networks. Computer‐Aided Civil and Infrastructure Engineering, 32(5), pp. 361-378, 2017. DOI: 10.1111/mice.12263

Cha, Y‐J., et al., Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types. Computer‐Aided Civil and Infrastructure Engineering, 33(9), pp. 731-747, 2017. DOI: 10.1111/mice.12334

ASTM D4788-03, Standard test method for detecting delaminations in bridge deck using infrared thermography, American Society of Testing Materials, [online]. 2013. [date of reference March 26th of 2020]. Available at. https://www.aenor.com/normas-y-libros/buscador-de-normas/astm?c=026572

Wang, J., Peilun, F. and Robert, X., Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51, pp. 52-60, 2019. DOI: 10.1016/j.jmsy.2019.03.002

Han, K., Lin, J. and Golparvar, F., A formalism for utilization of autonomous vision-based systems and integrate project models for construction progress monitoring, in: Proceedings of the Conference on Autonomous and Robotic Construction of Infrastructure, pp. 124-138, 2015.

Munguia, R., Urzua, S., Bolea, Y. and Grau, A., Vision-based SLAM system for unmanned aerial vehicles. Sensors, 16, art. 372, 2016. DOI: 10.3390/s16030372

Jiarong, L. and Zhang, F., Loam livox: a fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV, in: International Conference on Robotics and Automation (ICRA), pp. 3126-3131, 2020. DOI: 10.1109/ICRA40945.2020.9197440

Andaluz, V. et al., Robot nonlinear control for unmanned aerial vehicles’ multitasking. Assembly Automation, 38(5), pp. 645-660, 2018. DOI: 10.1108/AA-02-2018-036

Kung, O. et al., Simplified building models extraction from ultra-light uav imagery. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(1C22), pp.217-222, 2011. DOI: 10.5194/isprsarchives-XXXVIII-1-C22-217-2011

Nazmus, S., Muhammad, M. and Sharif, M., Application framework for forest surveillance and data acquisition using unmanned aerial vehicle system, International Conference on Engineering Technology and Technopreneurship (ICE2T), pp. 1-6, 2017. DOI: 10.1109/ICE2T.2017.8215979

Jeremy, H. et al., Design of guaranteed safe maneuvers using reachable sets: autonomous quadrotor aerobatics in theory and practice, IEEE International Conference on Robotics and Automation, pp. 1649-1654, 2010. DOI: 10.1109/ROBOT.2010.5509627

Bachrach, R. et al., On the design and use of a micro air vehicle to track and avoid adversaries The International Journal of Robotics Research, 29(5), pp 529-546, 2010. DOI: 10.1177/0278364909348805

Mistler, V., Benallegue, A. and M´sirdi, M., Exact linearization and noninteracting control of a 4 rotors helicopter via dynamic feedback, IEEE International Workshop on Robot and Human Interactive Communication, pp. 586-593, 2001. DOI: 10.1109/ROMAN.2001.981968

Belkheiri, M., et al., Different linearization control techniques for a quadrotor system, in: 2nd International Conference on Communications, Computing and control Applications (CCCA), 2012, pp. 1-6. DOI: 10.1109/CCCA.2012.6417914

Chovancová, A., Fico, T., Hubinsky, P. and Duchon, F., Comparison of various quaternion-based control methods applied to quadrotor with disturbance observer and position stimator. Robotics and Autonomous Systems, 79, pp. 87-98, 2015. DOI: 10.1016/j.robot.2016.01.011

Bouabdallah, S., Noth, A. and Siegwart, R., PID vs LQ control techniques applied to an indoor micro quadrotor, International Conference on intelligent Robots and Systems, pp. 2451-2456, 2004. DOI: 10.1109/IROS.2004.1389776

Reyes, V. et al., LQR control for a quadrotor using unit quaternions: modeling and simulation, International Conference on Electronics, Communications and Computing, CONIELECOMP, pp. 172-178, 2013. DOI:10.1109/CONIELECOMP.2013.6525781

Fresk, E. and Nikolakopoulos, G., Full quaternion-based attitude control for a quadrotor, European Control Conference, pp. 3864-3869, 2013. DOI: 10.23919/ECC.2013.6669617

Ajmera, J., Sankaranarayanan, V., Point-to-point control of a quadrotor: theory and experiment, IFAC-PapersOnLine, 49(1), pp. 401-406, 2016. DOI: 10.1016/j.ifacol.2016.03.087

Ryan, T. and Kim, H., LMI- based gain synthesis for simple robust quadrotor control, IEEE Transaction on Automation Science and Engineering, 10(4), pp. 1173-1178, 2013. DOI: 10.1109/TASE.2013.2259156

Wang, X., Shirinzadeh, B. and Ang, M., Nonlinear double - integral observer and application to quadrotor aircraft. IEEE Transactions on Industrial Electronics, 62(2), pp. 1189-1200, 2015. DOI: 10.1109/TIE.2014.2341571

Raffo, G., Ortega, M. and Rubio, F., Backstepping/nonlinear H∞ control for path tracking of a quadrotor unmanned aerial vehicle, American Control Conference, pp. 3356-3361, 2008. DOI: 10.1109/ACC.2008.4587010

Lopes, R., Santana, P., Borges, G. and Ishihara, J., Model predictive control applied to tracking and attitude stabilization of a VTOL quadrotor aircraft, International Congress of Mechanical Engineering, COBEM, pp. 176-185, 2011

Dong, W., Gu, G.Y., Zhu, X. and Ding, H., High - performance trajectory tracking control of a quadrotor with disturbance observer. Sensors and Actuators, 211, pp. 67-77, 2014. DOI: 10.1016/j.sna.2014.03.011

Dong, W., Gu, G.Y., Zhu, X. and Ding, H., An adaptive trajectory control for UAV using a real-time architecture, International Conference on Unmanned Aircraft Systems, ICUAS, pp. 32-42, 2014. DOI: 10.1109/ICUAS.2014.6842236

Nicol, C., Macnab, C. and Serrano, A., Robust adaptive control of a quadrotor helicopter. Mechatronics, 21, pp. 927-938, 2011. DOI: 10.1016/j.mechatronics.2011.02.007

Salaan, C. et al., Close visual bridge inspection using a UAV with a passive rotating spherical shell. Journal of Field Robotics, 35(6), pp. 850-867, 2018. DOI: 10.1002/rob.21781

Cómo citar

IEEE

[1]
D. Aldana Rodriguez, D. L. Ávila Granados, y J. A. . Villalba-Vidales, «Use of Unmanned Aircraft Systems for Bridge Inspection: A Review», DYNA, vol. 88, n.º 217, pp. 32–41, may 2021.

ACM

[1]
Aldana Rodriguez, D., Ávila Granados, D.L. y Villalba-Vidales, J.A. 2021. Use of Unmanned Aircraft Systems for Bridge Inspection: A Review. DYNA. 88, 217 (may 2021), 32–41. DOI:https://doi.org/10.15446/dyna.v88n217.91879.

ACS

(1)
Aldana Rodriguez, D.; Ávila Granados, D. L.; Villalba-Vidales, J. A. . Use of Unmanned Aircraft Systems for Bridge Inspection: A Review. DYNA 2021, 88, 32-41.

APA

Aldana Rodriguez, D., Ávila Granados, D. L. & Villalba-Vidales, J. A. . (2021). Use of Unmanned Aircraft Systems for Bridge Inspection: A Review. DYNA, 88(217), 32–41. https://doi.org/10.15446/dyna.v88n217.91879

ABNT

ALDANA RODRIGUEZ, D.; ÁVILA GRANADOS, D. L.; VILLALBA-VIDALES, J. A. . Use of Unmanned Aircraft Systems for Bridge Inspection: A Review. DYNA, [S. l.], v. 88, n. 217, p. 32–41, 2021. DOI: 10.15446/dyna.v88n217.91879. Disponível em: https://revistas.unal.edu.co/index.php/dyna/article/view/91879. Acesso em: 13 mar. 2026.

Chicago

Aldana Rodriguez, Didier, Diego Leonardo Ávila Granados, y Jorge Armando Villalba-Vidales. 2021. «Use of Unmanned Aircraft Systems for Bridge Inspection: A Review». DYNA 88 (217):32-41. https://doi.org/10.15446/dyna.v88n217.91879.

Harvard

Aldana Rodriguez, D., Ávila Granados, D. L. y Villalba-Vidales, J. A. . (2021) «Use of Unmanned Aircraft Systems for Bridge Inspection: A Review», DYNA, 88(217), pp. 32–41. doi: 10.15446/dyna.v88n217.91879.

MLA

Aldana Rodriguez, D., D. L. Ávila Granados, y J. A. . Villalba-Vidales. «Use of Unmanned Aircraft Systems for Bridge Inspection: A Review». DYNA, vol. 88, n.º 217, mayo de 2021, pp. 32-41, doi:10.15446/dyna.v88n217.91879.

Turabian

Aldana Rodriguez, Didier, Diego Leonardo Ávila Granados, y Jorge Armando Villalba-Vidales. «Use of Unmanned Aircraft Systems for Bridge Inspection: A Review». DYNA 88, no. 217 (mayo 10, 2021): 32–41. Accedido marzo 13, 2026. https://revistas.unal.edu.co/index.php/dyna/article/view/91879.

Vancouver

1.
Aldana Rodriguez D, Ávila Granados DL, Villalba-Vidales JA. Use of Unmanned Aircraft Systems for Bridge Inspection: A Review. DYNA [Internet]. 10 de mayo de 2021 [citado 13 de marzo de 2026];88(217):32-41. Disponible en: https://revistas.unal.edu.co/index.php/dyna/article/view/91879

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1. Raffaele Zinno, Sina Shaffiee Haghshenas, Giuseppe Guido, Kaveh Rashvand, Alessandro Vitale, Ali Sarhadi. (2022). The State of the Art of Artificial Intelligence Approaches and New Technologies in Structural Health Monitoring of Bridges. Applied Sciences, 13(1), p.97. https://doi.org/10.3390/app13010097.

2. Bhupesh Chand, Frezer Ayele, Ian Pineiro-Dakers, Reihaneh Samsami, Byungik Chang. (2026). Uncrewed Aerial System (UAS) Applications in Bridge Inspection: A Comprehensive Review of Platforms, Sensors, and Operational Effectiveness. Drones, 10(2), p.144. https://doi.org/10.3390/drones10020144.

3. Changdong Zhou, Mingjing Dai, Feng Wang, Yu Dong, Xinghua Chen, Chenghuan He. (2025). An innovative UAV and deep learning-based framework for automatic bridge crack detection and measurement. The Journal of Supercomputing, 81(15) https://doi.org/10.1007/s11227-025-07903-6.

4. Shanshan Wu, Guobing Deng, Jiawei Zan, Tingrong Xie. (2025). Application of Drones and Artificial Intelligence in Highway and Bridge Inspection. Journal of Civil and Transportation Engineering, 2(3), p.1. https://doi.org/10.62517/jcte.202506301.

5. Honghu Chu, Lizhi Long, Jingjing Guo, Huaqing Yuan, Lu Deng. (2024). Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images. Computer-Aided Civil and Infrastructure Engineering, 39(4), p.539. https://doi.org/10.1111/mice.13052.

6. Christos Karakostas, Giuseppe Quaranta, Eleni Chatzi, Abdullah Can Zülfikar, Oğuzhan Çetindemir, Guido De Roeck, Michael Döhler, Maria Pina Limongelli, Geert Lombaert, Nurdan Memişoğlu Apaydın, Vikram Pakrashi, Costas Papadimitriou, Ali Yeşilyurt. (2024). Seismic assessment of bridges through structural health monitoring: a state-of-the-art review. Bulletin of Earthquake Engineering, 22(3), p.1309. https://doi.org/10.1007/s10518-023-01819-3.

7. Honghu Chu, Wei Wang, Lu Deng. (2022). Tiny‐Crack‐Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks. Computer-Aided Civil and Infrastructure Engineering, 37(14), p.1914. https://doi.org/10.1111/mice.12881.

8. Tamara Phillips Fudge, Susan Shepherd Ferebee. (2024). Cybersecurity Issues and Challenges in the Drone Industry. Advances in Information Security, Privacy, and Ethics. , p.108. https://doi.org/10.4018/979-8-3693-0774-8.ch005.

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