Article http://dx.doi.org/10.26855/ea.2021.06.002

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring


Meisam Gordan1, Zubaidah Ismail1,*, Khaled Ghaedi1,2, Zainah Ibrahim1, Huzaifa Hashim1, Haider Hamad Ghayeb1, Marieh Talebkhah3

1Department of Civil Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia.

2Center of Research & Development, PASOFAL Engineering, 52200, Kuala Lumpur, Malaysia.

3Department of Computer and Communication Systems Engineering, Universiti Putra Malaysia, 43400, Malaysia.

*Corresponding author: Zubaidah Ismail

Published: February 24,2021


Remote sensing with Unmanned Aerial Systems (UASs) is a game-changer in various fields such as environmental monitoring, surveillance, aerial photography, digital communications, search and rescue operations and military. This paper presents one of the most economical and yet the most effective approaches used in Structural Health Monitoring (SHM). Herein, a review of the recent advances, applications and future perspective of UASs, i.e. drones in SHM is discussed. Drones have become popular in several developed countries in recent years. However, the use of drones is still in the infancy stage of development in developing countries. The development of drones in the last decade has marked a new era in remote sensing, providing data of unprecedented spatial, spectral, and temporal resolution. This is due to the fact that UASs are low cost aerial robots, that require little preparation and infrastructure and can be equipped with any number of sensors or cameras making them ideal for monitoring the environment. To this end, drones offer an opportunity to infrastructures the existing gap between field observations and remote sensing by providing high spatial detail over relatively large areas in a cost-effective way.


[1] C. A. Marinho, C. De Souza, T. Motomura, A. G. da S. Silva, (2012). In-service flares inspection by unmanned aerial vehicles (UAVs), in: 18th World Conf. Nondestruct. Test., Durban, South Africa, 2012: pp. 16-20.

[2] R. R. S. de Melo, D. B. Costa, J. S. Álvares, J. Irizarry. (2017). Applicability of unmanned aerial system (UAS) for safety in-spection on construction sites, Saf. Sci. 98(2017), 174-185. doi:10.1016/j.ssci.2017.06.008.

[3] N. Metni, T. Hamel. (2007). A UAV for bridge inspection: Visual servoing control law with orientation limits, Autom. Constr., 17(2007), 3-10. doi:10.1016/j.autcon.2006.12.010.

[4] C. Eschmann, C. M. Kuo, C. H. Kuo, C. Boller. (2012). Unmanned aircraft systems for remote building inspection and moni-toring, Saarbrücken, Germany, 2012.

[5] S. Sadovnychiy. (2004). Count buffon on cultural changes of the physical environment, in: 8th WSEAS Int. Conf. Syst., Athens, Greece, 2004. doi:10.1111/j.1467-8306.1960.tb00325.x.

[6] H. Kim, S. H. Sim, S. Cho. (2015). Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing, in: 6th Int. Conf. Adv. Exp. Struct. Eng., Urbana-Champaign, United States, 2015.

[7] U. Papa, S. Ponte. (2018). Preliminary design of an unmanned aircraft system for aircraft general visual inspection, Electron. 7 (2018). doi: 10.3390/electronics7120435.

[8] A. A. Ab Rahman, W. S. Wan Mohd Jaafar, K. N. Abdul Maulud, N. M. Noor, M. Mohan, A. Cardil, C. A. Silva, N. N. Che’Ya, N. I. Naba. (2019). Applications of Drones in Emerging Economies: A case study of Malaysia, in: 6th Int. Conf. Sp. Sci. Commun. Iconsp., Johor Bahru, Malaysia, 2019. doi:10.1109/IconSpace.2019.8905962.

[9] S. Chen, Y. Tong. (2012). Integrated Remote Sensing and Visualization: Phase Two, Web-GIS Based Bridg. Inf. Database. 4 (2012). https://ntl.bts.gov/lib/54000/54800/54876/PhaseII_USDOT_UNCC_Vol4-FinalRpt.pdf.

[10] S. Park, Y. Yamaguchi, D. Kim. (2013). Remote Sensing of Environment Polarimetric SAR remote sensing of the 2011 Tohoku earthquake using ALOS/PALSAR, Remote Sens. Environ. 132 (2013) 212–220. doi:10.1016/j.rse.2013.01.018.

[11] R. Kumar Sing. (2005). Pattern Recognition in Remote-Sensing Imagery using Data Mining and Statistical Techniques, PhD Thesis, Purdue University, 2005.

[12] L. Dong, J. Shan. (2013). A comprehensive review of earthquake-induced building damage detection with remote sensing tech-niques, ISPRS J. Photogramm. Remote Sens. 84 (2013), 85-99. doi:10.1016/j.isprsjprs.2013.06.011.

[13] S. Ghaffarian, N. Kerle, T. Filatova. (2018). Remote sensing-based proxies for urban disaster risk management and resilience: A review, Remote Sens. 10(2018). doi: 10.3390/rs10111760.

[14] M. Gordan, Z. Ismail, H. A. Razak, Z. Ibrahim. (2017). Vibration-Based Structural Damage Identification Using Data Mining, in: 24th Int. Congr. Sound Vib. London, 2017.

[15] M. Gordan, H. A. Razak, Z. Ismail, K. Ghaedi. (2017). Recent developments in damage identification of structures using data mining, Lat. Am. J. Solids Struct. 14(2017), 2373-2401. doi:10.1590/1679-78254378.

[16] M. Gordan, H. A. Razak, Z. Ismail, K. Ghaedi, Z. X. Tan, H. H. Ghayeb. (2020). A hybrid ANN-based imperial competitive algorithm methodology for structural damage identification of slab-on-girder bridge using data mining, Appl. Soft Comput. J. 88 (2020) 106013. doi:10.1016/j.asoc.2019.106013.

[17] M. Gordan, Z. Ismail, H. Abdul Razak, K. Ghaedi, Z. Ibrahim, Z. X. Tan, H. H. Ghayeb. (2020). Data mining-based damage identification of a slab-on-girder bridge using inverse analysis, Measurement. 151(2020), 107175. doi:10.1016/j.measurement.2019.107175.

[18] K. Ghaedi, Z. Ibrahim. (2017). Earthquake Prediction, in: T. Zouaghi (Ed.), Earthquakes-Tectonics, Hazard Risk Mitig., InTech, 2017: pp. 205-227. doi:10.5772/65511.

[19] Z. X. Tan, D. P. Thambiratnam, T. H. T. Chan, M. Gordan, H. Abdul Razak. (2019). Damage detection in steel-concrete com-posite bridge using vibration characteristics and artificial neural network, Struct. Infrastruct. Eng. (2019), 1-15. doi:10.1080/15732479.2019.1696378.

[20] M. Gordan, Z. Ismail, Z. Ibrahim, H. Hashim. (2019). Data Mining Technology for Structural Control Systems: Concept, De-velopment, and Comparison, in: Recent Trends Artif. Neural Networks, IntechOpen Limited, London, 2019. doi:10.5772/intechopen.88651.

[21] M. U. Hanif, Z. Ibrahim, K. Ghaedi, H. Hashim, A. Javanmardi. (2018). Damage assessment of reinforced concrete structures using a model-based nonlinear approach—A comprehensive review, Construction and Building Materials. 192(2018), 846-865.doi:10.1016/j.conbuildmat.2018.10.115.

[22] A. Javanmardi, Z. Ibrahim, K. Ghaedi, N. Bahadur Khan, H. Benisi Ghadim. (2018). Seismic isolation retrofitting solution for an existing steel cable-stayed bridge, PLoS ONE (2018), 13(7).doi.org/10.1371/journal.pone.0200482.

[23] H. H. Ghayeb, H. A. Razak, N. H. R. Sulong, A. N. Hanoon, F. Abutaha, H. A. Ibrahim, M. Gordan, M. F. Alnahhal. (2019). Predicting the Mechanical Properties of Concrete Using Intelligent Techniques to Reduce CO 2 Emissions, Mater. Construcción. 69(2019), 1-20.

[24] S. A. Ravanfar. (2017). Vibration-Based Structural Damage Detection and System Identification Using Wavelet Multiresolution Analysis, Doctor of Philosophy Thesis, University of Malaya, 2017.

[25] M. Gordan, H. A. Razak, Z. Ismail, K. Ghaedi. (2018). Data mining based damage identification using imperialist competitive algorithm and artificial neural network, Lat. Am. J. Solids Struct., 15(2018), 1-14. doi:http://dx.doi.org/10.1590/1679-78254546.

[26] M. Gordan, Z. B. Ismail, H. A. Razak, K. Ghaedi. (2019). Optimization-Based Evolutionary Data Mining Techniques for Structural Health Monitoring, J. Civ. Eng. Constr., 9(2019), 14-23.

[27] W. H. Maes, K. Steppe. (2019). Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture, Trends Plant Sci., 24(2019), 152-164. doi:10.1016/j.tplants.2018.11.007.

[28] D. Kinaneva, G. Hristov, J. Raychev, P. Zahariev. (2019). Early forest fire detection using drones and artificial intelligence, in: 2nd Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2019-Proc., Croatian Society MIPRO, 2019: pp. 1060-1065. doi:10.23919/MIPRO.2019.8756696.

[29] P. Darby, V. Gopu. (2018). Bridge Inspecting with Unmanned Aerial Vehicles R & D, 2018.

[30] M. Wazid, A. K. Das, N. Kumar, A. V. Vasilakos, J. J. P. C. Rodrigues. (2019). Design and analysis of secure lightweight re-mote user authentication and key agreement scheme in internet of drones deployment, IEEE Internet Things J., 6(2019), 3572-3584. doi:10.1109/JIOT.2018.2888821.

[31] K. Ghaedi, Z. Ibrahim, A. Javanmardi, R. Rupakhety. (2018). Experimental study of a new bar damper device for vibration control of structures subjected to earthquake loads. Journal of Earthquake Engineering, (2018), 1-19. doi:10.1080/13632469.2018.1515796.

[32] Y. Dong, Q. Li, A. Dou, X. Wang. (2011). Journal of Asian Earth Sciences Extracting damages caused by the 2008 Ms 8. 0 Wenchuan earthquake from SAR remote sensing data, 40(2011), 907-914. doi:10.1016/j.jseaes.2010.07.009.

[33] Y. Pang, B. K. Chen, W. Liu, S. Fung, S. N. Lingamanaik. (2020). Development of a non-contact and non-destructive laser speckle imaging system for remote sensing of anisotropic deformation around fastener holes. NDT E Int. 111(2020), 102219. doi:10.1016/j.ndteint.2020.102219.

[34] M. Talebkhah, A. Sali, M. Marjani, M. Gordan, S. J. Hashim, F. Z. Rokhani. (2020). Edge computing: Architecture, Applications and Future Perspectives, in: IICAIET2020 (IEEE Int. Conf. Artif. Intell. Eng. Technol., Sabah, Malaysia, 2020).

How to cite this paper

A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring

How to cite this paper: Meisam Gordan, Zubaidah Ismail, Khaled Ghaedi, Zainah Ibrahim, Huzaifa Hashim, Haider Hamad Ghayeb, Marieh Talebkhah. (2021). A Brief Overview and Future Perspective of Unmanned Aerial Systems for In-Service Structural Health Monitoring. Engineering Advances1(1), 9-15.

DOI: http://dx.doi.org/10.26855/ea.2021.06.002