The Paton Welding Journal, 2025, №07

2025 №07 (04)

DOI of Article 10.37434/tpwj2025.07.05

2025 №07 (06)

---

The Paton Welding Journal, 2025, #7, 28-36 pages

Enhancing large-scale structure diagnostics through uav-based data and neural network analysis

L.M. Lobanov, I.L. Shkurat, D.I. Stelmakh, O.P. Shutkevych, V.V. Savitsky

E.O. Paton Electric Welding Institute of the NASU. 11 Kazymyr Malevych Str., 03150, Kyiv, Ukraine. E-mail: innashkurat2909@gmail.com

Abstract
The article presents an approach to remote diagnostics of damage to large-scale engineering structures using unmanned aerial vehicles (UAVs) and convolutional neural networks. The study was conducted to automate the process of detecting structural defects in the Kyiv TV tower. The research methodology involved the collection and preprocessing of 14187 images and the development of a modified architecture of the U-Net neural network for damage segmentation. An experimental study of different architectural settings of the model demonstrated the effectiveness of the proposed modifications, which allowed reducing the error of defect detection by 3‒5 % compared to the baseline models. It was found that the optimal number of training iterations is 15‒20 epochs. The developed model demonstrated the ability to detect damage that may be missed by the operator, which confirms the potential of automated diagnostic systems based on artificial intelligence. The study provides new prospects for improving the efficiency of monitoring infrastructure facilities, especially under conditions of limited access or increased risks to personnel.
Keywords: remote diagnostics, defects, artificial intelligence, neural networks, image segmentation, UAVs

Received: Received: 06.03.2024
Received in revised form: 08.05.2024
Accepted: 26.06.2025

References

1. Balayssac, J.-P., Garnier, V. et al. (2018) Non-Destructive testing and evaluation of civil engineering structures. STE Press Ltd., Elsevier Science. https://doi.org/10.1016/C2016-0-01227-5
2. Reddy, K.A. (2017) Non-destructive testing, evaluation of stainless steel materials. Mater. Today Proc., 4(8), 7302-7312. https://doi.org/10.1016/j.matpr.2017.07.060
3. Deepak, J.R., Raja, V.K.B., Srikanth, D. et al. (2021) Non-destructive testing (NDT) techniques for low carbon steel welded joints: A review and experimental study. Mater. Today Proc., 44(8), 3732-3737. https://doi.org/10.1016/j.matpr.2020.11.578
4. Polimeno, M., Roselli, I., Luprano, V.A.M. et al. (2018) A non-destructive testing methodology for damage assessment of reinforced concrete buildings after seismic events. Eng. Structures, 163, 122-136. https://doi.org/10.1016/j.engstruct.2018.02.053
5. Bahonar, M., Safizadeh, M. (2021) Investigation of real delamination detection in composite structure using air-coupled ultrasonic testing. Composite Structures, 280, 114939. https://doi.org/10.1016/j.compstruct.2021.114939
6. Chen, Y., Kang, Y., Feng, B., Li, Y., Cai, X. (2022) Automatic defect identification in magnetic particle testing using a digital model aided de-noising method. Measurement, 198, 111427. https://doi.org/10.1016/j.measurement.2022.111427
7. Van Steen, C., Pahlavan, P., Wevers, M., Verstrynge, E. (2018) Localisation and characterisation of corrosion damage in reinforced concrete by means of acoustic emission and X-ray computed tomography. Construction and Building Materials, 197, 21-29. https://doi.org/10.1016/j.conbuildmat.2018.11.159
8. Suzuki, T., Ogata, H., Takada, R. et al. (2010) Use of acoustic emission and X-ray computed tomography for damage evaluation of freeze-thawed concrete. Construction and Building Materials, 24, 2347-2352. https://doi.org/10.1016/j.conbuildmat.2010.05.005
9. Pedram, M., Taylor, S., Hamill, G. et al. (2022) Experimental evaluation of heat transition mechanism in concrete with subsurface defects using infrared thermography. Construction and Building Materials, 360, 129531. https://doi.org/10.1016/j.conbuildmat.2022.129531
10. Shrestha, P., Avci, O., Rifai, S. et al. (2025) A review of infrared thermography applications for civil infrastructure. Structural Durability & Health Monitoring, 19(2), 193-231. https://doi.org/10.32604/sdhm.2024.049530
11. Lobanov, L.M., Stelmakh, D., Shkurat, I. et al. (2025) Determination of a TV tower verticality using UAV s, RT K and photogrammetry. In: Proc. of VIIth Inter. Conf. on Welding and Related Technologies, Yaremche, Ukraine, 7-10 October 2024, 149-153. https://doi.org/10.1201/9781003518518-30
12. Lobanov, L., Stelmakh, D., Savitsky, V. et al. (2024) Damage detection and analysis using unmanned aerial vehicles (UAVs) and photogrammetry method. Procedia Structural Integrity, 59, 43-49. https://doi.org/10.1016/j.prostr.2024.04.008
13. Lobanov, L.M., Stelmakh, D.I., Savitsky, V.V. et al. (2023) Remote assessment of damage to Kyiv TV tower based on the application of aerial photography and photogrammetry method. Tekh. Diagnost. ta Neruiniv. Kontrol, 3, 16-20 [in Ukrainian]. https://doi.org/10.37434/tdnk2023.03.03
14. Onososen, A., Musonda, I., Onatayo, D. et al. (2023) Impediments to construction site digitalization using unmanned aerial vehicles (UAVs). Drones, 7(1), 45. https://doi.org/10.3390/drones7010045
15. Albeaino, G., Gheisari, M., Franz, B.W. (2019) A systematic review of unmanned aerial vehicle application areas and technologies in the AEC domain. J. of Information Technology in Construction, 24, 381-405. DOI: https://doi.org/www.itcon.org/2019/20
16. Ham, Y., Han, K.K., Lin, J.J., Golparvar-Fard, M. (2016) Visual monitoring of civil infrastructure systems via camera- equipped unmanned aerial vehicles (UAVs): A review of related works. Visualization in Eng., 4, 1. https://doi.org/10.1186/s40327-015-0029-z
17. Pant, S, Nooralishahi, P., Avdelidis, N.P. et al. (2021) Evaluation and selection of video stabilization techniques for uavbased active infrared thermography application. Sensors, 21, 1604. https://doi.org/10.3390/s21051604
18. Ciampa, E., De Vito, L., Rosaria Pecce, M. (2019) Practical issues on the use of drones for construction inspections. J. of Physics: Conf. Series, 1249, 012016. https://doi.org/10.1088/1742-6596/1249/1/012016
19. Duque, L., Seo, J., Wacker, J. (2018) Synthesis of unmanned aerial vehicle applications for infrastructures. J. Perform. Constr. Facil., 32(4). https://doi.org/10.1061/(ASCE)CF.1943-5509.0001185
20. Rakha, T., Gorodetsky, A. (2018) A review of unmanned aerial system (UAS) applications in the built environment: Towards automated building inspection procedures using drones Aut. in Constr., 93, 252-264. https://doi.org/10.1016/j.autcon.2018.05.002
21. Wu, W., Qurishee, M.A., Owino, J. et al. (2018) Coupling deep learning and UAV for infrastructure condition assessment automation. In: Proc. of IEEE Inter. Smart Cities Conf., ISC2, 2018 Sept. 16-19, Kansas City, MO, USA. https://doi.org/10.1109/ISC2.2018.8656971
22. Gu, J., Wang, Z., Kuen, J. et al. (2016) Recent advances in convolutional neural networks. Pattern Recognition, 77, 354-377. https://doi.org/10.1016/j.patcog.2017.10.013
23. Gulgec, N.S., Takáč, M., Pakzad, S.N. (2017) Structural damage detection using convolutional neural networks. In: Proc. of Conf. on Society for Experimental Mechanics Series, 331-337. https://doi.org/10.1007/978-3-319-54858-6_33
24. Krizhevsky, A., Sutskever, I., Hinton, G. (2012) Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25(2), 1097-1105. https://doi.org/10.1145/3065386
25. Lee, S.Y., Tama, B.A., Moon, S.J., Lee, S. (2019) Steel surface defect diagnostics using deep convolutional neural network and class activation map. Applied Sci., 9(24), 5449. https://doi.org/10.3390/app9245449
26. Tabernik, D., Šela, S., Skvarc, J., Skocaj, D. (2020) Segmentation- based deep-learning approach for surface-defect detection. J. of Intelligent Manufacturing, 31(3), 759-776. https://doi.org/10.1007/s10845-019-01476-x
27. Prappacher, N., Bullmann, M., Bohn, G. et al. (2020) Defect detection on rolling element surface scans using neural image segmentation. Applied Sci., 10(9), 3290. https://doi.org/10.3390/app10093290
28. Li, J., Su, Z., Geng, J., Yin, Y. (2018) Real-time detection of steel strip surface defects based on improved YOLO detection network. IFAC-PapersOnLine, 51(21), 76-81. https://doi.org/10.1016/j.ifacol.2018.09.412
29. Wei, R., Song, Y., Zhang, Y. (2020) Enhanced faster region convolutional neural networks for steel surface defect detection. ISIJ Inter., 60(3), 539-545. https://doi.org/10.2355/isijinternational.ISIJINT-2019-335
30. Cha, Y.-J., Choi, W., Buyukozturk, O. (2017) Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263
31. Hutchinson, T., Chen, Z. (2006) Improved image analysis for evaluating concrete damage. J. of Computing in Civil Eng., 20(3), 210-216. https://doi.org/10.1061/(ASCE)0887-3801(2006)20:3(210)
32. Dung, C., Sekiya, H., Hirano, S. et al. (2019) A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks. Automation in Construction, 102, 217-229. https://doi.org/10.1016/j.autcon.2019.02.013
33. Shen, H.-K., Chen, P.-H., Chang, L.-M. (2013) Automated steel bridge coating rust defect recognition method based on color and texture feature. Automation in Construction, 31, 338-356. https://doi.org/10.1016/j.autcon.2012.11.003
34. Xu, Y., Bao, Y., Chen, J. et al. (2018) Surface fatigue crack identification in steel box girder of bridges by a deep fusion convolutional neural network based on consumer-grade camera images. Structural Health Monitoring, 18(3), 653-674. https://doi.org/10.1177/1475921718764873
35. Prasanna, P., Dana, K.J., Gucunski, N. et al. (2016) Automated crack detection on concrete bridges. IEEE Transact. on Automation Sci. and Eng., 13(2), 591-599. https://doi.org/10.1109/TASE.2014.2354314
36. An, Y.-K., Jang, K.-Y., Kim, B., Cho, S. (2018) Deep learning-based concrete crack detection using hybrid images. In: Proc. of SPIE 10598 on Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 1059812. https://doi.org/10.1117/12.2294959
37. Chow, J.K., Su, Z., Wu, J. et al. (2020) Anomaly detection of defects on concrete structures with the convolutional autoencoder. Advanced Eng. Informatics, 45, 101105. https://doi.org/10.1016/j.aei.2020.101105
38. Miranda, J., Veith, J., Larnierc, S. et al. (2019) Machine learning approaches for defect classification on aircraft fuselage images acquired by an UAV. In: Proc. of Fourteenth Inter. Conf. on Quality Control by Artificial Vision, 1117208. https://doi.org/10.1117/12.2520567
39. Avdelidis, N.P., Tsourdos, A., Lafiosca, P. et al. (2022) Defects recognition algorithm development from visual UAV inspections. Sensors, 22(13), 4682. https://doi.org/10.3390/s22134682
40. Ronneberger, O., Fischer, P., Brox, T. (2015) U-net: convolutional networks for biomedical image segmentation. In: Proc. of the Medical Image Computing and Computer-Assisted Intervention, 234-241. http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net https://doi.org/10.1007/978-3-319-24574-4_28
41. Ren, J., Wang, H. (2023) Calculus and optimization. In: Mathematical Methods in Data Science. Chapter 3. Elsevier, 51-89. https://doi.org/10.1016/B978-0-44-318679-0.00009-0

---

Suggested Citation

L.M. Lobanov, I.L. Shkurat, D.I. Stelmakh, O.P. Shutkevych, V.V. Savitsky (2025) Enhancing large-scale structure diagnostics through uav-based data and neural network analysis. The Paton Welding J., 07, 28-36.

---