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UAV Image-Based Defect Detection for Ancient Bridge Maintenance

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Advances in Information Technology in Civil and Building Engineering (ICCCBE 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 357))

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Abstract

Ancient bridges lack adequate maintenance strategies and public attention compared to modern bridges. The current bridge maintenance standards are tailored for modern bridges and cannot be directly applied to ancient bridge maintenance because of differences in structure designs and construction materials. Besides, due to the urban development and the evolution of traffic, the frequency of using the ancient bridges has tapered off; people gradually elided the maintenance of ancient bridges. Nevertheless, some ancient bridges still serve as integral hubs in the transportation network and require more inspection due to their common features of aging structures and complex damage history. Previous studies have mainly applied sensor-based analysis for structural deformation problems in ancient bridge health monitoring. The mainstream inspection technologies include sonic transmission, radiography, infrared thermography, and ground-penetrating radar (GPR). However, these methods can only partially depict the interior condition of the bridge, and are time-consuming and complicated to implement in practice; their feasibility on ancient bridge maintenance is debatable. This paper proposes an image-based detection method to provide an effective solution for the maintenance of ancient bridges using Deep Neural Networks (DNNs). A masonry arch bridge in Hong Kong, built in the 1880s, was investigated. Unmanned Aerial Vehicles (UAVs) were deployed to collect the bridge surface information, and a 3D model generated with Structure from Motion (SfM) was preserved for further bridge health monitoring. In addition, an assessment criterion was purposed to evaluate the ancient bridge health condition, which is beneficial for the decision-making on ancient bridge maintenance.

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References

  1. Bridge Inspections (2022) A Complete Guide, Flyability. Drones for indoor inspection and confined space. https://www.flyability.com/bridge-inspections/. Accessed 18 June 2022

  2. McClain & Co., Inc. Underbridge and Aerial Access Equipment Rentals. https://mcclain1.com/we-all-know-the-venerable-snooper-truck-but-what-is-a-snooper-truck/. Accessed 20 June 2022

  3. Asm.aviyaan.com. Step 6: Types of Bridge Inspection. http://asm.aviyaan.com/bridge_toolkit/step_6_types_of_bridge_inspection.html. Accessed 20 June 2022

  4. von Kotzebue A (1806) Travels through Italy, in the years 1804 and 1805, vol. 4. Richard Phillips... By. T. Gillet... and to be had be had of all booksellers

    Google Scholar 

  5. Dajun D (1994) Ancient and modern chinese bridges. Struct Eng Int 4(1):41–43

    Article  Google Scholar 

  6. Alvares JS, Costa DB (2019) Construction progress monitoring using unmanned aerial system and 4D BIM. In: Proceedings of the 27th annual conference of the international. Grupo para Construcao Enxuta (IGLC), Dublin, Irlanda, pp 1445–1456

    Google Scholar 

  7. Silveira B, Melo R, Costa DB (2020) Using UAS for roofs structure inspections at post occupational residential buildings. In: International conference on computing in civil and building engineering. Springer, Cham, pp 1055–1068

    Google Scholar 

  8. Floreano D, Wood RJ (2015) Science, technology and the future of small autonomous drones. Nature 521(7553):460–466

    Google Scholar 

  9. Ruiz RDB, Lordsleem Junior AC, Fernandes BJT, Oliveira SC (2020) Unmanned aerial vehicles and digital image processing with deep learning for the detection of pathological manifestations on facades. In: International conference on computing in civil and building engineering. Springer, Cham, pp 1099–1112

    Google Scholar 

  10. Kumar K, Rasheed AM, Krishna Kumar R, Giridharan M et al (2013) Dhaksha, the unmanned aircraft system in its new avatar-automated aerial inspection of India’stallest tower. ISPRS-Int Arch Photogram Remote Sens Spatial Inf Sci 40:241–246

    Google Scholar 

  11. Ellenberg A, Branco L, Krick A, Bartoli I, Kontsos A (2015) Use of unmanned aerial vehicle for quantitative infrastructure evaluation. J Infrastruct Syst 21(3):04014054

    Article  Google Scholar 

  12. Costa DB, De Melo RR, Alvares JS, Bello AA (2016) Evaluating the performance of unmanned aerial vehicles for safety inspection. Boston, MA, USA, pp 23–32

    Google Scholar 

  13. Hull B, John V (1988) Non-destructive testing

    Google Scholar 

  14. Gaussorgues G, Chomet S (1993) Infrared thermography, vol 5. Springer, Heidelberg (1993)

    Google Scholar 

  15. Rahiman MHF, Rahim RA, Rahiman MHF, Tajjudin M (2006) Ultrasonic transmission-mode tomography imaging for liquid/gas two-phase flow. IEEE Sens J 6(6):1706–1715

    Article  Google Scholar 

  16. Hagedoorn JG (1954) A process of seismic reflection interpretation. Geophys Prospect 2(2):85–127

    Article  Google Scholar 

  17. Baker GS, Jordan TE, Pardy J et al (2007) An introduction to ground penetrating radar (GPR). Special Papers-Geol Soc Am 432:1

    Google Scholar 

  18. Cheney M, Isaacson D, Newell JC (1999) Electrical impedance tomography. SIAM Rev 41(1):85–101

    Article  MathSciNet  MATH  Google Scholar 

  19. Parks ET, Williamson GF (2002) Digital radiography: an overview. J Contemp Dent Pract 3(4):23–39

    Article  Google Scholar 

  20. Pfeifer N, Briese C (2007) Laser scanning–principles and applications. In: GeoSiberia 2007-international exhibition and scientific congress. European Association of Geoscientists & Engineers, pp cp–59

    Google Scholar 

  21. PyTorch implementation of the U-Net for image semantic segmentation with high quality images. https://github.com/milesial/Pytorch-UNet. Accessed 29 June 2022

  22. Ullman S (1979) The interpretation of structure from motion. Proc R Soc London Ser B Biol Sci 203(1153):405–426

    Google Scholar 

  23. Langley RB (1998) RTK GPS. GPS World 9(9):70–76

    Google Scholar 

  24. Zhang L, Yang F, Zhang YD, Zhu YJ (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 3708–3712

    Google Scholar 

  25. Fan R et al (2019) Road crack detection using deep convolutional neural network and adaptive thresholding. In: 2019 IEEE intelligent vehicles symposium (IV). IEEE, pp 474–479

    Google Scholar 

  26. Amhaz R, Chambon S, Idier J, Baltazart V (2016) Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection. IEEE Trans Intell Transp Syst 17(10):2718–2729

    Article  Google Scholar 

  27. Zou Q, Cao Y, Li Q, Mao Q, Wang S (2012) Cracktree: automatic crack detection from pavement images. Pattern Recogn Lett 33(3):227–238

    Article  Google Scholar 

  28. Shi Y, Cui L, Qi Z, Meng F, Chen Z (2016) Automatic road crack detection using random structured forests. IEEE Trans Intell Transp Syst 17(12):3434–3445

    Article  Google Scholar 

  29. Eisenbach M et al (2017) How to get pavement distress detection ready for deep learning? A systematic approach. In: 2017 international joint conference on neural networks (IJCNN). IEEE, pp 2039–2047

    Google Scholar 

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Kowloon, Hong Kong SAR, China

    Zhaolun Liang, Hao Wu, Haojia Li, Yanlin Wan & Jack C. P. Cheng

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  1. Zhaolun Liang
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  2. Hao Wu
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  3. Haojia Li
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  4. Yanlin Wan
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  5. Jack C. P. Cheng
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Correspondence to Zhaolun Liang .

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Editors and Affiliations

  1. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Sebastian Skatulla

  2. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Hans Beushausen

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Liang, Z., Wu, H., Li, H., Wan, Y., Cheng, J.C.P. (2024). UAV Image-Based Defect Detection for Ancient Bridge Maintenance. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-35399-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35398-7

  • Online ISBN: 978-3-031-35399-4

  • eBook Packages: EngineeringEngineering (R0)

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