Published Papers

Theme Issue Papers

Empowering automated surface defect inspection with IoT and deep learning techniques: an application in Trunk Road T2 and Cha Kwo Ling Tunnel, Hong Kong

Zi Yang, Hiu Ngai Chan, Albert W Y Chan and Tommy C W Wong
Pages: 1-8Published: 25 Mar 2025
DOI: 10.33430/V31N3THIE-2024-0007
Cite thisHide

Yang Z, Chan HN, Chan WY and Wong CW, Empowering automated surface defect inspection with IoT and deep learning techniques: an application in Trunk Road T2 and Cha Kwo Ling Tunnel, Hong Kong, HKIE Transactions, Vol. 31, No. 3 (Theme Issue), Article THIE-2024-0007, 2025, 10.33430/V31N3THIE-2024-0007

 Copy

Abstract:

The continuous collection and analysis of data are instrumental in the inspection of tunnel structures. This procedure enables tunnel inspectorates to systematically monitor structural integrity, thereby ensuring the safety and stability of tunnels. Thanks to recent breakthroughs in technologies such as Internet of Things (IoT), deep learning, and robotics, it is now feasible to automate the inspection of tunnel structures. Hong Kong Productivity Council (HKPC), Civil Engineering and Development Department (CEDD), and Hyder-Meinhardt Joint Venture (HMJV) have collaboratively developed an AI-based tunnel structure inspection system that integrates air-ground cooperation. This system leverages cutting-edge techniques, including IoT sensors, Unmanned Ground Vehicle (UGV), and Unmanned Aerial Vehicle (UAV), to facilitate the real-time image data capturing of tunnel structures. Furthermore, it is equipped with the capability to automatically identify defects such as concrete cracks, concrete spalling, and water leakages. Consequently, this advancement not only heightens the efficiency of
the tunnel inspection process by diminishing the inspection time and increasing cost savings, but also enhances the site safety and optimises the construction logistics.

Keywords:

Internet of Things; deep learning; robotics; automate the inspection of tunnel structures; air-ground cooperation

Reference List:

  1. Akinosho TD, Oyedele LO, Bilal M, Ajayi AO, Delgado MD, Akinade OO and Ahmed AA (2020). Deep learning in the construction industry: A review of present status and future innovations. Journal of Building Engineering, 32, pp. 101827.
  2. Civil Engineering and Development Department (2024). Major Projects - Trunk Road T2 and Cha Kwo Ling Tunnel . [online]. Available at: <https://www.cedd.gov.hk/eng/our-projects/major-projects/indexid-31.html>. [Accessed on 9th Feb 2025].
  3. Devalal S and Karthikeyan A (2018). LoRa technology - an overview. In: 2018 second international conference on electronics, communication and aerospace technology (ICECA), Coimbatore, India: IEEE, pp. 284-290.
  4. Ghosh A, Edwards DJ and Hosseini MR (2021). Patterns and trends in Internet of Things (IoT) research: future applications in the construction industry. Engineering, Construction and Architectural Management, 28(2), pp. 457-481.
  5. Kamolov S (2024). Machine learning methods in civil engineering: a systematic review. Annals of Mathematics and Computer Science, 21, pp. 181-191.
  6. Mijwil MM, Hiran KK, Doshi R and Unogwu OJ (2023). Advancing Construction with IoT and RFID Technology in Civil Engineering: A Technology Review. Al-Salam Journal for Engineering and Technology, 2(02), pp. 54-62.
  7. Mishra M, Lourenço PB and Ramana GV (2022). Structural health monitoring of civil engineering structures by using the internet of things: A review. Journal of Building Engineering, 48, pp. 103954.
  8. Redmon J and Farhadi A (2018). YOLOv3: An incremental improvement. In: Computer Vision and Pattern Recognition. Berlin/Heiderlberg, Germany: Springer, pp. 1-6.
  9. Shan F, He X, Armaghani DJ, Zhang P and Sheng D (2022). Success and challenges in predicting TBM penetration rate using recurrent neural networks. Tunnelling and underground space technology, 130, pp. 104728.
  10. Wang CY, Bochkovskiy A and Liao HYM (2023). YOLOv7: Trainable bag-of-freebies sets new state-ofthe-art for real-time object detectors. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Vancouver, BC, Canada: IEEE, pp. 7464-7475.
  11. Wang J, Xiao G, Zhu H, Li W, Cui J, Wan Y, Wang Z and Sui Q (2023). N-LoLiGan: Unsupervised lowlight enhancement GAN with an N-Net for low-light tunnel images. Digital Signal Processing, 143, pp. 104259.
  12. Yang Z, Lee WC, Chan HN and Ge M (2022). A Realtime Tunnel Surface Inspection System using Edge-AI on Drone. In: 2022 IEEE International Conference on Mechatronics and Automation (ICMA). Guilin: IEEE, pp. 749-754.
  13. Yu H, Tao J, Qin C, Liu M, Xiao D, Sun H and Liu C (2022). A novel constrained dense convolutional autoencoder and DNN-based semi-supervised method for shield machine tunnel geological formation recognition. Mechanical Systems and Signal Processing, 165, pp. 108353.
  14. Zhu G, Zhao H, Liu Z and Shi C (2020). Design and Implementation of Tunnel Environment Monitoring System Based on LoRa. In: IoT as a Service: 5th EAI International Conference, IoTaaS 2019. Xi’an, China: Springer International Publishing, pp. 621-638.
>> more<< less