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Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings

Author

Listed:
  • Peng Liu

    (Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China)

  • Zude Ding

    (Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China)

  • Wanping Zhang

    (Faculty of Civil Engineering and Mechanics, Kunming University of Science and Technology, Kunming 650500, China)

  • Zhihua Ren

    (Yunnan Institute of Highway Science and Technology, Kunming 650051, China)

  • Xuxiang Yang

    (Yunnan Institute of Highway Science and Technology, Kunming 650051, China)

Abstract

The geological radar method has found widespread use in evaluating the quality of tunnel lining. However, relying on manual experience to interpret geological radar data may cause identification errors and reduce efficiency when dealing with large numbers of data. This paper proposes a method for identifying internal quality defects in tunnel lining using deep learning and transfer learning techniques. An experimental physical model for detecting the quality of tunnel lining radars was developed to identify the typical radar image features of internal quality defects. Using the geological radar method, a large volume of lining quality detection radar image data was collected, in conjunction with several examples of tunnel engineering. The preprocessing of geological radar data was performed, including gain and normalization, and a set of data samples exhibiting typical lining quality defects was prepared with 6236 detection targets in 4246 images. The intelligent recognition models for tunnel lining quality defects were established using a combination of geological radar image datasets and transfer learning concepts, based on the SSD and YOLOv4 models. The accuracy of the SSD algorithm for cavity defect recognition is 86.58%, with the YOLOv4 algorithm achieving slightly lower accuracy at 86.05%. For steel bar missing recognition, the SSD algorithm has an accuracy of 97.7%, compared to 98.18% accuracy for the YOLOv4 algorithm. This indicates that deep learning-based models are practical for tunnel quality defect detection.

Suggested Citation

  • Peng Liu & Zude Ding & Wanping Zhang & Zhihua Ren & Xuxiang Yang, 2023. "Using Ground-Penetrating Radar and Deep Learning to Rapidly Detect Voids and Rebar Defects in Linings," Sustainability, MDPI, vol. 15(15), pages 1-16, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11855-:d:1208609
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    References listed on IDEAS

    as
    1. Feng Lu & Yi Wang & Junfu Fu & Yanxing Yang & Wenge Qiu & Yawen Jing & Manlin Jiang & Huayun Li, 2023. "Safety Evaluation of Plain Concrete Lining Considering Deterioration and Aerodynamic Effects," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
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    Cited by:

    1. Mingzhou Bai & Hongyu Liu & Zhuangzhuang Cui & Dayong Wang & Juntao Han & Chunrong Gao & Shuanglai Li, 2023. "A Study on the Influence of Steel Structures in Concrete Subgrades on the Detection of Subgrade Distresses by Ground-Penetrating Radar," Sustainability, MDPI, vol. 15(24), pages 1-15, December.

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