Volume 38 Issue 5
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Liu Shiwei, Wang Rong, Zhang Tonghu, Wu Huiguo. Road Automation Monitoring System Based on Deep Learning: an Application Research[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2024, 38(5): 527-532. doi: 10.3969/j.issn.1007-2993.2024.05.004
Citation: Liu Shiwei, Wang Rong, Zhang Tonghu, Wu Huiguo. Road Automation Monitoring System Based on Deep Learning: an Application Research[J]. GEOTECHNICAL ENGINEERING TECHNIQUE, 2024, 38(5): 527-532. doi: 10.3969/j.issn.1007-2993.2024.05.004
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Road Automation Monitoring System Based on Deep Learning: an Application Research

doi: 10.3969/j.issn.1007-2993.2024.05.004
  • 1.

    Air Force Logistics Department Engineering Construction Management Office First Construction Project Department, Nanjing 210018, Jiangsu, China

  • 2.

    First Generation Construction Department Plan Contracting Office of the Air Force Logistics Department Engineering Construction and Management Office, Nanjing 210000, Jiangsu, China

  • 3.

    Guannan County Water Resources Bureau, Lianyungang 222500, Jiangsu, China

  • 4.

    Hohai University, Nanjing 210024, Jiangsu, China

  • Received Date: 2023-06-30
  • Accepted Date: 2023-12-25
  • Rev Recd Date: 2023-10-09
    • Available Online: 2024-10-09
  • Publish Date: 2024-10-09
    Abstract
  • Traditional monitoring methods are inefficient and cannot predict soil deformation in real-time and continuously with accuracy in road construction. A road automation monitoring system integrated with artificial intelligence technology was proposed. The system consists of a real-time Internet of Things (IoT) system and a data processing system. The real-time IoT system includes embedded settlement instruments with dual pressure sensors, a data acquisition system, and a network transmission system. The data processing system utilizes deep learning algorithms to train the measured data to predict soil deformation. The composition and working principles of the monitoring system was introduced. The system was validated through on-site experiments. By comparing the data from the dual pressure sensors in the embedded settlement instruments with the data from settlement plates, the results show that the error between the two is only 6.7%. This indicated that the automated monitoring instrument has high precision in road construction monitoring. On-site experimental results also prove that the deformation prediction method based on deep learning algorithms can accurately predict soil deformation during the road construction process, with a maximum prediction error of only 5.3%.

     

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