ISSN 2096-4498

   CN 44-1745/U

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Tunnel Construction ›› 2023, Vol. 43 ›› Issue (S2): 215-222.DOI: 10.3973/j.issn.2096-4498.2023.S2.024

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Machine VisionBased Monitoring and Safety Assessment of Prefabricated Underground Stations

HONG Chengyu1, 2, 3, RAO Wei1, 2, 3, *, WU Chenggang4, DAI Ji5, JIA Ke5   

  1. (1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China; 2. Key Laboratory of Green, Efficient and Intelligent Construction of Shenzhen Metro Underground Station, Shenzhen 518060, Guangdong, China; 3. Key Laboratory of Coastal Urban Resilience Infrastructure, Ministry of Education, Shenzhen 518060, Guangdong, China; 4. Beijing Urban Construction Design and Development Group Co., Ltd., Beijing 100037, China; 5. Shenzhen Metro Group Co., Ltd., Shenzhen, Guangdong 518038, China)

  • Online:2023-12-30 Published:2024-03-27

Abstract: To explore the applicability of the machine vision-based monitoring system in underground engineering and the settlement variation patterns of underground prefabricated stations, field calibration experiments are conducted and the settlement during the construction and postconstruction phases of the underground prefabricated stations is monitored. The field monitoring data is then compared with the finite element calculation results to establish a real-time multi-scale safety level evaluation method. The results show that: (1) When the distance between the camera and the target is less than 60 m, the average measurement error rate is within 3%. This indicates that the system has a high accuracy in monitoring the structural displacements over short distances. (2) The measured data reveal the settlement variations in the stations due to factors such as backfill construction, entrance excavation, and rainfall. (3) During the backfilling phase, the settlement of the prefabricated underground stations is within 7.0 mm, indicating an overall safe condition of the station structure. The measured data obtained from the machine visionbased monitoring system coincide with the settlement variation patterns revealed by the finite element calculation. This demonstrates that the machine visionbased monitoring system can accurately capture and measure the deformations during the construction of underground stations, and it has a good applicability in underground engineering.

Key words: prefabricated underground station, machine vision, settlement monitoring, safety assessment