ISSN 2096-4498

   CN 44-1745/U

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Tunnel Construction >

2024 , Vol. 44 >Issue 9: 1752 - 1761

DOI: https://doi.org/10.3973/j.issn.2096-4498.2024.09.004

Knowledge-Data-Driven Analysis Method for Safety Status of Immersed Tube Tunnel Joints: A Case Study of Subsea Immersed Tube Tunnel of Hong Kong-Zhuhai-Macao Bridge

  • DING Hao ,
  • ZHOU Chenyi ,
  • GUO Hongyan ,
  • ZHOU Yunteng
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  • (1. China Merchants Chongqing Communications Technology Research & Design Institute Co., Ltd., Chongqing 400067, China; 2. National Engineering Research Center for Road Tunnel, Chongqing 400067, China)

Online published: 2024-10-12

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Abstract

The safety status of immersed tube tunnel(ITT) joints is continuously evolving, making direct deformation sensing challenging. Therefore, a case study is conducted on the subsea ITT of the Hong Kong-Zhuhai-Macao bridge. By examining existing monitoring data, the deformation patterns of ITT joints are summarized to reveal the strong correlation between the expansion/contraction deformation of joints and structural temperature as well as the significant impact of tidal variations on the shear deformation at joints. Furthermore, a knowledge-data-driven rapid inference method is proposed for joint deformation. This method involves constructing a refined finiteelement model of the tunnel, analyzing its mechanical behavior under various typical deformation modes, and generating a dataset of deformation service behaviors. By employing a back-propagation neural network, a comprehensive cross-sectional deformation inference model is developed for tunnel joints based on the simulated joint service behaviors. This enables the rapid reconstruction of joint deformations using limited actual measurement data. The proposed analytical method has been successfully applied to the on-site maintenance of the ITT of the Hong Kong-Zhuhai-Macao bridge. The case study demonstrates that during Typhoon Saola in 2023, the overall impact of the typhoon on the ITT joint system was relatively small.

Key words： knowledge-data-driven; immersed tube tunnel; pipe joint; safety status; simulation analysis; neural network

Cite this article

DING Hao , ZHOU Chenyi , GUO Hongyan , ZHOU Yunteng .

Knowledge-Data-Driven Analysis Method for Safety Status of Immersed Tube Tunnel Joints: A Case Study of Subsea Immersed Tube Tunnel of Hong Kong-Zhuhai-Macao Bridge

[J]. Tunnel Construction, 2024 , 44(9) : 1752 -1761 . DOI: 10.3973/j.issn.2096-4498.2024.09.004

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