为解决沉管隧道接头安全状态不断变化且难以直接感知的技术难点，以港珠澳大桥海底沉管隧道为工程背景，通过分析既有监测数据，总结管节接头的变形模式，揭示管节接头张合量与结构温度的强相关性，明确潮位变化对接头剪切变形的显著影响。在此基础上提出一种基于知识-数据驱动的沉管隧道接头变形快速推演方法，通过建立沉管隧道精细化有限元模型，开展海量典型变形模式下的沉管隧道结构力学行为分析，构建沉管隧道变形服役行为数据集；
利用BP神经网络，建立基于仿真接头服役行为特征的沉管隧道接头全断面变形推演模型，实现基于有限实测数据的接头全断面变形快速重构。该方法在港珠澳大桥海底沉管隧道的现场管养中得到成功应用。以2023年台风“苏拉”为例，基于台风登陆过程中接头的局部位移实测数据，推演接头剪力键及止水带关键点位处管节接头的变形情况。结果表明，该沉管隧道接头系统整体受台风影响较小。
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.
