• Title/Summary/Keyword: soil underwater tunnel

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The Continuously Underwater Tunnelling Methods by Incremental launching Methods (연속압출공법(ILM)을 이용한 수저(水底)터널공법에 관한 연구)

  • Jung, Byung-Ryul;Ryu, Dong-Hun;Kim, Joon-Mo
    • Proceedings of the KSR Conference
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    • 2009.05a
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    • pp.28-41
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    • 2009
  • We know the several construction methods for underwater tunnel, but properly submerged concrete box type tunnel was mostly good structure stability and mostly shot length of tunnels. Submerged box type tunnel was buildup the unit segments in dry dock or ship yard by 10 to 20meters. The submerged box was composed with segments was join each together. It was installing the gate and waterproofing the coupling the front hull of a box. The complete submerged box rise up to the surface water, tow in the submerged box by tugboat, going to the destination of tunnel construction site. Beforehand dredge up soil at the bottom of a underwater, sinking the submerged box, connection together complete submerged box in underwater. The research and development ILM tunneling method is receiving careful study. Biggest weakness in submerged concrete box type tunnel was pressure waterproofing, box to box connecting, complete submerged boxes navigation and installation, after operation the submerged tunnel and management concrete box structure. It was positive evidence in submerged concrete box type tunnel. We make a practical application of the principle "the ILM tunneling method in underwater construction methods."

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Face stability analysis of large-diameter underwater shield tunnel in soft-hard uneven strata under fluid-solid coupling

  • Shanglong Zhang;Xuansheng Cheng;Xinhai Zhou;Yue Sun
    • Geomechanics and Engineering
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    • v.32 no.2
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    • pp.145-157
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    • 2023
  • This paper aims at investigating the face stability of large-diameter underwater shield tunnels considering seepage in soft-hard uneven strata. Using the kinematic approach of limit upper-bound analysis, the analytical solution of limit supporting pressure on the tunnel face considering seepage was obtained based on a logarithmic spiral collapsed body in uneven strata. The stability analysis method of the excavation face with different soft- and hard-stratum ratios was explored and validated. Moreover, the effects of water level and burial depth on tunnel face stability were discussed. The results show the effect of seepage on the excavation face stability can be accounted as the seepage force on the excavation face and the seepage force of pore water in instability body. When the thickness ratio of hard soil layer within the excavation face exceeds 1/6D, the interface of the soft and hard soil layer can be placed at tunnel axis during stability analysis. The reliability of the analytical solution of the limit supporting pressure is validated by numerical method and literature methods. The increase of water level causes the instability of upper soft soil layer firstly due to the higher seepage force. With the rise of burial depth, the horizontal displacement of the upper soft soil decreases and the limit supporting pressure changes little because of soil arching effect.

DATCN: Deep Attention fused Temporal Convolution Network for the prediction of monitoring indicators in the tunnel

  • Bowen, Du;Zhixin, Zhang;Junchen, Ye;Xuyan, Tan;Wentao, Li;Weizhong, Chen
    • Smart Structures and Systems
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    • v.30 no.6
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    • pp.601-612
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    • 2022
  • The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.