• Title/Summary/Keyword: Tunnel collapse hazard management system

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Study on qunatified management for tunnel collapses on construction based on the KICT Tunnel Collapse Hazard index(KTH-Index) (터널 붕괴 위험도 지수(KTH-Index)에 기반한 터널 시공 중 붕괴 위험도 정량적 관리 사례 연구)

  • Kim, Young-Yun;Choi, Yu-Mi;Baek, Yong;Shin, Hyu-Soung;Kim, Bum-Joo
    • Proceedings of the Korean Geotechical Society Conference
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    • 2010.09a
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    • pp.1294-1301
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    • 2010
  • In this study, a case study, where a hazard management for tunnel collapses has been quantitatively undertaken based on the KICT Tunnel Hazard(KTH) index, is presented. From this, it was able to timely inform the field engineers when the more detailed investigation is required for checking if any risky factor is shown on the tunnel face. At the same time, variable additional information such as sensitivities of major influence factors are also provided to field engineers from the methodology given in this study. The additional information would be helpful for better understanding of tunnel hazard level at the current tunnelling stage and following the required actions for more detailed checks of risky factors.

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Quantitative evaluation of collapse hazard levels of tunnel faces by interlinked consideration of face mapping, design and construction data: focused on adaptive weights (막장관찰 및 설계/시공자료가 연계 고려된 터널막장 붕괴 위험도의 정량적 산정: 가변형 가중치 중심으로)

  • Shin, Hyu-Soung;Lee, Seung-Soo;Kim, Kwang-Yeom;Bae, Gyu-Jin
    • Journal of Korean Tunnelling and Underground Space Association
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    • v.15 no.5
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    • pp.505-522
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    • 2013
  • Previously, a new concept of indexing methodology has been proposed for quantitative assessment of tunnel collapse hazard level at each tunnel face with respect to the given geological data, design condition and the corresponding construction activity (Shin et al, 2009a). In this paper, 'linear' model, in which weights of influence factors are invariable, and 'non-linear' model, in which weights of influence factors are variable, are taken into account with some examples. Then, the 'non-linear' model is validated by using 100 tunnel collapse cases. It appears that 'non-linear' model allows us to have adapted weight values of influence factors to characteristics of given tunnel site. In order to make a better understanding and help for an effective use of the system, a series of operating processes of the system are built up. Then, by following the processes, the system is applied to a real-life tunnel project in very weak and varying ground conditions. Through this approach, it would be quite apparent that the tunnel collapse hazard indices are determined by well interlinked consideration of face mapping data as well as design/construction data. The calculated indices seem to be in good agreement with available electric resistivity distribution and design/construction status. In addition, This approach could enhance effective usage of face mapping data and lead timely and well corresponding field reactions to situation of weak tunnel faces.