Geomechanics and Engineering

  • 제9권1호
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  • Pages.57-81
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  • 2015
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Dynamic risk assessment of water inrush in tunnelling and software development

  • Li, L.P. (Geotechnical & Structural Engineering Research Center, Shandong University) ;
  • Lei, T. (Geotechnical & Structural Engineering Research Center, Shandong University) ;
  • Li, S.C. (Geotechnical & Structural Engineering Research Center, Shandong University) ;
  • Xu, Z.H. (Geotechnical & Structural Engineering Research Center, Shandong University) ;
  • Xue, Y.G. (Geotechnical & Structural Engineering Research Center, Shandong University) ;
  • Shi, S.S. (Geotechnical & Structural Engineering Research Center, Shandong University)
  • 투고 : 2014.11.14
  • 심사 : 2015.03.19
  • 발행 : 2015.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Water inrush and mud outburst always restricts the tunnel constructions in mountain area, which becomes a major geological barrier against the development of underground engineering. In view of the complex disaster-causing mechanism and difficult quantitative predictions of water inrush and mud outburst, several theoretical methods are adopted to realize dynamic assessment of water inrush in the progressive process of tunnel construction. Concerning both the geological condition and construction situation, eleven risk factors are quantitatively described and an assessment system is developed to evaluate the water inrush risk. In the static assessment, the weights of eight risk factors about the geological condition are determined using Analytic Hierarchy Process (AHP). Each factor is scored by experts and the synthesis scores are weighted. The risk level is ultimately determined based on the scoring outcome which is derived from the sum of products of weights and comprehensive scores. In the secondary assessment, the eight risk factors in static assessment and three factors about construction situation are quantitatively analyzed using fuzzy evaluation method. Subordinate levels and weight of factors are prepared and then used to calculate the comprehensive subordinate degree and risk level. In the dynamic assessment, the classical field of the eleven risk factors is normalized by using the extension evaluation method. From the input of the matter-element, weights of risk factors are determined and correlation analysis is carried out to determine the risk level. This system has been applied to the dynamic assessment of water inrush during construction of the Yuanliangshan tunnel of Yuhuai Railway. The assessment results are consistent with the actual excavation, which verifies the rationality and feasibility of the software. The developed system is believed capable to be back-up and applied for risk assessment of water inrush in the underground engineering construction.

키워드

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피인용 문헌

  1. A multi-factor comprehensive risk assessment method of karst tunnels and its engineering application 2019, https://doi.org/10.1007/s10064-017-1214-1
  2. Numerical analysis of water flow characteristics after inrushing from the tunnel floor in process of karst tunnel excavation vol.10, pp.4, 2016, https://doi.org/10.12989/gae.2016.10.4.471
  3. Risk assessment of water inrush in karst tunnels based on a modified grey evaluation model: Sample as Shangjiawan Tunnel vol.11, pp.4, 2016, https://doi.org/10.12989/gae.2016.11.4.493
  4. Analysis of Pipe-Roof in Tunnel Exiting Portal by the Foundation Elastic Model vol.2017, 2017, https://doi.org/10.1155/2017/9387628
  5. Numerical Simulation on the Seepage Properties of Soil-Rock Mixture vol.2018, pp.1687-8442, 2018, https://doi.org/10.1155/2018/1859319
  6. Fuzzy risk assessment of a deeply buried tunnel under incomplete information vol.5, pp.10, 2018, https://doi.org/10.1098/rsos.180305
  7. A New Advance Classification Method for Surrounding Rock in Tunnels Based on the Set-Pair Analysis and Tunnel Seismic Prediction System vol.36, pp.4, 2018, https://doi.org/10.1007/s10706-018-0471-5
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  9. Combination of engineering geological data and numerical modeling results to classify the tunnel route based on the groundwater seepage vol.13, pp.4, 2017, https://doi.org/10.12989/gae.2017.13.4.671
  10. Study on Early Warning Method for Water Inrush in Tunnel Based on Fine Risk Evaluation and Hierarchical Advance Forecast vol.9, pp.9, 2019, https://doi.org/10.3390/geosciences9090392
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  14. Modelling the coupled fracture propagation and fluid flow in jointed rock mass using FRACOD vol.22, pp.6, 2020, https://doi.org/10.12989/gae.2020.22.6.529
  15. Development and application of a floor failure depth prediction system based on the WEKA platform vol.23, pp.1, 2015, https://doi.org/10.12989/gae.2020.23.1.051