Journal of Korean Tunnelling and Underground Space Association (한국터널지하공간학회 논문집)

Korean Tunnelling and Underground Space Association (한국터널지하공간학회)
권호 표지
DOI QR코드

DOI QR Code

A study on the risk index for tunnel collapse

터널 붕괴 위험도 지수 연구

  • Jeong-Heum Kim (Dept. of Geotechnical Engineering Research, Korea Institute of Civil Engineering and Building Technology)
  • 김정흠 (한국건설기술연구원 지반연구본부)
  • Received : 2024.06.25
  • Accepted : 2024.08.05
  • Published : 2024.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

As the utilization of underground space increases, preventing collapse accidents during tunnel construction has become a significant challenge. This study aims to quantitatively assess the risk of tunnel collapse during construction by analyzing various influencing factors and proposing a tunnel collapse risk index based on these factors. For the 14 major influencing factors affecting tunnel collapse, weights were calculated using the analytic hierarchy process (AHP) method. Data from 27 collapse cases were collected, and Monte Carlo simulation was used to calculate the grade scores for each influencing factor. These scores were then synthesized to derive the tunnel collapse risk index. The average value of the tunnel collapse risk index was analyzed to be 49.359 points. Future comparisons with section-by-section evaluation results of tunnel collapse risk will allow for the assessment of whether a specific section has a lower or higher collapse risk. This study provides a systematic method for quantitatively evaluating the key factors of tunnel collapse risk, thereby contributing to the prevention of collapse accidents during tunnel construction and the establishment of appropriate countermeasures. Future research is expected to enhance the reliability of the tunnel collapse risk index by incorporating more field data and improving the accuracy of tunnel collapse risk assessment based on this index.

지하 공간 활용이 증가하면서 터널 시공 중 발생하는 붕괴 사고의 예방이 중요한 과제가 되고 있다. 본 연구는 터널 시공 중 붕괴 위험을 정량적으로 평가하기 위해 다양한 영향인자를 분석하고, 이를 바탕으로 터널 붕괴 위험도 지수를 제안하였다. 터널 붕괴에 영향을 미치는 14개의 주요 영향 인자에 대해 AHP (analytic hierarchy process)를 통해 가중치를 산정하였다. 27개의 붕괴 사례 데이터를 수집하여 몬테카를로 시뮬레이션을 활용해 각 영향 인자의 등급 점수를 산정하고, 이를 종합하여 터널 붕괴 위험도 지수를 도출하였다. 터널 붕괴 위험도 지수의 평균값은 49.359점으로 분석되었으며, 향후 터널 붕괴 위험도에 대한 막장별 평가결과와 비교를 통해 해당 막장의 붕괴 위험도가 낮은지 높은지 평가할 수 있다. 본 연구는 터널 붕괴 위험도의 주요 인자들을 정량적으로 평가할 수 있는 체계적인 기법을 제시함으로써, 터널 시공 중 발생할 수 있는 붕괴 사고를 사전에 예방하고, 적절한 대책을 수립하는 데 기여할 수 있다. 향후 연구에서는 더 많은 현장 자료를 통해 터널 붕괴 위험도 지수의 신뢰성을 높이고, 이를 바탕으로 한 터널 붕괴 위험도 평가의 신뢰성을 향상시킬 수 있을 것으로 판단된다.

Keywords

References

  1. Ang, A.H-S., Tang, W.H. (1975), Probability Concepts in Engineering Planning and Design, John Wiley & Sons, New York, pp. 572.
  2. Barton, N., Lien, R., Lunde, J. (1974), "Engineering classification of rock masses for the design of tunnel support", Rock Mechanics, Vol. 6, No. 4, pp. 189-236.
  3. Bieniawski, Z.T. (1989), Engineering Rock Mass Classifications, Wiley, New York, pp. 51-72.
  4. Deere, D.U. (1964), "Technical description of rock cores for engineering purposes", Rock Mechanics and Engineering Geology, Vol. 1, No. 1, pp. 16-22.
  5. He, L., Tang, T., Hu, Q., Cai, Q., Li, Z., Tang, S., Wang, Y. (2021), "Integration of interpretive structural modeling with fuzzy Bayesian network for risk assessment of tunnel collapse", Mathematical Problems in Engineering, Vol. 2021, 7518284.
  6. Huang, R., Liu, B., Sun, J., Song, Y., Yu, M. (2022), "A multistate Bayesian network-based approach for risk analysis of tunnel collapse", Arabian Journal for Science and Engineering, Vol. 47, No. 4, pp. 4893-4911.
  7. KICT (2009), Development of technologies for minimizing and preventing the disaster on tunnel construction (VI), Final Technical Report, KICT 2009-071, Korea Institute of Construction Technology, pp. 867.
  8. Kim, J.H., Kim, C.Y., Lee, S.S., Lee, J.H. (2017), "A study on influence factors for tunnel collapse risk analysis using Delphi method", The Journal of Engineering Geology, Vol. 27, No. 2, pp. 165-172.
  9. Kwon, K., Kang, M., Kim, D., Choi, H. (2022), "New tunnel risk assessment model combining AHP and fuzzy set theory", Available at SSRN: https://ssrn.com/abstract=4156575, pp. 1-17.
  10. Li, L., Ni, B., Zhang, S., Qiang, Y., Zhang, Z., Zhou, L., Liu, G., Cheng, L. (2022), "Tunnel collapse risk assessment based on improved quantitative theory III and EW-AHP coupling weight", Scientific Reports, Vol. 12, No. 1, 16054.
  11. Liu, S., Xia, C., Ye, J. (2023), "Risk assessment method for large-section tunnel using neutrosophic numbers similarity measure based on arcsine function", Journal of Intelligent & Fuzzy Systems, Vol. 45, No. 6, pp. 9335-9345.
  12. Meng, G., Li, H., Wu, B., Liu, G., Ye, H., Zuo, Y. (2023), Prediction of the tunnel collapse probability using SVR-based Monte Carlo simulation: a case study, Sustainability, Vol. 15, No. 9, 7098.
  13. MLTMA (2012), Construction safety management guidelines, Ministry of Land, Transport and Maritime Affairs Notice, No. 2012-935.
  14. Saaty, T.L. (1982), "The analytic hierarchy process: a new approach to deal with fuzziness in architecture", Architectural Science Review, Vol. 25, No. 3, pp. 64-69.
  15. Yu, Y., He, X., Wan, F., Bai, Z., Fu, C. (2022), "Dynamic risk assessment of karst tunnel collapse based on fuzzy-AHP: a case study of the LianHuaShan Tunnel, China", Advances in Civil Engineering, Vol. 2022, 4426318.
  16. Zhou, B., Xue, Y., Li, S., Qiu, D., Tao, Y., Zhang, K., Zhang, X., Xia, T. (2020), "Probabilistic analysis of tunnel collapse: Bayesian method for detecting change points", Geomechanics and Engineering, Vol. 22, No. 4, pp. 291-303.