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 knowledge-based study on design of NATM lining for subsea tunnels

지식기반 개념을 이용한 해저터널의 NATM 터널의 라이닝 설계

  • Sin, Chunwon (Dept. of Global Construction Engineering, Sungkyunkwan University) ;
  • Woo, Seungjoo (Dept. of Global Construction Engineering, Sungkyunkwan University) ;
  • Yoo, Chungsik (Dept. of Civil and Environmental Engineering, Sungkyunkwan University)
  • 신춘원 (성균관대학교 글로벌건설엔지니어링학과) ;
  • 우승주 (성균관대학교 글로벌건설엔지니어링학과) ;
  • 유충식 (성균관대학교 토목공학과)
  • Received : 2016.02.23
  • Accepted : 2016.03.11
  • Published : 2016.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper concerns a study of a knowledge-based NATM tunnel lining design for subsea tunnels. Concept for tunnel automation designing system, the development of Artificial Neural Network based technology of the tunnel design system, the learning process and verification of the technology forecasting member forces were described. The design system is the series of process which can predict segmental lining member forces by ANN(artificial neural network system), analyze suitable section for the designated ground, construction and tunnel conditions using a FEM(finite element analysis). The lining member forces are predicted based on the ANN quickly and it helps designers determine its segmental lining dimension easily.

이 논문에서는 해저터널의 특수성을 고려한 NATM 터널의 라이닝의 설계에 대한 내용을 다루었다. 해저터널 자동화 설계 시스템 개발의 요소기술인 인공신경망(Artificial Neural Network) 기반의 터널 설계 시스템에 대한 개념, 학습과정 및 검증과정과 예측된 부재력을 통한 최적 단면 설계에 대한 내용을 기술하였다. 부재력 평가가 정확하게 구현 가능한 인공신경망을 개발하기 위해서 다양한 설계 조건과 각 조건에 따른 해석 모델을 이용한 유한요소해석을 수행하여 단면 설계에 필요한 최대부재력의 학습DB를 구축하고 인공신경망을 통해 일반화하였다. 인공신경망을 이용해 산정된 부재력은 해저 터널의 라이닝 설계 시스템에 적용시켜 빠르고 쉽게 해저터널의 라이닝 설계에 적용이 가능하도록 하였다.

Keywords

References

  1. Beale, M.H., Hagan, M.T., Demuth, H.B. (2013), Neural Network Toolbox User's Guide, Mathwork Inc.
  2. European Committee for Standardization, (2004). "Eurocode 2: design of concrete structures - part 1-1: general rules and rules for buildings".
  3. Garson, G.D. (1991), "Interpreting neural-network connection weights", AI Expert, Vol. 6, No. 7, pp. 47-51.
  4. Lim, S.B. (1997), "Evaluation of dynamic damage adjacent to a blasthole in tunnel excavations", Kyunghee Univ. PhD dissertation.
  5. Midas users manual version 2012+ (2012), MIDAS IT Co., Ltd.
  6. Ministry of Land, Transport and Maritime Affairs (2012), "Standard concrete construction specification 2012".
  7. Park, E.S., Shin, H.S., Hong, E.S. (2007), "Trends of investigation and design for subsea tunnels", KSCE Tunnel Committee Special Conference 27th Dec. pp. 29-41.
  8. Shin, H.S. (2011), "Technology aspects of subsea tunnels", Proceedings of the Korean Society for Rock Mechanics Conference 2011.9, pp. 35-43.
  9. Terzaghi, K. (1943), "Theoretical soil mechanics", J. Wiley & Sons, New York.
  10. Yoo, C.S., Choi, J.H. (2014), "Prediction of TBM tunnel segment lining forces using ANN technique", Journal of Korean Tunnelling and Underground Space Association, Vol. 16, No. 1, pp. 13-24. https://doi.org/10.9711/KTAJ.2014.16.1.013
  11. Yoo, C.S., Kim, S.B., Yoo, K.H. (2008), Development of IT-based tunnel design system, Journal of Korean Tunnelling and Underground Space Association, Vol. 10, No. 2, pp. 153-166.
  12. Yoo, C.S., Kim, J.M., Kim, J.H. (2005), Application of Information Technology in Tunnel Design - A case study, Journal of Korean Tunnelling and Underground Space Association pp. 105-116.
  13. Yang, Y., Zhang, Q. (1997), "A hierarchical analysis for rock engineering using artificial neural networks", Rock Mechanics and Rock Engineering, Vol. 30, Issue 4, pp. 207-222. https://doi.org/10.1007/BF01045717
  14. Yang, Y., Zhang, Q. (1998), "The application of neural networks to rock engineering system (RES)", Rock Mechanics and Mining Sciences, Vol. 35, Issue 6, pp. 727-745. https://doi.org/10.1016/S0148-9062(97)00339-2