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

Korean Tunnelling and Underground Space Association (한국터널지하공간학회)
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Development of optimized TBM segmental lining design system

TBM 세그먼트 라이닝 최적 설계 시스템 개발

  • Woo, Seungjoo (Dept. of Global Construction Engineering, Sungkyunkwan University) ;
  • Chung, Eunmok (Dept. of Civil Engineering, Sungkyunkwan University) ;
  • Yoo, Chungsik (Dept. of Civil Engineering, Sungkyunkwan University)
  • 우승주 (성균관대학교 글로벌건설엔지니어링학과) ;
  • 정은목 (성균관대학교 건설환경시스템공학과) ;
  • 유충식 (성균관대학교 건설환경시스템공학과)
  • Received : 2015.11.10
  • Accepted : 2015.12.14
  • Published : 2016.01.30
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Abstract

This paper concerns the development of an optimized TBM segmental lining design system for a subsea tunnel. The subsea tunnel is normally laid down under the sea water and submarine ground which consists of soil or rock. 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. Finally, this lining design system aims to be connected with a BIM system for designing the subsea tunnel automatically. The lining member forces are predicted based on the ANN which was calculated by a FEM (finite element analysis) and it helps designers determine its segmental lining dimension easily without any further FE calculations.

본 연구에서는 해저 터널의 특수성을 고려한 TBM 세그먼트 라이닝의 최적 설계 시스템을 개발하였다. 해저 터널은 일반적으로 일정 수압 하의 토사나 암반 등으로 구성된 해저 지반 내에 시공된다. 본 설계 시스템은 특정 해저 터널 단면에서의 지반 조건, 시공 조건 및 터널 조건을 고려하여 인공신경망 기반의 세그먼트 라이닝 부재력 예측 시스템을 구축하고, 시공성이 확보된 단면 DB를 구축하여 해저터널에서 최적 단면 설계가 가능하도록 구성하였다. 결과적으로 본 시스템은 해저 터널 설계에 사용되는 BIM과 연동되어 자동으로 설계가 가능하도록 하였다. 단면 검토 및 설계에 사용되는 세그먼트 라이닝 부재력 예측은 유한요소해석을 토대로 구축한 인공신경망을 통해 일반화한 후 BIM 시스템에 접목시켜 별도의 추가 해석이 필요없이 유사 단면의 해저 터널 설계에 적용이 가능하도록 하였다.

Keywords

References

  1. Bae, G.J., Shin, H.S., Kim, D.G., Chang, S.H., Lim, J.J., Lee, G.P., Choi, S.W. (2005), "Development of technologies for minimizing and preventing the disaster on tunnel construction 2", KICT, pp. 73-87.
  2. Beale, M.H., Hagan, M.T., Demuth, H.B. (2013), Neural Network Toolbox User's Guide, Mathwork Inc.
  3. Choi, J.H., Yoo, C.S. (2015), "TBM segment lining section design of hypothetical subsea tunnels", Journal of Korean Tunnelling and Underground Space Association, Vol. 17, No. 1, pp. 49-63. https://doi.org/10.9711/KTAJ.2015.17.1.049
  4. Garson, G.D. (1991), "Interpreting neural-network connection weights", AI Expert, Vol. 6, No. 7, pp. 47-51.
  5. International Tunnelling Association Working Group No. 2. (2000), "Guidelines for the design of shield tunnel lining", Tunnelling and Underground Space Technology, Vol. 15, No. 3, pp. 303-331. https://doi.org/10.1016/S0886-7798(00)00058-4
  6. Jeong, Y.J., Yoo, C.S. (2014), "Development and implementation of a knowledge based TBM tunnel segment lining design program", Journal of Korean Tunnelling and Underground Space Association, Vol. 16, No. 3, pp. 321-339. https://doi.org/10.9711/KTAJ.2014.16.3.321
  7. Jiao, Y., Hudson, J.A. (1995), "The Fully-Coupled Model for Rock Engineering System", Rock Mechanics and Mining Sciences & Geomechanics, Vol. 32, Issue 5, pp. 491-512. https://doi.org/10.1016/0148-9062(95)00038-I
  8. KCI (2012), Korean Design Standards on Structural Concrete 2012, Korea Concrete Institute.
  9. Kim, C.Y., Bae, G.J., Hong, S.W., Park, C.H., Moon, H.K., Shin, H.S. (2001), "Neural network based prediction of ground surface settlements due to tunneling", Computers and Geotechnics, Vol. 28, pp. 517-547. https://doi.org/10.1016/S0266-352X(01)00011-8
  10. KTA (2008), "3rd Mechanized tunnel construction tunnel design service technical conference", Korean Tunnelling Association, pp. 3-458.
  11. Lee, Y.J., Kim, K.L., Jeong, K.W., Hong, E.J., Kim, S.H., Jun, D.C. (2015), "A study on the design of shield tunnel lining in high water pressure condition", Journal of Korean Tunnelling and Underground Space Association, Vol. 17, No. 3, pp. 227-236. https://doi.org/10.9711/KTAJ.2015.17.3.227
  12. Matlab user manual version R2012b (2012), MathWorks, INC.
  13. Midas users manual version 2012+ (2012), MIDAS IT Co.,Ltd.
  14. 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
  15. 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
  16. Yoo, C.S., Jeon, H.M. (2012), "A comparative study on methods for shield tunnel segment lining sectional forces", Journal of Korean Tunnelling and Underground Space Association, Vol. 14, No. 3, pp. 159-181. https://doi.org/10.9711/KTAJ.2012.14.3.159
  17. Yoo, C.S., Jeon, Y.W., Kim, J.H., Park, Y.J., Yoo, J.H. (2004), "Development and implementation of a GIS-based tunnelling risk management system", Journal of Korean Geotechnical Society, Vol. 20, No. 1, pp. 49-59.
  18. Yoo, C.S., Kim, J.M., Kim, S.B., Jung, H.Y. (2006), "Tunnel design/construction risk assessment based on GIS/ANN", Journal of Korean Society of Civil Engineers, Vol. 26, No. 1C, pp. 63-72.
  19. Yoo, C.S., Kim, S.B. (2007), "A sensitivity analysis on the influencing factors affecting ground surface settlements caused by tunnelling", Korean Geotechnical Society Autumn Conference 2007.
  20. 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.