Development and implementation of a knowledge based TBM tunnel segment lining design program

지식기반형 TBM 터널 세그먼트 라이닝 설계 프로그램의 개발 및 적용

  • 정용준 (성균관대학교 글로벌건설엔지니어링학과) ;
  • 유충식 (성균관대학교 건설환경시스템공학과)
  • Received : 2014.05.10
  • Accepted : 2014.05.27
  • Published : 2014.05.31


This paper concerns the development of a knowledge-based tunnel design system within the framework of artifical neural networks(ANNs). The system is aimed at expediting a routine tunnel design works such as computation of segment lining body forces and stability analysis of selected cross section. A number of sub-modules for computation of segment lining body forces and stability analysis were developed and implemented to the system. It is shown that the ANNs trained with the results of 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.


Supported by : 한국건설교통기술평가원


  1. Bae, G.J., Shin, H.S., Kim, D.G., Chang, S.H., Lim, J.J., Lee, G.P., Choi, S.W., KICT (2005), "Development of technologies for minimizing and preventing the disaster on tunnel construction 2", KICT, pp. 73-87.
  2. KTA (2008), "3rd Mechanized tunnel construction tunnel design service technical conference", KTA, pp. 3-7, 393-399, 457-458.
  3. Yoo, C.S., Kim, J.M., Kim, S.B., Jung, H.Y. (2006), "Tunnel design/construction risk assessment based on GIS-ANN", Journal of the Korean Society of Civil Engineers, Vol. 26, No. 1C, pp. 63-72.
  4. 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.
  5. 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.
  6. 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.
  7. Chang, S.H., Lee, G.P., Choi, S.W., Bae, G.J. (2011), "State of the art of segment lining in shield tunnel and statistical analysis of its key design parameters", Journal of Korean Society of Rock Mechanics, Vol. 6, No. 6, pp. 427-438.
  8. Japan society of civil engineers (2007), Standard specifications for tunneling-2006 : Shield tunnels, Japan society of civil engineers.
  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.
  10. Duddeck, H., Erdmann, J. (1985), "Structure design for tunnels", Tunnel82, Proceedings of the 3rd international symposium, Brighton, 7-11 June 1982, pp. 83-91.
  11. 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.
  12. Muirwood, A.M. (1975), "The circular tunnel in elastic ground", Geotechnique, Vol. 25, No. 1, pp. 115-127.
  13. Mark Hudson Beale, Martic T. Hagan, Howard B. Demuth (2013), Neural Network Toolbox User's Guide, Mathwork Inc.
  14. 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.
  15. 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.
  16. 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.

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