인공신경망 기반의 TBM 터널 세그먼트 라이닝 부재력 평가

Prediction of TBM tunnel segment lining forces using ANN technique

  • 유충식 (성균관대학교 건설환경시스템공학과) ;
  • 최정혁 (성균관대학교 건설환경시스템공학과)
  • 투고 : 2013.12.09
  • 심사 : 2013.12.27
  • 발행 : 2014.01.29


본 논문에서는 TBM 터널의 세그먼트 라이닝 설계 자동화 기술 개발의 일환으로 인공신경망기법을 이용한 세그먼트 라이닝 부재력 산정기법 개발에 관한 내용을 다루었다. 부재력 평가가 가능한 인공신경망을 개발하기 위해 먼저 다양한 설계조건을 도출하고 이에 대해 2-Ring Beam 모델을 이용한 유한요소해석을 수행하여 인공신경망 학습에 필요한 설계조건별 부재력에 관한 DB를 구축하였다. 구축된 DB를 활용하여 인공신경망의 최적화 과정을 통해 최대 부재력 및 분포도를 예측할 수 있는 인공신경망을 구축하였다. 검토 결과 구축된 인공신경망은 유한요소해석과 동일한 정밀도의 부재력 산정 기능을 확보하는 것으로 검토되었으며 따라서 TBM 세그먼트 라이닝 설계시 필요한 부재력 평가를 위한 효율적인 수단으로 활용될 수 있는 것으로 판단된다.

This paper presents development of artificial neural network(ANN) based prediction method for section forces of TBM tunnel segment lining in an effort to develop an automatized design technique. A series of design cases were first developed and subsequently analyzed using the two-ring beam finite element model. The results were then used to form a database for use as training and validation data sets for ANN development. Using the database, optimized ANNs were developed that can readily be used to predict maximum sectional forces and their distributions. It is shown that the compute maximum section forces and their distributions by the developed ANNs are almost identical to the computed by the two-ring beam finite element model, implying that the developed ANNs can be used as design tools which expedite routine design calculation process. The results of this study indicate that the neural network model can be effectively used as a reliable and simple predictive tool for the prediction of segment sectional forces for design.



연구 과제 주관 기관 : 한국건설교통기술평가원


  1. Yoo, C.S., Choi, B.S. (2004), "Prediction of deep Excavation-induced ground surface movements using artificial neural network", Journal of Korean Geotechnical Society, Vol. 20, No. 3, pp. 53-65.
  2. Yoo, C.S., Jung, H.Y., Kim, J.M. (2005), "Development of support pattern prediction system using ANN", KSCE Conference, pp. 5563-5566.
  3. Yoo, C.S., Kim, J.M., Kim, S.B., Jung, H.Y. (2006), "Tunnel design/construction risk assessment base 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., Byun, J., Han, D.H. (2006), "ANN-Based prediction on tunnel behavior", KGS Fall Conference, pp. 2073-2080.
  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., Kim, J.M. (2007), "Tunneling performance prediction using an intergrated GIS and neural network", Computers and Geotechincs, Vol. 34, pp. 19-30.
  7. KTA (2008), "3rd Mechanized tunnel construction tunnel design service Technical Conference", KTA, pp. 457-458.
  8. Muirwood, A.M. (1975), "The circular tunnel in elastic ground", Geotechnique, Vol. 25, No. 1, pp. 115-127.
  9. Duddeck, H., Erdmann, J. (1985), "Structure design for tunnels", Tunnel82, Proceedings of the 3rd international symposium, Brighton, 7-11 June 1982, pp. 83-91.
  10. Hecht-Nielsen, R. (1990), Nerocomputing, Addison-Wesly, Readings, Mass.
  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.

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