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

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

DOI QR Code

Design of umbrella arch method based on adaptive SVM and reliability concept

Adaptive SVM 기법 및 신뢰성 개념을 적용한 강관다단공법의 설계기법 연구

  • Lee, Jun S. (Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute) ;
  • Sagong, Myung (Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute) ;
  • Park, Jeongjun (Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute) ;
  • Choi, Il Yoon (Advanced Railroad & Civil Engineering Division, Korea Railroad Research Institute)
  • 이준석 (한국철도기술연구원 첨단궤도토목본부) ;
  • 사공명 (한국철도기술연구원 첨단궤도토목본부) ;
  • 박정준 (한국철도기술연구원 첨단궤도토목본부) ;
  • 최일윤 (한국철도기술연구원 첨단궤도토목본부)
  • Received : 2018.05.30
  • Accepted : 2018.07.03
  • Published : 2018.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

A reliability based design approach of the tunnel reinforcement with umbrella arch method was considered to better represent the uncertainties of the weak rock properties around the tunnel. For this, a machine learning approach called an Adaptive Support Vector Machine (ASVM) together with the limit equilibrium method were introduced to minimize the iteration numbers during the classification training of the tunnel stability. The proposed method was compared with the results of typical Monte Carlo simulations. It was concluded that the ASVM was very efficient and accurate to calculate the probability of failure having auxiliary umbrella arches and uncertain material properties of the tunnel. Future work will be concentrated on the refinement of the fast adaptation of the SVM classification so that the minimum number of numerical analyses can be used where the limit solution is not available.

본 연구에서는 터널주변 원지반의 불확실성을 고려한 신뢰성기반 강관다단공법의 설계기법에 대하여 논의하였다. 이를 위하여 기계학습기법의 한 부류인 adaptive support vector machine과 시공 중인 터널의 한계평형해석기법을 도입한 후, 강관다단공법을 적용한 터널의 안전성 여부에 대한 훈련과정을 최소화할 수 있는 방안을 제안하였다. 제안한 기법은 전형적인 Monte Carlo 기법과의 비교를 통해 그 효과를 분석하였다. 이 결과, 제안한 신뢰성기반 ASVM 기법은 원지반의 불확실성을 감안하는 경우, 보조공법 적용에 따른 터널의 시공 중 파괴확률을 효율적으로 계산할 수 있음을 입증하였다. 이 결과를 바탕으로 향후에는 한계평형해석을 적용할 수 없는 경우 등을 감안하여 최소의 수치해석 결과를 바탕으로 파괴확률을 추론해 낼 수 있는 신속 ASVM 기법을 개발할 예정이다.

Keywords

References

  1. Basudhar, A., Missoum, S. (2008), "Adaptive explicit decision functions for probabilistic design and optimization using support vector machines", Computers and Structures, Vol. 86, No. 19-20, pp. 1904-1917. https://doi.org/10.1016/j.compstruc.2008.02.008
  2. Chang, C.-C., Lin, C.-J. (2013), LIBSVM: A library for support vector machines, Report, Dept. Computer Science, National Taiwan University, pp. 1-39.
  3. Cortes, C., Vapnik, V. (1995), "Support-vector networks", Machine Learning, Vol. 20, No. 3, pp. 273-297. https://doi.org/10.1007/BF00994018
  4. Dias, D., Oreste, P. (2013), "Key factors in the face stability analysis of shallow tunnels", American Journal of Applied Sciences, Vol. 10, No. 9, pp. 1025-1038. https://doi.org/10.3844/ajassp.2013.1025.1038
  5. Hamrouni, A., Dias, D., Sbartai, B. (2017), "Reliability analysis of shallow tunnels using the response surface methodology", Underground Space, Vol. 2, No. 4, pp. 246-258. https://doi.org/10.1016/j.undsp.2017.11.003
  6. Hariri-Ardebili, M., Pourkamali-Anaraki, F. (2018), "Support vector machine based reliability analysis of concrete dams", Soil Dynamics and Earthquake Engineering, Vol. 104, pp.276-295. https://doi.org/10.1016/j.soildyn.2017.09.016
  7. Ji, J., Zhang, C., Gui, Y., Lu, Q., Kodikara, J. (2016), "New observations on the application of LS-SVM in slope system reliability analysis", Journal of Computing in Civil Engineering, Vol. 31, No. 2, 06016002-1:9.
  8. Kim, S.H. (2010), "The effects of stability of the tunnel reinforced by rebar steel pipe", Journal of Korean Tunnelling and Underground Space Association, Vol. 12, No. 5, pp. 389-397.
  9. KR Network (2014), Excavation, KR C-12070.
  10. Lee, J.S., Bang, C.S., Mok, Y.J., Joh, S.H. (2000), "Numerical and experimental analysis of penetration grouting in jointed rock masses", International Journal of Rock Mechanics and Mining Sciences, Vol. 37, No. 7, pp. 1027-1037. https://doi.org/10.1016/S1365-1609(00)00040-X
  11. Lee, J.S., Choi, I.Y., Kim, I.K., Hwang, S.H. (2018a), "Tamping and renewal optimization of ballasted track using track measurement data and genetic algorithm", Journal of Transportation Engineering, Part A: systems, Vol. 144, No. 3, pp. 04017081-1:8.
  12. Lee, J.S., Hwang, S.H., Choi, I.Y., Kim I.K. (2018b), "Prediction of track deterioration using maintenance data and machine learning schemes", Journal of Transportation Engineering, Part A: systems, Vol. 144, No. 9, pp. 04018045-1:9.
  13. MathWorks (2018), MATLAB: R2018a, MathWorks.
  14. Mollon, G., Dias, D., Soubra, A. (2011), "Rotational failure mechanisms for the face stability analysis of tunnels driven by a pressurized shield", International Journal for Numerical and Analytical Methods in Geomechanics, Vol. 35, No. 12, pp.1363-1388. https://doi.org/10.1002/nag.962
  15. Mouyeaux, A., Garvajal, C., Bressolette, P., Peyras, L., Breul, P., Bacconnet, C. (2018), "Probabilistic stability analysis of an earth dam by stochastic finite element method based on field data", Computers and Geotechnics, Vol. 101, pp.34-47. https://doi.org/10.1016/j.compgeo.2018.04.017
  16. Oke, J., Vlachopoulos, N., Diederichs, M. (2016). Semi-analytical model for umbrella arch systems employed in squeezing ground conditions", Tunnelling and Underground Space Technology, Vol. 56, pp. 136-156. https://doi.org/10.1016/j.tust.2016.03.006
  17. Oreste, P.P. (2009), "Face stabilization of shallow tunnels using fiberglass dowels", PICE Geotechnical Engineering, Vol. 162, No. 2, pp. 95-109.
  18. Oreste, P.P., Dias, D. (2012), "Stabilisation of the excavation face in shallow tunnels using fiberglass dowels", Rock Mechanics and Rock Engineering, Vol. 45, No. 4, pp. 499-517. https://doi.org/10.1007/s00603-012-0234-1
  19. Pan, Q., Dias, D. (2017a), "An efficient reliability method combining adaptive support vector machine and Monte Carlo simulation", Structural Safety, Vol. 67, pp. 85-95. https://doi.org/10.1016/j.strusafe.2017.04.006
  20. Pan, Q., Dias, D. (2017b), "Safety factor assessment of a tunnel face reinforced by horizontal dowels", Engineering Structures, Vol. 142, pp. 56-66. https://doi.org/10.1016/j.engstruct.2017.03.056
  21. Perazzelli, P., Anagnostou, G. (2017), "Analysis method and design charts for bolt reinforcement of the tunnel face in purely cohesive soils", Journal of Geotechnical and Geoenvironmental Engineering, Vol. 143, No. 9, pp. 04017046-1:15.
  22. Sim, Y., Jin, K.N. (2016), "Experimental analysis for the effect of integrated pipe-roof in trenchless method", Journal of Korean Tunnelling and Underground Space Association, Vol. 18, No. 5, pp. 377-387. https://doi.org/10.9711/KTAJ.2016.18.5.377
  23. Smola, A.J., Scholkopf, B. (2004), "A tutorial on support vector regression", Statistics and Computing, Vol. 14, No. 3, pp. 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  24. Song, H., Choi, K.K., Lee, I., Zhao, L., Lamb, D. (2013), "Adaptive virtual support vector machine for reliability analysis of high-dimensional problems", Structural and Multidisciplinary Optimization, Vol. 47, No. 4, pp. 479-491. https://doi.org/10.1007/s00158-012-0857-6
  25. Song, K.I., Kim, J., Cho, G.C. (2007), "Numerical analysis of pre-reinforced zones in tunnel considering the time-dependent grouting performance", Journal of Korean Tunnelling and Underground Space Association, Vol. 9, No. 2, pp. 109-120.
  26. Wang, H., Jia, J. (2009), "Face stability analysis of tunnel with pipe roof reinforcement based on limit analysis", Electronic Journal of Geotechnical Engineering, Vol. 14, pp. 1-15.