DOI QR코드

DOI QR Code

A study on the optimum cutter spacing ratio according to penetration depth using decision tree-based and SVM regressions

의사결정나무 기반 회귀분석과 SVM 회귀분석을 이용한 커터 관입깊이에 따른 최적 커터간격 비 연구

  • Lee, Gi-Jun (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Ryu, Hee-Hwan (Korea Electric Power Research Institute (KEPRI)) ;
  • Kwon, Tae-Hyuk (Dept. of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST))
  • 이기준 (한국과학기술원 건설및환경공학과) ;
  • 류희환 (한국전력연구원) ;
  • 권태혁 (한국과학기술원 건설및환경공학과)
  • Received : 2020.07.17
  • Accepted : 2020.08.31
  • Published : 2020.09.30

Abstract

Cutter cutting tests for the cutter placement in the cutter head are being conducted through various studies. Although the cutter spacing at the minimum specific energy is mainly reflected in the cutter head design, since the optimum cutter spacing at the same cutter penetration depth varies depending on the rock conditions, studies on deciding the optimum cutter spacing should be actively conducted. The machine learning techniques such as the decision tree-based regression model and the SVM regression model were applied to predict the optimum cutter spacing ratio for the nonlinear relationship between cutter penetration depth and cutter spacing. Since the decision tree-based methods are greatly influenced by the number of data, SVM regression predicted optimum cutter spacing ratio according to the penetration depth more accurately and it is judged that the SVM regression will be effectively used to decide the cutter spacing when designing the cutter head if a large amount of data of the optimum cutter spacing ratio according to the penetration depth is accumulated.

References

  1. Acaroglu, O., Ozdemir, L., Asbury, B. (2008), "A fuzzy logic model to predict specific energy requirement for TBM performance prediction", Tunnelling and Underground Space Technology, Vol. 23, No. 5, pp. 600-608. https://doi.org/10.1016/j.tust.2007.11.003
  2. Ali, J., Khan, R., Ahmad, N., Maqsood, I. (2012), "Random forests and decision trees", International Journal of Computer Science Issues (IJCSI), Vol. 9, No. 5, pp. 272-278.
  3. Aroef, C., Rivan, Y., Rustam, Z. (2020), "Comparing random forest and support vector machines for breast cancer classification", Telkomnika, Vol. 18, No. 2, pp. 815-821. https://doi.org/10.12928/telkomnika.v18i2.14785
  4. Bae, G.J., Chang, S.H., Park, Y.T., Choi, S.W., Lee, G.P., Kwon, J.Y., Han, K.T. (2014), "Manufacturing of an earth pressure balanced shield TBM cutterhead for a subsea discharge tunnel and its field performance analysis", Journal of Korean Tunnelling and Underground Space Association, Vol. 16, No. 2, pp. 161-172. https://doi.org/10.9711/KTAJ.2014.16.2.161
  5. Bieniawski, R.Z.T., Celada, B., Tardaguila, I., Rodrigues, A. (2012), "Specific energy of excavation in detecting tunnelling conditions ahead of TBMs", Tunnels and Tunnelling International, February 1, pp. 65-68.
  6. Chang, S.H., Choi, S.W., Bae, G.J., Jeon, S. (2006), "Performance prediction of TBM disc cutting on granitic rock by the linear cutting test", Tunnelling and Underground Space Technology, Vol. 21, No. 3, pp. 271-277. https://doi.org/10.1016/j.tust.2005.12.131
  7. Cho, J.W., Jeon, S., Jeong, H.Y., Chang, S.H. (2013), "Evaluation of cutting efficiency during TBM disc cutter excavation within a Korean granitic rock using linear-cutting-machine testing and photogrammetric measurement", Tunnelling and Underground Space Technology, Vol. 35, pp. 37-54. https://doi.org/10.1016/j.tust.2012.08.006
  8. Cho, J.W., Jeon, S., Yu, S.H., Chang, S.H. (2010), "Optimum spacing of TBM disc cutters: A numerical simulation using the three-dimensional dynamic fracturing method", Tunnelling and Underground Space Technology, Vol. 25, No. 3, pp. 230-244. https://doi.org/10.1016/j.tust.2009.11.007
  9. Esmaily, H., Tayefi, M., Doosti, H., Ghayour-Mobarhan, M., Nezami, H., Amirabadizadeh, A. (2018), "A comparison between decision tree and random forest in determining the risk factors associated with type 2 diabetes", Journal of Research in Health Sciences, Vol. 18, No. 2, pp. 412-418.
  10. Geng, Q., Wei, Z., Meng, H., Macias, F.J. (2016), "Mechanical performance of TBM cutterhead in mixed rock ground conditions", Tunnelling and Underground Space Technology, Vol. 57, pp. 76-84. https://doi.org/10.1016/j.tust.2016.02.012
  11. Gertsch, R., Gertsch, L., Rostami, J. (2007), "Disc cutting tests in Colorado Red Granite: Implications for TBM performance prediction", International Journal of Rock Mechanics and Mining Sciences, Vol. 44, No. 2, pp. 238-246. https://doi.org/10.1016/j.ijrmms.2006.07.007
  12. Huo, J., Sun, W., Chen, J., Zhang, X. (2011), "Disc cutters plane layout design of the full-face rock tunnel boring machine (TBM) based on different layout patterns", Computers and Industrial Engineering, Vol. 61, No. 4, pp. 1209-1225. https://doi.org/10.1016/j.cie.2011.07.011
  13. Jeong, H.Y., Jeon, S.W., Cho, J.W., Chang, S.H., Bae, G.J. (2011), "Assessment of cutting performance of a TBM disc cutter for anisotropic rock by linear cutting test", Tunnel and Underground Space, Vol. 21, No. 6, pp. 508-517. https://doi.org/10.7474/TUS.2011.21.6.508
  14. Kim, S.H., Kim, J.T., Lim, C.H. (2012), "A study on the arrangement design of Shield-TBM cutter bit", Journal of the Korean Geotechnical Society, Vol. 28, No. 5, pp. 67-76. https://doi.org/10.7843/kgs.2012.28.5.67
  15. La, Y.S., Kim, M.I., Kim, B. (2019), "Prediction of replacement period of shield TBM disc cutter using SVM", Journal of Korean Tunnelling and Underground Space Association, Vol. 21, No. 5, pp. 641-656. https://doi.org/10.9711/KTAJ.2019.21.5.641
  16. Larios, N., Soran, B., Shapiro, L.G., Martinez-Muñoz, G., Lin, J., Dietterich, T.G. (2010), "Haar random forest features and SVM spatial matching kernel for stonefly species identification", Proceedings of the 2010 20th International Conference on Pattern Recognition, IEEE, Istanbul, pp. 2624-2627.
  17. Lee, G.J., Kwon, T.H., Kim, K.Y., Song, K.I. (2017), "Relationship between brittleness index of hard rocks and TBM penetration rates", Journal of Korean Tunnelling and Underground Space Association, Vol. 19, No. 4, pp. 611-634. https://doi.org/10.9711/KTAJ.2017.19.4.611
  18. Lee, S.J., Choi, S.O. (2013), "Three dimensional numerical analysis on rock cutting behavior of disc cutter using particle flow code", Tunnel and Underground Space, Vol. 23, No. 1, pp. 54-65. https://doi.org/10.7474/TUS.2013.23.1.054
  19. Ma, H., Gong, Q., Wang, J., Yin, L., Zhao, X. (2016), "Study on the influence of confining stress on TBM performance in granite rock by linear cutting test", Tunnelling and Underground Space Technology, Vol. 57, pp. 145-150. https://doi.org/10.1016/j.tust.2016.02.020
  20. Murugan, A., Nair, S.A.H., Sanal Kumar, K.P. (2019), "Detection of skin cancer using SVM, Random Forest and kNN classifiers", Journal of Medical Systems, Vol. 43, No. 8, pp. 269-277. https://doi.org/10.1007/s10916-019-1400-8
  21. Naghibi, S.A., Ahmadi, K., Daneshi, A. (2017), "Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping", Water Resources Management, Vol. 31, No. 9, pp. 2761-2775. https://doi.org/10.1007/s11269-017-1660-3
  22. Pan, Y., Liu, Q., Peng, X., Kong, X., Liu, J., Zhang, X. (2018), "Full-scale rotary cutting test to study the influence of disc cutter installment radius on rock cutting forces", Rock Mechanics and Rock Engineering, Vol. 51, No. 7, pp. 2223-2236. https://doi.org/10.1007/s00603-018-1460-y
  23. Peng, X., Liu, Q., Pan, Y., Lei, G., Wei, L., Luo, C. (2018), "Study on the influence of different control modes on TBM disc cutter performance by rotary cutting tests", Rock Mechanics and Rock Engineering, Vol. 51, No. 3, pp. 961-967. https://doi.org/10.1007/s00603-017-1368-y
  24. QYResearch (2013), Deep research report on global and China tunnel boring machine industry.
  25. Raczko, E., Zagajewski, B. (2017), "Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images", European Journal of Remote Sensing, Vol. 50, No. 1, pp. 144-154. https://doi.org/10.1080/22797254.2017.1299557
  26. Rostami, J., Chang, S.H. (2017), "A closer look at the design of cutterheads for hard rock tunnel-boring machines", Engineering, Vol. 3, No. 6, pp. 892-904. https://doi.org/10.1016/j.eng.2017.12.009
  27. Rostami, J., Ozdemir, L. (1993), "A new model for performance prediction of hard rock TBMs", Proceedings of the Rapid Excavation and Tunneling Conference, Boston, pp. 793-809.
  28. Tan, Q., Yi, L., Xia, Y.M. (2018), "Performance prediction of TBM disc cutting on marble rock under different load cases", KSCE Journal of Civil Engineering, Vol. 22, No. 4, pp. 1466-1472. https://doi.org/10.1007/s12205-017-1048-1
  29. Tao, Y., Yu, H., Yang, W.L., Xi, H.B., Wang, J.B. (2017), "Cutter layout design for tunnel boring machine (TBM) using fuzzy collaborative optimization model", Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), IEEE, pp. 1315-1320.
  30. Teale, R. (1965), "The concept of specific energy in rock drilling", International Journal of Rock Mechanics and Mining Sciences and Geomechanics Abstracts, Vol. 2, No. 1, pp. 57-73. https://doi.org/10.1016/0148-9062(65)90022-7
  31. Thanh Noi, P., Kappas, M. (2018), "Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery", Sensors, Vol. 18, No. 1, pp. 18-37. https://doi.org/10.3390/s18010018
  32. Xia, Y.M., Ouyang, T., Zhang, X.M., Luo, D.Z. (2012), "Mechanical model of breaking rock and force characteristic of disc cutter", Journal of Central South University, Vol. 19, No. 7, pp. 1846-1852. https://doi.org/10.1007/s11771-012-1218-8