Geomechanics and Engineering

  • 제25권6호
  • /
  • Pages.439-453
  • /
  • 2021
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Influence of different combinations of measurement while drilling parameters by artificial neural network on estimation of tunnel support patterns

  • Liu, Jiankang (College of Energy and Mining Engineering, Shandong University of Science and Technology) ;
  • Jiang, Yujing (Graduate School of Engineering, Nagasaki University) ;
  • Zhang, Yuanchao (Graduate School of Engineering, Nagasaki University) ;
  • Sakaguchi, Osamu (Department of Civil Engineering, Konoike Construction Co., Ltd.)
  • 투고 : 2020.02.01
  • 심사 : 2021.06.08
  • 발행 : 2021.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

In tunnel engineering, the selection of tunnel support patterns should be estimated accurately to ensure stability of the tunnel, which may be caused by unexpected hazardous zones ahead of tunnel face. This study presents a method to estimate the selection of support patterns using artificial neural network (ANN) based on 318, 649 Measurement While Drilling (MWD) data. Controlled trials are conducted considering different input layer sizes and hidden layer sizes to obtain the optimal ANN model. Combinations of 6 feature parameters including penetration rate (PR), hammer pressure (HP), rotation pressure (RP), feed pressure (FP), hammer frequency (HF) and specific energy (SE) correspond to the different input layer sizes of the ANN. Average accuracy (A), average computing-time (T), sensitivity and stability are adopted as the performance index. The results show that a strong correlation exists between MWD data and support patterns. The combination of 6 feature parameters outperforms the subset of the entire feature parameters in terms of A, sensitivity and stability. The ANN model with the combination of PR, HP, RP, FP, HF and SE as the input feature parameters has the highest estimation stability. The ANN model with 6 feature parameters and one hidden layer with 30 nodes is proposed as optimal model considering all indices. The results confirm that it is feasible to estimate support patterns ahead of tunnel face using ANN based on MWD data.

키워드

과제정보

The authors gratefully acknowledge support of Civil Engineering Department, Technical Division, Konoike Construction Japan for providing field data and sharing experience on tunnel construction.

참고문헌

  1. Basu, J.K., Bhattacharyya, D. and Kim, T. (2010), "Use of artificial neural network in pattern recognition", Int. J. Software Eng. Appl., 4(2).
  2. Ben Ali, J., Fnaiech, N., Saidi, L., Chebel-Morello, B. and Fnaiech, F. (2015), "Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals", Appl. Acoust., 89, 16-27. https://doi.org/10.1016/j.apacoust.2014.08.016.
  3. Bizjak, K.F. and Petkovsek, B. (2004), "Displacement analysis of tunnel support in soft rock around a shallow highway tunnel at Golovec", Eng. Geol., 75(1), 89-106. https://doi.org/10.1016/j.enggeo.2004.05.003.
  4. Dahlin, T., Bjelm, L. and Svensson, C. (1999), "Use of electrical imaging in site investigations for a railway tunnel through the Hallandsas Horst, Sweden", Q. J. Eng. Geol. Hydrogeol., 32(2), 163-172. https://doi.org/10.1144/GSL.QJEG.1999.032.P2.06.
  5. Dreyfus, G. (2005), Neural Networks: Methodology and Applications, Springer Science & Business Media,
  6. El-Naqa, A. (2001), "Application of RMR and Q geomechanical classification systems along the proposed Mujib Tunnel route, central Jordan", B. Eng. Geol. Environ., 60(4), 257-269. https://doi.org/10.1007/s100640100112.
  7. Elyasi, A., Javadi, M., Moradi, T., Moharrami, J., Parnian, S. and Amrac, M. (2016), "Numerical modeling of an umbrella arch as a pre-support system in difficult geological conditions: A case study", B. Eng. Geol. Environ., 75(1), 211-221. https://doi.org/10.1007/s10064-015-0738-5.
  8. Friant, J.E., Bauer, R.A., Gross, D.L., May, M. and Lach, J. (1997), "Pipetron tunnel construction issues", Fermi National Accelerator Lab.(FNAL), Batavia, Illinois, U.S.A.
  9. Galende-Hernandez, M., Menendez, M., Fuente, M.J. and Sainz-Palmero, G.I. (2018), "Monitor-while-drilling-based estimation of rock mass rating with computational intelligence: The case of tunnel excavation front", Autom. Constr., 93, 325-338. https://doi.org/10.1016/j.autcon.2018.05.019.
  10. Garcia-Pedrajas, N., Hervas-Martinez, C. and Ortiz-Boyer, D. (2005), "Cooperative coevolution of artificial neural network ensembles for pattern classification", IEEE T. Evol. Comput., 9(3), 271-302. https://doi.org/10.1109/TEVC.2005.844158.
  11. Garson, G.D. (1998), Neural Networks: An Introductory Guide for Social Scientists, Sage, London, U.K.
  12. Ghorbani, A., Hasanzadehshooiili, H. and Sadowski, L. (2018), "Neural prediction of tunnels' support pressure in elasto-plastic, strain-softening rock mass", Appl. Sci., 8(5), 841. https://doi.org/10.3390/app8050841.
  13. Ghosh, R., Schunnesson, H. and Kumar, U. (2015), "The use of specific energy in rotary drilling: The effect of operational parameters", Proceedings of the 37th International Symposium on the Application of Computers and Operations Research in the Mineral Industry, Fairbanks, Alaska, U.S.A., May.
  14. Gong, Q., Yin, L. and She, Q. (2013), "TBM tunneling in marble rock masses with high in situ stress and large groundwater inflow: A case study in China", B. Eng. Geol. Environ., 72(2), 163-172. https://doi.org/10.1007/s10064-013-0460-0.
  15. Guan, Z., Jiang, Y. and Tanabashi, Y. (2009), "Rheological parameter estimation for the prediction of long-term deformations in conventional tunnelling", Tunn. Undergr. Sp. Tech., 24(3), 250-259. https://doi.org/10.1016/j.tust.2008.08.001.
  16. Hecht-Nielsen, R. (1987), "Kolmogorov's mapping neural network existence theorem", Proceedings of the International Conference on Neural Networks, Alexandria, Virginia, U.S.A., October.
  17. Hsu, K.L., Gupta, H.V. and Sorooshian, S. (1995), "Artificial neural network modeling of the rainfall-runoff process", Water Resour. Res., 31(10), 2517-2530. https://doi.org/10.1029/95WR01955.
  18. Humstad, T., Hoien, A.H., Kveen, A. and Hoel, J.E. (2012), "Complete software overview of rock mass and support in Norwegian road tunnels", Proceedings of the ISRM International Symposium - EUROCK 2012, Stockholm, Sweden, May.
  19. Hush, D.R. (1989), "Classification with neural networks: A performance analysis", Proceedings of the IEEE International Conference on Systems Engineering, Fairborn, Ohio, U.S.A., August.
  20. Hussain, S., Mohammad, N., Khan, M., Rehman, Z.U. and Tahir, M. (2016), "Comparative analysis of rock mass rating prediction using different inductive modeling techniques", Int. J. Min. Eng. Miner. Process., 5(1), 9-15. https://doi.org/10.5923/j.mining.20160501.02.
  21. Kanellopoulos, I. and Wilkinson, G.G. (1997), "Strategies and best practice for neural network image classification", Int. J. Remote Sens., 18(4), 711-725. https://doi.org/10.1080/014311697218719.
  22. Kaya, A., Bulut, F. and Sayin, A. (2011), "Analysis of support requirements for a tunnel portal in weak rock: A case study from Turkey", Sci. Res. Essays, 6(31), 6566-6583. https://doi.org/10.5897/SRE11.1691.
  23. Kim, C.Y., Bae, G.J., Hong, S.W., Park, C.H., Moon, H.K. and Shin, H.S. (2001), "Neural network based prediction of ground surface settlements due to tunnelling", Comput. Geotech., 28(6), 517-547. https://doi.org/10.1016/S0266-352X(01)00011-8.
  24. Kontogianni, V., Tzortzis, A. and Stiros, S. (2004), "Deformation and failure of the Tymfristos tunnel, Greece", J. Geotech. Geoenviron. Eng., 130(10), 1004-1013. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:10(1004).
  25. Koopialipoor, M., Ghaleini, E.N., Tootoonchi, H., Jahed Armaghani, D., Haghighi, M. and Hedayat, A. (2019), "Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN", Environ. Earth Sci., 78(5), 165. https://doi.org/10.1007/s12665-019-8163-x.
  26. Kumar, R., Kumaraswamidhas, L.A., Murthy, V.M.S.R. and Vettivel, S.C. (2019), "Experimental investigations on machine vibration in blast-hole drills and optimization of operating parameters", Measurement, 145, 803-819. https://doi.org/10.1016/j.measurement.2019.05.069.
  27. Kun, M. and Onargan, T. (2013), "Influence of the fault zone in shallow tunneling: A case study of Izmir Metro Tunnel", Tunn. Undergr. Sp. Tech., 33, 34-45. https://doi.org/10.1016/j.tust.2012.06.016.
  28. Kwon, S. and Lee, C. (2018)," "HM analysis for an in situ experiment using FLAC3D-TOUGH2 and an artificial neural network", Geomech. Eng., 16(4), 363-373. http://doi.org/10.12989/gae.2018.16.4.363.
  29. Leu, S.S.,Chen, C.N. and Chang, S.L. (2001), "Data mining for tunnel support stability: Neural network approach", Autom. Constr., 10(4), 429-441. https://doi.org/10.1016/S0926-5805(00)00078-9.
  30. Li, L., Li, S., Zhao, Y., Wang, H., Liu, Q., Yuan, X., Zhao, Y. and Zhang, Q. (2012), "Spatial deformation mechanism and load release evolution law of surrounding rock during construction of super-large section tunnel with soft broken surrounding rock masses", Chin. J. Rock Mech. Eng., 10, 2109-2118.
  31. Linden, P. (2005), "Val av borrklass kopplat till MWD, bergklass samt vattenflode vid projektet Hallandsas".
  32. Liu, J., Sakaguchi, O., Ishizu, S., Luan, H., Han, W. and Jiang, Y. (2020), "Application of specific energy in evaluation of geological conditions ahead of tunnel face", Energies, 13(4), 909. https://doi.org/10.3390/en13040909.
  33. Lo, S.C.B., Chan, H.P., Lin, J.S., Li, H., Freedman, M.T. and Mun, S.K. (1995), "Artificial convolution neural network for medical image pattern recognition", Neural Networks, 8(7-8), 1201-1214. https://doi.org/10.1016/0893-6080(95)00061-5.
  34. Mahdevari, S. and Torabi, S.R. (2012), "Prediction of tunnel convergence using artificial neural networks", Tunn. Undergr. Sp. Tech., 28, 218-228. https://doi.org/10.1016/j.tust.2011.11.002.
  35. Mahdevari, S., Torabi, S.R. and Monjezi, M. (2012), "Application of artificial intelligence algorithms in predicting tunnel convergence to avoid TBM jamming phenomenon", Int. J. Rock Mech. Min. Sci., 55, 33-44. https://doi.org/10.1016/j.ijrmms.2012.06.005.
  36. McCulloch, W.S. and Pitts, W. (1943), "A logical calculus of the ideas immanent in nervous activity", B. Math. Biophys., 5(4), 115-133. https://doi.org/10.1007/BF02478259.
  37. Mikaeil, R., Haghshenas, S.S., Shirvand, Y., Hasanluy, M.V. and Roshanaei, V. (2016), "Risk assessment of geological hazards in a tunneling project using harmony search algorithm (case study: Ardabil-Mianeh railway tunnel)", Civ. Eng. J., 2(10), 546-554. https://doi.org/10.28991/cej-2016-00000057.
  38. Miura, K. (2003), "Design and construction of mountain tunnels in Japan", Tunn. Undergr. Sp. Tech., 18(2), 115-126. https://doi.org/10.1016/S0886-7798(03)00038-5.
  39. Navarro, J., Sanchidrian, J.A., Segarra, P., Castedo, R., Paredes, C. and Lopez, L.M. (2018), "On the mutual relations of drill monitoring variables and the drill control system in tunneling operations", Tunn. Undergr. Sp. Tech., 72, 294-304. https://doi.org/10.1016/j.tust.2017.10.011.
  40. Nikafshan Rad, H., Jalali, Z. and Jalalifar, H. (2015), "Prediction of rock mass rating system based on continuous functions using Chaos-ANFIS model", Int. J. Rock Mech. Min. Sci., 73, 1-9. https://doi.org/10.1016/j.ijrmms.2014.10.004.
  41. Nilsen, B. (2015), "Main challenges for deep subsea tunnels based on norwegian experience", J. Kor. Tunn. Undergr. Sp. Assoc., 17(5), 563-573. https://doi.org/10.9711/KTAJ.2015.17.5.563.
  42. Ocak, I. and Seker, S.E. (2013), "Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes", Environ. Earth Sci., 70(3), 1263-1276. https://doi.org/10.1007/s12665-012-2214-x.
  43. Paola, J. (1994), "Neural network classification of multispectral imagery", M.Sc. Thesis, The University of Arizona, Tucson, Arizona, U.S.A.
  44. Park, J., Lee, K.H., Kim, B.K., Choi, H. and Lee, I.M. (2017), |Predicting anomalous zone ahead of tunnel face utilizing electrical resistivity: II. Field tests", , Tunn. Undergr. Sp. Tech., 68, 1-10. https://doi.org/10.1016/j.tust.2017.05.017.
  45. Qiu, D., Li, S., Xu, Y., Tian, H. and Yan, M. (2014), "Advanced prediction of surrounding rock classification based on digital drilling technology and QGA-RBF neural network", Rock Soil Mech., (7), 2013-2018.
  46. Rafiq, M.Y., Bugmann, G. and Easterbrook, D.J. (2001), "Neural network design for engineering applications", Comput. Struct., 79(17), 1541-1552. https://doi.org/10.1016/S0045-7949(01)00039-6.
  47. Ripley, B.D. (1993), "Statistical aspects of neural networks", Networks Chaos Stat. Probab. Aspects, 50, 40-123. https://doi.org/10.1007/978-1-4899-3099-6_2
  48. Schunnesson, H. (1997), "Drill process monitoring in percussive drilling for location of structural features, lithological boundaries and rock properties, and for drill productivity evaluation", Ph.D. Dissertation, Lulea University of Technology, Lulea, Sweden.
  49. Schunnesson, H. (1998), "Rock characterisation using percussive drilling", Int. J. Rock Mech. Min. Sci., 35(6), 711-725. https://doi.org/10.1016/S0148-9062(97)00332-X.
  50. Schunnesson, H. (1996), "RQD predictions based on drill performance parameters", Tunn. Undergr. Sp. Tech., 11(3), 345-351. https://doi.org/10.1016/0886-7798(96)00024-7.
  51. Schunnesson, H., Elsrud, R. and Rai, P. (2011), "Drill monitoring for ground characterization in tunnelling operations", Proceedings of the International Symposium on Mine Planning and Equipment Selection, Almaty, Kazakhstan, October.
  52. Segui, J. and Higgins, M. (2002), "Blast design using measurement while drilling parameters", Fragblast, 6(3-4), 287-299. https://doi.org/10.1076/frag.6.3.287.14052.
  53. Singh, B., Jethwa, J., Dube, A. and Singh, B. (1992), "Correlation between observed support pressure and rock mass quality", Tunn. Undergr. Sp. Tech., 7(1), 59-74. https://doi.org/10.1016/0886-7798(92)90114-W.
  54. Soupios, P., Loupasakis, C. and Vallianatos, F. (2008), "Reconstructing former urban environments by combining geophysical electrical methods and geotechnical investigations-an example from Chania, Greece", J. Geophys. Eng., 5(2), 186-194. https://doi.org/10.1088/1742-2132/5/2/005.
  55. Suwansawat, S. and Einstein, H.H. (2006), "Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling", Tunn. Undergr. Sp. Tech., 21(2), 133-150. https://doi.org/10.1016/j.tust.2005.06.007.
  56. Toghroli, A., Mohammadhassani, M., Suhatril, M., Shariati, M. and Ibrahim, Z. (2014), "Prediction of shear capacity of channel shear connectors using the ANFIS model", Steel Compos. Struct., 17(5), 623-639. http://dx.doi.org/10.12989/scs.2014.17.5.000.
  57. Utt, W.K. (1999), "Neural network technology for strata strength characterization", Proceedings of the International Joint Conference on Neural Networks, Middleton, Wisconsin, U.S.A., July.
  58. Utt, W.K., Miller, G.G., Howie, W.L. and Woodward, C.C. (2002), "Drill monitor with strata strength classification in near-real time", Report of Investigations, No. 2002-2141, National