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2020 Vol.30, Issue 6 Preview Page

Original Article

December 2020. pp. 540-550
Abstract
References
1
Armaghani, D.J., Mohamad, E.T., Narayanasamy, M.S., Narita, N., Yagiz, S., 2017, Development of hybrid intelligent models for predicting TBM penetration rate in hardrock condition, Tunn. Undergr. Space Technol. Vol. 63, pp. 29-43. 10.1016/j.tust.2016.12.009
2
Breiman, L, Friedman, J, Stone, C.J, and Olshen, R.A., 1984, Classification and Regression Trees. CRC press.
3
Breiman, L., 1996, Bagging Predictors, Machine Learning, Vol. 24, No. 2, pp. 123-140. 10.1007/BF00058655. 10.1007/BF00058655
4
Breiman, L., 2001, Random Forests, Machine Learning, Vol. 45, No. 1, pp. 5-32. 10.1023/A:1010933404324. 10.1023/A:1010933404324
5
Chin, J. H. J., 2020, In Pursuit of the Autonomous TBM, Tunneling Journal, February/March 2020, pp. 18-22.
6
Gholamnejad, J., Narges, T., 2010, Application of artificial neural networks to the prediction of tunnel boring machine penetration rate, Min. Sci. Technol. (China) Vol. 20, No.5, pp. 727-733. 10.1016/S1674-5264(09)60271-4
7
Grima, M.A., Bruines, P.A., Verhoef, P.N.W., 2000, Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunn. Undergr. Space Technol. Vol. 15, No.3, pp. 259-269. 10.1016/S0886-7798(00)00055-9
8
Kim, T. H., Ko, T. Y., Park, Y. S., Kim, T. K., Lee, D. H., 2020, Prediction of Uniaxial Compressive Strength of Rock using Shield TBM Machine Data and Machine Learning Technique, TUNNEL & UNDERGROUND SPACE, Vol.30, No.3, pp. 214-225.
9
Mahdevari, S., Shahriar, K., Yagiz, S., Shirazi, M.A., 2014, A support vector regression model for predicting tunnel boring machine penetration rates, Int. J. Rock Mech. Min., Vol. 74, pp. 214-229. 10.1016/j.ijrmms.2014.09.012
10
Mokhtari, S., Mooney, M.A., 2020, Predicting EPBM advance rate performance using support vector regression modeling, Tunn. Undergr. Space Technol. Vol.104, 103520. 10.1016/j.tust.2020.103520
11
Salimi, A., Rostami, J., Moormann, C., Delisio, A., 2016, Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs, Tunn. Undergr. Space Technol. Vol.58, pp. 236-246 10.1016/j.tust.2016.05.009
12
Vapnik, V.N., 1995, The Nature of Statistical Learning Theory, Springer, New York, pp. 119-166. 10.1007/978-1-4757-2440-0_6
13
Yagiz, S., Gokceoglu, C., Sezer, E., Iplikci, S., 2009, Application of two non-linear prediction tools to the estimation of tunnel boring machine performance, Eng. Appl.Artif. Intell. Vol.22 (4-5), pp. 808-814. 10.1016/j.engappai.2009.03.007
14
Yagiz, S., Karahan, H., 2011, Prediction of hard rock TBM penetration rate using particle swarm optimization, Int. J. Rock Mech. Min. Sci. Vol.48, No.3, pp. 427-433. 10.1016/j.ijrmms.2011.02.013
Information
  • Publisher :Korean Society for Rock Mechanics and Rock Engineering
  • Publisher(Ko) :한국암반공학회
  • Journal Title :Tunnel and Underground Space
  • Journal Title(Ko) :터널과 지하공간
  • Volume : 30
  • No :6
  • Pages :540-550
  • Received Date :2020. 11. 26
  • Revised Date :2020. 11. 30
  • Accepted Date : 2020. 11. 30