Mining of Mineral Deposits

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Prediction of the number of consumed disc cutters of tunnel boring machine using intelligent methods

Alireza Afradi1, Arash Ebrahimabadi2, Tahereh Hallajian1

1Department of Mining and Geology, Qaemshahr Branch, Islamic Azad University, Qaemshahr, 4765161964, Iran

2Department of Petroleum, Mining and Material Engineering, Central Tehran Branch, Islamic Azad University, Tehran, 13117773591, Iran


Min. miner. depos. 2021, 15(4):68-74


https://doi.org/10.33271/mining15.04.068

Full text (PDF)


      ABSTRACT

      Purpose. Disc cutters are the main cutting tools for the Tunnel Boring Machines (TBMs). Prediction of the number of consumed disc cutters of TBMs is one of the most significant factors in the tunneling projects. Choosing the right model for predicting the number of consumed disc cutters in mechanized tunneling projects has been the most important mechanized tunneling topics in recent years.

      Methods. In this research, the prediction of the number of consumed disc cutters considering machine and ground conditions such as Power (KW), Revolutions per minute (RPM) (Cycle/Min), Thrust per Cutter (KN), Geological Strength Index (GSI) in the Sabzkooh water conveyance tunnel has been conducted by multiple linear regression analysis and multiple nonlinear regression, Gene Expression Programming (GEP) method and Support Vector Machine (SVM) approaches.

      Findings. Results showed that the number of consumed disc cutters for linear regression method is R2 = 0.95 and RMSE = 0.83, nonlinear regression method is – R2 = 0.95 and RMSE = 0.84, Gene Expression Programming (GEP) method is – R2 = 0.94 and RMSE = 0.95, Support Vector Machine (SVM) method is – R2 = 0.98 and RMSE = 0.45.

      Originality. During the analyses, in order to evaluate the accuracy and efficiency of predictive models, the coefficient of determination (R2) and root mean square error (RMSE) have been used.

      Practical implications. Results demonstrated that all four methods are effective and have high accuracy but the method of support vector machine has a special superiority over other methods.

      Keywords: regression, gene expression programming, support vector machine, Sabzkooh water conveyance tunnel


      REFERENCES

  1. Akiyama, T. (1970). A theory of the rock-breaking function of the disc cutter. Komatsu Technology, 16(3), 56-61.
  2. Evans, I. (1974). Relative efficiency of picks and discs for cutting rock. Proceedings of 3rd Congress on Advances in Rock Mechanics, 1399-1406.
  3. Snowdon, R., Ryley, M., & Temporal, J. (1982). A study of disc cutting in selected British rocks. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 19(3), 107-121. https://doi.org/10.1016/0148-9062(82)91151-2
  4. Sanio, H. (1985). Prediction of the performance of disc cutters in anisotropic rock. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 22(3), 153-161. https://doi.org/10.1016/0148-9062(85)93229-2
  5. Rostami, J. (1997). Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of the crushed zone pressure. Doctorate Dissertation. Golden, United States: Colorado School of Mines.
  6. Frenzel, C., Sling, H.K., & Thuro, K. (2008). Factors influencing disc cutter wear. Geomechanics and Tunnelling, 1(1), 55-60. https://doi.org/10.1002/geot.200800006
  7. Roby, J., Sandell, T., Kocab, J., & Lindbergh, L. (2008). The current state of disc cutter design and development directions. Proceedings of 2008 North American Tunneling Conference.
  8. Lin, Q., Cao, P., Li, K., Cao, R., Zhou, K., & Deng, H. (2018). Experimental study on acoustic emission characteristics of jointed rock mass by double disc cutter. Journal of Central South University, 25(2), 357-367. https://doi.org/10.1007/s11771-018-3742-7
  9. Ren, D.-J., Shen, S.-L., Arulrajah, A., & Cheng, W.-C. (2018). Prediction model of tbm disc cutter wear during tunnelling in heterogeneous ground. Rock Mechanics and Rock Engineering, 51(11), 3599-3611. https://doi.org/10.1007/s00603-018-1549-3
  10. Peng, X., Liu, Q., Pan, Y., Lei, G., Wei, L., & Luo, C. (2017). Study on the influence of different control modes on TBM disc cutter performance by rotary cutting tests. Rock Mechanics and Rock Engineering, 51(3), 961-967. https://doi.org/10.1007/s00603-017-1368-y
  11. Pan, Y., Liu, Q., Liu, J., Peng, X., & Kong, X. (2018). Full-scale linear cutting tests in chongqing sandstone to study the influence of confining stress on rock cutting forces by TBM disc cutter. Rock Mechanics and Rock Engineering, 51(6), 1697-1713. https://doi.org/10.1007/s00603-018-1412-6
  12. 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, 22(4), 1466-1472. https://doi.org/10.1007/s12205-017-1048-1
  13. Zhang, X., Xia, Y., Tan, Q., & Wu, D. (2018). Comparison study on the rock cutting characteristics of disc cutter under free-face-assisted and conventional cutting methods. KSCE Journal of Civil Engineering, 22(10), 4155-4162. https://doi.org/10.1007/s12205-018-0577-6
  14. Xia, Y., Lin, L., Wu, D., Jia, L., Chen, Z., & Bian, Z. (2018). Geological adaptability matching design of disc cutter using multicriteria decision making approaches. Journal of Central South University, 25(4), 843-854. https://doi.org/10.1007/s11771-018-3788-6
  15. Talary, G.A., Rahimi, E., & Abbasi, M. (2016). Seismotectonics and earthquake risk analysis in the area of Sabzkooh-Choqakhor water conveyance tunnel. Thirty-Fourth Gathering and Second International Congress of Geosciences.
  16. Yoon, G.L., Kim, B.T., & Jeon, S.S. (2004). Empirical correlations of compression index for marine clay from regression analysis. Canadian Geotechnical Journal, 41(6), 1213-1221. https://doi.org/10.1139/t04-057
  17. Wang, S., Zheng, L., & Dai, J. (2014). Empirical likelihood diagnosis of modal linear regression models. Journal of Applied Mathematics and Physics, 2(10), 948-952. https://doi.org/10.4236/jamp.2014.210107
  18. Quirós, E., Felicísimo, A.M., & Cuartero, A. (2009). Testing multivariate adaptive regression splines (MARS) as a method of land cover classification of TERRA-ASTER satellite images. Sensors (Basel, Switzerland), 9(11), 9011-9028. https://doi.org/10.3390/s91109011
  19. Bates, D.M., & Watts, D.G. (1988). Nonlinear regression analysis and its applications. New York, United States: John Wiley & Sons. https://doi.org/10.1002/9780470316757
  20. Huang, C., & Townshend, J.R.G. (2003). A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover. International Journal of Remote Sensing, 24(1), 75-90. https://doi.org/10.1080/01431160305001
  21. Brimacombe, M. (2016). Local curvature and centering effects in nonlinear regression models. Open Journal of Statistics, 6(1), 76-84. https://doi.org/10.4236/ojs.2016.61010
  22. Fiore, A., Quaranta, G., Marano, G., & Monti, G. (2016). Evolutionary polynomial regression-based statistical determination of the shear capacity equation for reinforced concrete beams without stirrups. Journal of Computing in Civil Engineering, 30(1), 04014111. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000450
  23. Giustolisi, O., & Savic, D.A. (2006). A symbolic data-driven technique based on evolutionary polynomial regression. Journal of Hydroinformatics, 8(3), 207-222. https://doi.org/10.2166/hydro.2006.020b
  24. Ferreira, C. (2001). Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems, 13(2), 87-129.
  25. Tsai, H.C. (2011). Using weighted genetic programming to program squat wall strengths and tune associated formulas. Engineering Applications of Artificial Intelligence, 24(3), 526-533. https://doi.org/10.1016/j.engappai.2010.08.010
  26. Azamathulla, H.M. (2013). Gene-expression programming to predict friction factor for Southern Italian Rivers. Neural Computing and Applications, 23(5), 1421-1426. https://doi.org/10.1007/s00521-012-1091-2
  27. Roushangar, K., Mouaze, D., & Shiri, J. (2014). Evaluation of genetic programming-based models for simulating friction factor in alluvial channels. Journal of Hydrology, (517), 1154-1161. https://doi.org/10.1016/j.jhydrol.2014.06.047
  28. Gandomi, A.H., & Roke, D.A. (2015). Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software, (88), 63-72, https://doi.org/10.1016/j.advengsoft.2015.05.007
  29. Azamathulla, H.M., Ghani, A.A., Chang, C.K., Hasan, Z.A., & Zakaria, N.A. (2010). Machine learning approach to predict sediment load-a case study. CLEAN Soil Air Water, 38(10), 969-976, https://doi.org/10.1002/clen.201000068
  30. Shirzad, A., Tabesh, M., & Farmani, R. (2014). A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks. KSCE Journal of Civil Engineering, 18(4), 941-948. https://doi.org/10.1007/s12205-014-0537-8
  31. Wang, W.C., Xu, D.M., Chau, K.W., & Chen, S. (2013). Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD. Journal of Hydroinformatics, 15(4), 1377-1390. https://doi.org/10.2166/hydro.2013.134
  32. Chiang, J., & Tsai, Y. (2011). Suspended sediment load estimate using support vector machines in Kaoping River Basin. In Consumer Electronics, Communications and Networks (CECNet), International Conference, 1750-1753. https://doi.org/10.1109/CECNET.2011.5769267
  33. Jajarmizadeh, M., Kakaei Lafdani, E., Harun, S., & Ahmadi, A. (2014). Application of SVM and SWAT models for monthly streamflow prediction, a case study in South of Iran. KSCE Journal of Civil Engineering, 19(1), 345-357. https://doi.org/10.1007/s12205-014-0060-y
  34. Kakaei Lafdani, E., Moghaddam Nia, A., & Ahmadi, A. (2013). Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology, (478), 50-62. https://doi.org/10.1016/j.jhydrol.2012.11.048
  35. Roushangar, K., & Koosheh, A. (2015). Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers. Journal of Hydrology, (527), 1142-1152. https://doi.org/10.1016/j.jhydrol.2015.06.006
  36. Afradi, A., Ebrahimabadi, A., & Hallajian, T. (2020). Prediction of tunnel boring machine penetration rate using ant colony optimization, bee colony optimization and the particle swarm optimization, case study: Sabzkooh water conveyance tunnel. Mining of Mineral Deposits, 14(2), 75-84. https://doi.org/10.33271/mining14.02.075

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