Neural Computing and Applications

, Volume 24, Issue 7–8, pp 1823–1831 | Cite as

Predicting roadheader performance by using artificial neural network

  • Armin Salsani
  • Jahanbakhsh Daneshian
  • Shahram Shariati
  • Abdolreza Yazdani-Chamzini
  • Mehdi Taheri
Original Article

Abstract

With growing use of roadheaders in the world and its significant role in the successful accomplishment of a tunneling project, it is a necessity to accurately predict performance of this machine in different ground conditions. On the other hand, the existence of some shortcomings in the prediction models has made it necessary to perform more research on the development of the new models. This paper makes an attempt to model the rate of roadheader performance based on the geotechnical and geological site conditions. For achieving the aim, an artificial neural network (ANN), a powerful tool for modeling and recognizing the sophisticated structures involved in data, is employed to model the relationship between the roadheader performance and the parameters influencing the tunneling operations with a high correlation. The database used in modeling is compiled from laboratory studies conducted at Azad University at Science and Research Branch, Tehran, Iran. A model with architecture 4-10-1 trained by back-propagation algorithm is found to be optimum. A multiple variable regression (MVR) analysis is also applied to compare performance of the neural network. The results demonstrate that predictive capability of the ANN model is better than that of the MVR model. It is concluded that roadheader performance could be accurately predicted as a function of unconfined compressive strength, Brazilian tensile strength, rock quality designation, and alpha angle R2 = 0.987. Sensitivity analysis reveals that the most effective parameter on roadheader performance is the unconfined compressive strength.

Keywords

Roadheader performance Artificial neural network Multiple variable regression Tunneling 

References

  1. 1.
    Jordan EW, Holben T, Kim J, Restner U, Hallett G (2011) Roadheader Excavations in Hard Rock—a case history from Midtown Manhattan. 2011 Rapid Excavation and Tunneling Conference Proceedings, pp. 765–795Google Scholar
  2. 2.
    Ergin H, Acaroglu O (2007) The effect of machine design parameters on the stability of a roadheader. Tunn Undergr Space Technol 22:80–89CrossRefGoogle Scholar
  3. 3.
    Tatiya RR (2005) Surface and underground excavations—methods, techniques and equipment. Taylor & Francis Group, LLC, LondonCrossRefGoogle Scholar
  4. 4.
    Gehring KH (1989) A cutting comparison. Tunn Tunn, pp. 27–30Google Scholar
  5. 5.
    Bilgin N, Seyrek T, Erdinc E, Shahriar K (1990) Roadheaders clean valuable tips for Istanbul Metro. Tunn Tunn (October), pp. 29–32Google Scholar
  6. 6.
    Fowell RJ, Johnson ST (1991) Cuttability assessment applied to drag tool tunneling machines. In: Proceeding of the 7th International Congress on Rock Mechanics, ISRM, Aachen, p. 985Google Scholar
  7. 7.
    Rostami J, Ozdemir L (1996) Modeling for design and performance analysis of mechanical excavators. In: Proceedings of the Conference on Mechanical Excavation’s Future Role in Mining, World Rock Boring Association, Sudbury, ON, Canada, September, pp. 17–19Google Scholar
  8. 8.
    Copur H, Ozdemir L, Rostami J (1998) Roadheader applications in mining and tunneling. Min Eng 50:38–42Google Scholar
  9. 9.
    Thuro K, Plinninger RJ (1999) Predicting roadheader advance rates. Tunn Tunn 31:36–39Google Scholar
  10. 10.
    Bilgin N, Demircin MA, Copur H, Balci C, Tuncdemir H, Akcin N (2006) Dominant rock properties affecting the performance of conical picks and the comparison of some experimental and theoretical results. Int J Rock Mech Min Sci 43(1):139–156CrossRefGoogle Scholar
  11. 11.
    Goshtasbi K, Monjezi M, Tourgoli P (2009) Evaluation of boring machine performance with special reference to geomechanical characteristics. Int J Min Met Mater 16(6):615–619Google Scholar
  12. 12.
    Ebrahimabadi A, Goshtasbi K, Shahriar K, Seifabad MCh (2011) A model to predict the performance of roadheaders based on the Rock Mass Brittleness Index. J S Afr I Min Metall 111:355–364Google Scholar
  13. 13.
    Ebrahimabadi A, Goshtasbi K, Shahriar K, Seifabad MCh (2011) Predictive models for roadheaders’ cutting performance in coal measure rocks. Yerbilimleri 32(2):89–104Google Scholar
  14. 14.
    Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees. Eng Geol 99(1–2):51–60CrossRefGoogle Scholar
  15. 15.
    Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min 46(7):1214–1222CrossRefGoogle Scholar
  16. 16.
    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 Intel 22(4–5):808–814CrossRefGoogle Scholar
  17. 17.
    Dehghan S, Sattari Gh, Chelgani SC, Aliabadi MA (2010) Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Tech (China) 20(1):41–46CrossRefGoogle Scholar
  18. 18.
    Gholamnejad J, Tayarani N (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Tech (China) 20(5):727–733CrossRefGoogle Scholar
  19. 19.
    Bahrami A, Monjezi M, Goshtasbi K, Ghazvinian A (2011) Prediction of rock fragmentation due to blasting using artificial neural network. Eng Comput 27:177–181CrossRefGoogle Scholar
  20. 20.
    Monjezi M, Bahrami A, Varjani AY, Sayadi AR (2011) Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4:421–425CrossRefGoogle Scholar
  21. 21.
    Manouchehrian A, Sharifzadeh M, Moghadam RH (2012) Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Min Sci Tech 22(2):229–236CrossRefGoogle Scholar
  22. 22.
    Monjezi M, Hasanipanah M, Khandelwal M (2012) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl. doi:10.1007/s00521-012-0856-y
  23. 23.
    Mahdevari S, Torabi SR (2012) Prediction of tunnel convergence using artificial neural networks. Tunn Undergr Space Technol 28:218–228CrossRefGoogle Scholar
  24. 24.
    Monjezi M, Ghafurikalajahi M, Bahrami A (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunn Undergr Space Technol 26:46–50CrossRefGoogle Scholar
  25. 25.
    Sumathi S, Paneerselvam S (2010) Computational intelligence paradigms theory and applications using MATLAB. Taylor and Francis Group, LLC, LondonGoogle Scholar
  26. 26.
    RM IS (1978) Suggested method for determining sound velocity. Int J Rock Mech Mining Sci Geomech Abst 15:A100Google Scholar
  27. 27.
    Yazdani-Chamzini A, Hashemi-Rizi SM, Javadi M, Saeedi A, Basiri MH (2011) Predicting the penetration rate of tunnel boring machine; the comparison between the output of multivariate linear regression, neural network, and neurofuzzy systems. The first Asian and 9th Iranian tunneling symposium, Tehran-Iran, pp. 1–7Google Scholar
  28. 28.
    Simpson PK (1990) Artificial neural system—foundation, paradigm, application and implementations. Pergamon Press, New YorkGoogle Scholar
  29. 29.
    Kosko B (1994) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice Hall, New Delhi, pp 12–17Google Scholar
  30. 30.
    Lashgari A, Fouladgar MM, Yazdani-Chamzini A, Skibniewski MJ (2011) Using an integrated model for shaft sinking method selection. J Civ Eng Manag 17(4):569–580CrossRefGoogle Scholar
  31. 31.
    Central Mine Design Report (CMDR) (2005) ADAM consulting engineersGoogle Scholar
  32. 32.
    Ghasemi E, Shahriar K (2012) A new coal pillars design method in order to enhance safety of the retreat mining in room and pillar mines. Safety Sci 50:579–585CrossRefGoogle Scholar
  33. 33.
    Ebrahimabadi A (2010) A model to predict the performance of roadheaders in tunneling. Ph.D. dissertation, Azad University, Science and Research Branch, Tehran, IranGoogle Scholar
  34. 34.
    Tumac D, Bilgin N, Feridunoglu C, Ergin H (2007) Estimation of Rock Cuttability from shore hardness and compressive strength properties. Rock Mech Rock Eng 40(5):477–490CrossRefGoogle Scholar
  35. 35.
    Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23:326–339CrossRefGoogle Scholar
  36. 36.
    en.wikipedia.orgGoogle Scholar
  37. 37.
    Siegel JG, Shim JK (2006) Accounting handbook (fourth edition). Barron’s educational series, Inc.Google Scholar
  38. 38.
    Meulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39CrossRefGoogle Scholar
  39. 39.
    Kulatilake PHSW, Qiong Wu, Hudaverdi T, Kuzu C (2010) Mean particle size prediction in rock blast fragmentation using neural net-works. Eng Geol 114:298–311CrossRefGoogle Scholar
  40. 40.
    Singh TN, Singh V (2005) An intelligent approach to prediction and control ground vibration in mines. Geotech Geol Eng 23:249–262CrossRefGoogle Scholar
  41. 41.
    Celikyilmaz A, Türksen IB (2009) Modeling uncertainty with fuzzy logic; with recent theory and applications. Springer, Berlin HeidelbergCrossRefMATHGoogle Scholar
  42. 42.
    Ross TJ (2010) Fuzzy logic with engineering applications (Third Edition). Wiley, HobokenCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Armin Salsani
    • 1
  • Jahanbakhsh Daneshian
    • 1
  • Shahram Shariati
    • 2
  • Abdolreza Yazdani-Chamzini
    • 3
  • Mehdi Taheri
    • 4
  1. 1.Department of GeologyFaculty of Science, University of KharazmiTehranIran
  2. 2.Department of GeologyFaculty of Science, University of SariTehranIran
  3. 3.Department of Mining EngineeringFaculty of Engineering, Tarbiat Modares UniversityTehranIran
  4. 4.Department of Mining EngineeringFaculty of Engineering, University of South TehranTehranIran

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