Neural Computing and Applications

, Volume 31, Issue 4, pp 1103–1116 | Cite as

Performance prediction of roadheaders using ensemble machine learning techniques

  • Sadi Evren Seker
  • Ibrahim OcakEmail author
Original Article


Mechanical excavators are widely used in mining, tunneling and civil engineering projects. There are several types of mechanical excavators, such as a roadheader, tunnel boring machine and impact hammer. This is because these tools can bring productivity to the project quickly, accurately and safely. Among these, roadheaders have some advantages like selective mining, mobility, less over excavation, minimal ground disturbances, elimination of blast vibration, reduced ventilation requirements and initial investment cost. A critical issue in successful roadheader application is the ability to evaluate and predict the machine performance named instantaneous (net) cutting rate. Although there are several prediction methods in the literature, for the prediction of roadheader performance, only a few of them have been developed via artificial neural network techniques. In this study, for this purpose, 333 data sets including uniaxial compressive strength and power on cutting boom, 103 data set including RQD, and 125 data sets including machine weight are accumulated from the literature. This paper focuses on roadheader performance prediction using six different machine learning algorithms and a combination of various machine learning algorithms via ensemble techniques. Algorithms are ZeroR, random forest (RF), Gaussian process, linear regression, logistic regression and multi-layer perceptron (MLP). As a result, MLP and RF give better results than the other algorithms also the best solution achieved was bagging technique on RF and principle component analysis (PCA). The best success rate obtained in this study is 90.2% successful prediction, and it is relatively better than contemporary research.


Roadheader Performance prediction Instantaneous cutting rate Machine learning Data mining Ensemble 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Rostami J, Ozdemir L, Neil DM (1994) Performance prediction: a key issue in mechanical hard rock mining. Min Eng 46(11):1264–1267Google Scholar
  2. 2.
    Avunduk E, Tumac D, Atalay AK (2014) Prediction of roadheader performance by artificial neural network. Tunn Undergr Space Technol 44:3–9Google Scholar
  3. 3.
    Farmer I, Garrity P (1987) Prediction of roadheader cutting performance from fracture toughness considerations. In: Proceedings of the 6th international congress on rock mechanics, vol 1, Montreal, Canada, pp 621–624Google Scholar
  4. 4.
    Poole D (1987) The effectiveness of tunnelling machines. Tunn Tunn 19:66–67Google Scholar
  5. 5.
    Gehring KH (1989) A cutting comparison. Tunn Tunn 21:27–30Google Scholar
  6. 6.
    Ocak I, Bilgin N (2010) Comparative studies on the performance of a roadheader, impact hammer and drilling and blasting method in the excavation of metro station tunnels in Istanbul. Tunn Undergr Space Technol 25(2):181–187Google Scholar
  7. 7.
  8. 8.
    Bilgin N, Seyre T, Shahriar K (1988) Roadheader performance in İstanbul, Golden Horn clean-up contributes valuable data. Tunn Tunn 20:41–44Google Scholar
  9. 9.
    Bilgin N, Seyrek T, Erdinc E, Shahriar K (1990) Roadheaders glean valuable tips for Istanbul Metro. Tunn Tunn 22(10):29–32Google Scholar
  10. 10.
    Bilgin N, Dincer T, Copur H, Erdogan M (2004) Some geological and geotechnical factors affecting the performance of a roadheader in an inclined tunnel. Tunn Undergr Space Technol 19(6):629–636Google Scholar
  11. 11.
    Copur H, Rostami J, Ozdemir L, Bilgin N (1997) Studies on performance prediction of roadheaders. In: Proceedings of the 4th international symposium on mine mechanization and automation, Brisbane, QLD, Australia, A4-1–A4-7Google Scholar
  12. 12.
    Copur H, Ozdemir L, Rostami J (1998) Roadheader applications in mining and tunneling. Min Eng 50:38–42Google Scholar
  13. 13.
    Thuro K, Plinninger RJ (1999) Predicting roadheader advance rates. Tunn Tunn 31:36–39Google Scholar
  14. 14.
    Balci C, Demircin MA, Copur H, Tuncdemir H (2004) Estimation of optimum specific energy based on rock properties for assessment of roadheader performance. J S Afr Inst Min Metall 104(11):633–642Google Scholar
  15. 15.
    Ebrahimabadi A, Goshtasbi K, Shahriar K, Seifabad CM (2011) A model to predict the performance of roadheaders based on the rock mass brittleness index. J S Afr Inst Min Metall 111:355–364Google Scholar
  16. 16.
    Ebrahimabadi A, Goshtasbi K, Shahriar K, Seifabad CM (2012) A universal model to predict roadheaders’ cutting performance. Arch Min Sci 57(4):1015–1026Google Scholar
  17. 17.
    Yang Y, Zhang Q (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30(4):207–222Google Scholar
  18. 18.
    Shahin M, Jaksa M, Maier H (2001) Artificial neural networks application in geotechnical engineering. Aust Geomech 36(1):49–62Google Scholar
  19. 19.
    Singh VK, Singh D, Singh PK (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38(2):269–284Google Scholar
  20. 20.
    Khandelwal M, Roy MP, Singh PK (2004) Application of artificial neural network in mining industry. Ind Min Eng J 43:19–23Google Scholar
  21. 21.
    Sonmez H, Gokceoglu C, Kayabas A, Nefeslioglu HA (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43(2):224–235Google Scholar
  22. 22.
    Yilmaz I, Yuksek AG (2008) An example of artificial neural network application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795Google Scholar
  23. 23.
    Dehghan S, Sattari G, Chehreh S, Aliabadi M (2010) Prediction of uniaxial compressive strength and modulus of elasticity for travertine samples using regression and artificial neural networks. Min Sci Technol (China) 20(1):41–46Google Scholar
  24. 24.
    Ocak I, Seker SE (2012) Estimation of elastic modulus of intact rocks by artificial neural network. Rock Mech Rock Eng 45(6):1047–1054Google Scholar
  25. 25.
    Enayatollahi I, Bazzazi AA, Asadil A (2013) Comparison between neural networks and multiple regression analysis to predict rock fragmentation in open-pit mines. Rock Mech Rock Eng 47(2):799–807Google Scholar
  26. 26.
    Grima MA, Brunies PA, Verhoef PNW (2000) Modeling tunnel boring machine by neuro-fuzzy methods. Tunn Undergr Space Technol 15(3):259–269Google Scholar
  27. 27.
    Benardos AG, Kaliampakos DC (2004) Modelling TBM performance with artificial neural networks. Tunn Undergr Space Technol 19(6):597–605Google Scholar
  28. 28.
    Kahraman S, Altun H, Tezekici BS, Fener M (2006) Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks. Int J Rock Mech Min Sci 43(1):157–164Google Scholar
  29. 29.
    Javad G, Narges T (2010) Application of artificial neural networks to the prediction of tunnel boring machine penetration rate. Min Sci Technol 20(5):727–733Google Scholar
  30. 30.
    Ovidio J, Santos JR, Tarcisio BC (2008) Artificial neural networks analysis of Sao Paulo subway tunnel settlement data. Tunn Undergr Space Technol 23:481–491Google Scholar
  31. 31.
    Shi J, Ortigao JAR, Bai JJ (1998) Modular neural networks for predicting settlements during tunneling. J Getech Geoenviron Eng 124(05):389–395Google Scholar
  32. 32.
    Suwansawat S (2002) Earth pressure balance (EPB) shield tunneling in Bangkok: ground response and prediction of surface settlements using artificial neural networks. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MAGoogle Scholar
  33. 33.
    Suwansawat S, Einstein HH (2006) Artificial neural networks for predicting the maximum surface settlement caused by EPB shield tunneling. Tunn Undergr Space Technol 21(2):133–150Google Scholar
  34. 34.
    Nellessen P (2007) Using neurofuzzy systems to predict settlements for slurry shield drives based on an evaluation of the process data synchronous to the advance. In: EURO: TUN thematic conference on computational methods in tunnelling, Vienna, AustriaGoogle Scholar
  35. 35.
    Ocak I, Seker SE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 70(3):1263–1276Google Scholar
  36. 36.
    Xu J, Xu Y (2011) Grey correlation-hierarchical analysis for metro-caused settlement. Environ Earth Sci 64(5):1246–1256Google Scholar
  37. 37.
    Ebrahimabadi A, Azimipour M, Bahreini A (2015) Prediction of roadheaders’ performance using artificial neural network approaches (MLP and KOSFM). J Rock Mech Geotech Eng 7:573–583Google Scholar
  38. 38.
    Salsani A, Daneshian J, Shariati S, Chamzini AY, Taheri M (2014) Predicting roadheader performance by using artificial neural network. Neural Comput Appl 24:1823–1831Google Scholar
  39. 39.
    Shahriar K (1988) Investigation of effects of rocks excavability and geotechnical properties on roadheaders’ excavation rate, ITU Institute of Science and Technology Ph.D. thesis, 241 pagesGoogle Scholar
  40. 40.
    Bilgin N, Yazıcı S, Eskikaya Ş (1996) A model to predict the performance of roadheaders and impact hammers in tunnel drivages. In: Proceeding of “Eurock’96 ISRM international symposium, Torino, BalkemaGoogle Scholar
  41. 41.
    Westfalia Becorit Industrietechnik GmbH (1993) Lüttich E5/E9 ProjectGoogle Scholar
  42. 42.
    Fowell RJ, Richardson G, Gollick MJ (1994) Prediction of boom tunnelling machine excavation rates. In: Proceedings of the 1st North American rock mechanics symposium, Balkema, pp 243–250Google Scholar
  43. 43.
    Dunn PG, Howarth DF, Schmidt SPJ, Bryan IJ (1997) A review of non-explosive excavation projects for the Australian metalliferous mining industry, pp 1–13Google Scholar
  44. 44.
    Schneider H (1998) Criteria for selecting a boom-type roadheader. Min Mag 26:183–188Google Scholar
  45. 45.
    Bauer R (2002) Hard rock roadheaders. In: Colorado school of mines short course notes (unpublished) Google Scholar
  46. 46.
    Tumac D (2014) The investigation into Roadheader performance prediction in Kuçuksu sewage tunnel, ITU Institute of Science and Technology M.Sc. thesisGoogle Scholar
  47. 47.
    Keles S (2005) Cutting performance assessment of a medium weight roadheader at Cayirhan coal mine, masters’ thesis of the graduate school of natural and applied sciences of middle east technical universityGoogle Scholar
  48. 48.
    Ocak I, Eyigun Y, Cinar M, Nahya T (2007) Investigation into roadheader excavation performance and pick consumption used in Kadkoy-Kartal metro tunnels. In: Proceedings of the 2nd symposium on underground excavations for transportation, Istanbul, pp 199–206 (in Turkish)Google Scholar
  49. 49.
    Comakli R, Kahraman S, Balci C (2014) Performance prediction of roadheaders in metallic ore excavation. Tunn Undergr Space Technol 40:38–45Google Scholar
  50. 50.
    Hemphill GB (2012) Practical tunnel construction. Wiley, Hoboken. ISBN: 978-1-118-33000-5Google Scholar
  51. 51.
    ISRM (1981) Rock characterization testing and monitoring, suggested methods. Pergamon Press, OxfordGoogle Scholar
  52. 52.
    Deere DU (1964) Technical description of rock cores for engineering purposes. Rock Mech Eng Geol 1:17–22Google Scholar
  53. 53.
    Keerthi SS, Bhattacharyya C, Murthy KRK, Shevade SK (1999) Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 11(5):1188–1193Google Scholar
  54. 54.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32zbMATHGoogle Scholar
  55. 55.
    Breiman L (1996) Stacked regressions. Mach Learn 24(1):49–84zbMATHGoogle Scholar
  56. 56.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140zbMATHGoogle Scholar
  57. 57.
    Ho TK (1995) Random decision forests. In: Proceedings of the 3rd international conference on document analysis and recognition, pp 278–282Google Scholar
  58. 58.
    Amit Y, Geman D (1997) Shape quantization and recognition with randomized trees. Neural Comput 9(7):1545–1588Google Scholar
  59. 59.
    Xu M, Watanachaturaporn P, Varshney PK, Arora MK (2005) Decision tree regression for soft classification of remote sensing data. Remote Sens Environ 97(3):322–336Google Scholar
  60. 60.
    Zhi-Hua Z (2012) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC, Boca RatonGoogle Scholar
  61. 61.
    Uestuen B, Melssen W, Buydens L (2006) Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometr Intell Lab Syst 81:29–40Google Scholar
  62. 62.
    Frank E, Wang Y, Inglis S, Holmes G, Witten IH (1998) Using model trees for classification. Mach Learn 32(1):63–76zbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Smith CollegeNorthamptonUSA
  2. 2.IstanbulTurkey

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