Skip to main content
Log in

Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

Diamond drilling has been widely used in the different civil engineering projects. The prediction of penetration rate in the drilling is especially useful for the feasibility studies. In this study, the predictability of penetration rate for the diamond drilling was investigated from the operational variables and the rock properties such as the uniaxial compressive strength, the tensile strength and the relative abrasiveness. Both the multiple regression and the artificial neural networks (ANN) analysis were used in the study. Very good models were derived from ANN analysis for the prediction of penetration rate. The comparison of ANN models with the regression models indicated that ANN models were much more reliable than the regression models. It is concluded that the penetration rate for the diamond drilling can be reliably estimated from the uniaxial compressive strength, the tensile strength and the relative abrasiveness using the ANN models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Paone J, Madson D (1966) Drillability studies-impregnated diamond bits. US Bureau of Mines RI 6776

  2. Paone J, Madson D, Bruce WE (1969) Drillability studies—laboratory percussive drilling. US Bureau of Mines RI 7300

  3. Howarth DF, Adamson WR, Brendt JR (1986) Correlation of model tunnel boring and drilling machine performances with rock properties. Int J Rock Mech Min Sci 23:171–175

    Article  Google Scholar 

  4. Ersoy A, Waller MD (1995) Prediciton of drill-bit performance using multivariable linear regression analysis. Trans Inst Min Metall (Section A: Min. Industry) 104:A101–A114

    Google Scholar 

  5. Kahraman S (2002) Correlation of TBM and drilling machine performances with rock brittleness. Eng Geol 65:269–283

    Article  Google Scholar 

  6. Kahraman S (2003) Performance analysis of drilling machines using rock modulus ratio. J S Afr Inst Min Metall 103:515–522

    Google Scholar 

  7. Akün ME, Karpuz C (2005) Drillability studies of surface-set diamond drilling in onguldak region sandstones from Turkey. Int J Rock Mech Min Sci 42:473–479

    Article  Google Scholar 

  8. Yuanyou X, Yanming X, Ruigeng Z (1997) An engineering geology evaluation method based on an artificial neural network and its application. Eng Geol 47:149–156

    Article  Google Scholar 

  9. Yang Y, Zhang Q (1998) The applications of neural networks to rock engineering systems (RES). Int J Rock Mech Min Sci 35(6):727–745

    Article  Google Scholar 

  10. Singh VK, Singh D, Singh TN (2001) Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38:269–284

    Article  Google Scholar 

  11. Kahraman S, Altun H, Tezekici BS, Fener M (2005) Sawability prediction of carbonate rocks from shear strength parameters using artificial neural networks. Int J Rock Mech Min Sci 43(1):157–164

    Article  Google Scholar 

  12. Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical function. Int J Rock Mech Min Sci 43(2):224–235

    Article  Google Scholar 

  13. Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141–158

    Article  Google Scholar 

  14. Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis fault breccia using artificial neural networks. Expert Syst Appl 36:6874–6878

    Article  Google Scholar 

  15. Kahraman S, Alber M, Fener M, Gunaydin O (2010) The usability of Cerchar abrasivity index for the prediction of UCS and E of Misis fault breccia: regression and artificial neural networks analysis. Expert Syst Appl 37:8750–8756

    Article  Google Scholar 

  16. Akin S, Karpuz C (2008) Estimating drilling paramters for diamond bit drilling operations using artificial neural networks. Int J Geomech 8(1):68–73

    Article  Google Scholar 

  17. Bhatnagar A, Khandelwal M (2012) An intelligent approach to evaluate drilling performance. Neural Comput Appl 21:763–770

    Article  Google Scholar 

  18. Clark GB (1979) Principles of rock drilling. Colorado Sch Min Q 74:1–91

    Google Scholar 

  19. Kumar UA (2005) Comparison of neural networks and regression analysis: a new insight. Expert Syst Appl 29:424–430

    Article  Google Scholar 

  20. Altun H, Bilgil A, Fidan BC (2007) Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models. Expert Syst Appl 33:978–983

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Kahraman.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kahraman, S. Estimating the Penetration Rate in Diamond Drilling in Laboratory Works Using the Regression and Artificial Neural Network Analysis. Neural Process Lett 43, 523–535 (2016). https://doi.org/10.1007/s11063-015-9424-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-015-9424-7

Keywords

Navigation