Skip to main content
SpringerLink
Log in

Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique

  • Original paper
  • Published:
Geotechnical and Geological Engineering Aims and scope Submit manuscript

Abstract

The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.

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

Access this article

Log in via an institution

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Adebayo B, Opafunso ZO, Akande JM (2010) Drillability and strength characteristics of selected rocks in Nigeria. AU J Technol 14(1):56–60

    Google Scholar 

  • Aghajanloo MB, Sabziparvar AA, Talaee PH (2013) Artificial neural network–genetic algorithm for estimation of crop evapotranspiration in a semi-arid region of Iran. Neural Comput Appl 23(5):1387–1393

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Arabjamaloei R, Karimi Dehkordi B (2012) Investigation of the most efficient approach of the prediction of the rate of penetration. Energy Sour A Recov Util Environ Eff 34(7):581–590

    Article  Google Scholar 

  • Armaghani DJ, Momeni E, Alavi Nezhad Khalil Abad SV, Khandelwal M (2015a) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. DOI, Environ Earth Sci. doi:10.1007/s12665-015-4305-y

    Google Scholar 

  • Armaghani DJ, Tonnizam Mohamad E, Hajihassani M, Alavi Nezhad Khalil Abad SV, Marto A, Moghaddam MR (2015b) Evaluation and prediction of flyrock resulting from blasting operations using empirical and computational methods. Eng Comput. doi:10.1007/s00366-015-0402-5

    Google Scholar 

  • Armaghani DJ, Hajihassani M, Sohaei H, Mohamad ET, Marto A, Motaghedi H, Moghaddam MR (2015c) Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arab J Geosci 1–14

  • Ataei M, KaKaie R, Ghavidel M, Saeidi O (2015) Drilling rate prediction of an open pit mine using the rock mass drillability index. Int J Rock Mech Min Sci 73:130–138

    Google Scholar 

  • Basarir H, Tutluoglu L, Karpuz C (2014) Penetration rate prediction for diamond bit drilling by adaptive neuro-fuzzy inference system and multiple regressions. Eng Geol 173:1–9

    Article  Google Scholar 

  • Bruland A (1998) Drillability test methods. NTNU, Trondheim

    Google Scholar 

  • Caudill M (1988) Neural networks primer part III. Al Expert 3:53–59

    Google Scholar 

  • Chambers LD (2010) Practical handbook of genetic algorithms: complex coding systems. CRC Press, Boca Raton

    Google Scholar 

  • Cheniany A, Hasan KS, Shahriar K, Hamidi JK (2012) An estimation of the penetration rate of rotary drills using the Specific Rock Mass Drillability index. Int J Rock Mech Min Sci 22:187–193

    Article  Google Scholar 

  • Chipperfield A, Fleming P, Pohlheim H (2006) Genetic algorithm toolbox for use with MATLAB user’s guide. version 1.2. University of Sheffield

  • Dahl F (2003) DRI, BWI, CLI standards. NTNU, Angleggsdrift, Trondheim

    Google Scholar 

  • Dahl F, Bruland A, Jakobsen PD, Nilsen B, Grøv E (2012) Classifications of properties influencing the drillability of rocks, based on the NTNU/SINTEF test method. Tunnel Undergr Sp Technol 28:150–158

    Article  Google Scholar 

  • Dreyfus G (2005) Neural networks: methodology and application. Springer, Berlin

    Google Scholar 

  • Ekincioglu G, Altindag R, Sengun N, Demirdag S, Guney A (2013) The relationships between drilling rate index (DRI), physico-mechanical properties and specific cutting energy for some carbonate rocks, rock mechanics for resources, energy and environment. Taylor & Francis, London, pp 867–873

    Google Scholar 

  • Garrett J (1994) Where and why artificial neural networks are applicable in civil engineering. J Comput Civil Eng 8:129–130

    Article  Google Scholar 

  • Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Networks 5:861–867

    Article  Google Scholar 

  • Hajihassani M, Armaghani DJ, Marto A, Tonnizam Mohamad E (2014) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Environ. doi:10.1007/s10064-014-0657-x

    Google Scholar 

  • Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, pp 11–14

  • Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366

    Article  Google Scholar 

  • Hoseinie SH, Aghababaei H, Pourrahimian Y (2008) Development of a new classification system for assessing of rock mass drillability index (RDi). Int J Rock Mech Min Sci 45:1–10

    Article  Google Scholar 

  • Hoseinie SH, Ataei M, Osanloo M (2009) A new classification system for evaluating rock penetrability. Int J Rock Mech Min Sci 46:1329–1340

    Article  Google Scholar 

  • Hush DR (1989) Classification with neural networks: a performance analysis. In: Proceedings of the IEEE international conference on systems engineering. Dayton, OH, pp 277–280

  • SPSS Inc (2007). SPSS for Windows (Version 16.0). SPSS Inc, Chicago

  • ISRM (1978) Suggested methods for determining tensile strength of rock materials. Int J Rock Mech Min Sci Geomech Abstr 15:101–103

    Google Scholar 

  • ISRM (1979) Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Int J Rock Mech Min Sci Geomech Abstr 16:135–140

    Google Scholar 

  • Jaeger JC (1967) Failure of rocks under tensile strength. Int J Rock Mech Min Sci 4:219–227

    Article  Google Scholar 

  • Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  • Kahraman S (1999) Rotary and percussive drilling prediction using regression analysis. Int J Rock Mech Min Sci 36:981–989

    Article  Google Scholar 

  • Kahraman S, Balcı C, Yazıcı S, Bilgin N (2000) Prediction of the penetration rate of rotary blast hole drills using a new drillability index. Int J Rock Mech Min Sci 37:729–743

    Article  Google Scholar 

  • Kahraman S, Bilgin N, Feridunoglu C (2003) Dominant rock properties affecting the penetration rate of percussive drills. Int J Rock Mech Min Sci 40:711–723

    Article  Google Scholar 

  • Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725

    Article  Google Scholar 

  • Karpuz C, Pasamehmetoglu AG, Dincer T, Muftuoglu Y (1990) Drillability studies on the rotary blast hole drilling of lignite overburden series. Int J Surf Min Recl 4:89–93

    Article  Google Scholar 

  • Khandelwal M (2013) Correlating P-wave velocity with the physico-mechanical properties of different rocks. Pure appl Geophys 170(4):507–514

    Article  Google Scholar 

  • Khandelwal M, Monjezi M (2013) Prediction of backbreak in open-pit blasting operations using the machine learning method. Rock Mech Rock Eng 46(2):389–396

    Article  Google Scholar 

  • Khandelwal M, Ranjith PG (2010) Correlating index properties of rocks with P-wave measurements. J Appl Geophys 71(1):1–5

    Article  Google Scholar 

  • Lee Y, Oh SH, Kim MW (1991) The effect of initial weights on premature saturation in back-propagation learning, In: Proceedings of the international joint conference on neural networks, pp 765–770

  • Macias FJ, Jakobsen PD, Seo Y, Bruland A (2014) Influence of rock mass fracturing on the net penetration rates of hard rock TBMs. Tunnel Undergr Sp Technol 44:108–120

    Article  Google Scholar 

  • Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47:246–253

    Article  Google Scholar 

  • Masters T (1994) Practical neural network recipes in C ++. Academic Press, Boston

    Google Scholar 

  • Moein MJA, Shaabani E, Rezaeian M (2014) Experimental evaluation of hardness models by drillability tests for carbonate rocks. J Petroleum Sci Eng 113:104–108

    Article  Google Scholar 

  • Momeni E, Nazir R, Armaghani DJ, Maizir H (2014) Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN. Measurement 57:122–131

    Article  Google Scholar 

  • Monjezi M, Khoshalan HA, Varjani AY (2012) Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5(3):441–448

    Article  Google Scholar 

  • Nelson M, Illingworth WT (1990) A practical guide to neural nets. Addison-Wesley, Reading

    Google Scholar 

  • Paola JD (1994) Neural network classification of multispectral imagery. MSc thesis, The University of Arizona

  • Rashidian V, Hassanlourad M (2013) Predicting the shear behavior of cemented and uncemented carbonate sands using a genetic algorithm-based artificial neural network. Geotech Geol Eng 2:1–18

    Google Scholar 

  • Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff- Neilsen OE, Jensen JL, Kendall WS, editors. Networks and chaos-statistical and probabilistic aspects. London: Chapman & Hall, pp 40-123

  • Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Petroleum Sci Eng 59:97–105

    Article  Google Scholar 

  • Schmidt RL (1972) Drillability Studies – Percussive Drilling in the Field, US Bureau of Mines RI 7684

  • Selim AA, Bruce WE (1970) Prediction of penetration rate for percussive drilling. USBM. RI; p 7396

  • Selmer-Olsen R, Lien R (1960) Bergartens borbarhet og sprengbarhet, Teknisk Ukeblad, 34, Oslo, pp 3–11

  • Sievers H (1950) Die Bestimmung des Bohrwiderstandes von Gesteinen, Glückauf 86: 37/38, pp 776–784. Glückauf G.M.B.H., Essen

  • Simpson P (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York

    Google Scholar 

  • 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 equation. Int J Rock Mech Min Sci 43:224–235

    Article  Google Scholar 

  • Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York

    Google Scholar 

  • Tripathy A, Singh TN, Kundu J (2015) Prediction of abrasiveness index of some Indian rocks using soft computing methods. Measurement 68:302–309

    Article  Google Scholar 

  • von Matern N, Hjelmer A (1943) Försök med pågrus (‘Tests with chippings), Medelande nr. 65, Statens väginstitut, Stockholm, 65 pp (English summary, pp 56–60)

  • Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania

  • Wijk G (1989) The stamp test for rock drillability classification. Int J Rock Mech Min Sci Geomech Abstr 26:37–44

    Article  Google Scholar 

  • 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):808–814

    Article  Google Scholar 

  • Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222

    Article  Google Scholar 

  • Yarali O, Kahraman S (2011) The drillability assessment of rocks using the different brittleness values. Tunnel Undergr Sp Technol 26:406–414

    Article  Google Scholar 

  • Yarali O, Soyer E (2013) Assessment of relationships between drilling rate index and mechanical properties of rocks. Tunnel Undergr Sp Technol 33:46–53

    Article  Google Scholar 

  • 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(3):141–158

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Science and Technology, Federation University Australia, P.O. Box 663, Ballarat, VIC, 3353, Australia

    Manoj Khandelwal

  2. Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, UTM, Skudai, Johor, Malaysia

    Danial Jahed Armaghani

Authors
  1. Manoj Khandelwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Danial Jahed Armaghani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Khandelwal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khandelwal, M., Armaghani, D.J. Prediction of Drillability of Rocks with Strength Properties Using a Hybrid GA-ANN Technique. Geotech Geol Eng 34, 605–620 (2016). https://doi.org/10.1007/s10706-015-9970-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10706-015-9970-9

Keywords

Search

Navigation