Rock drillability characteristic is one of the important properties for mining and tunneling operations. The rock drillability can be determined by using the drilling rate index (DRI) for engineering applications. The present study attempts to develop a practical and convenient DRI estimation model by using rock strength and abrasivity properties. For this purpose, fuzzy inference system (FIS) being an accurate prediction model was applied to predict DRI by using experimental data obtained with 37 different rocks. The predictive FIS based on experts knowledge by taking mechanical and abrasivity properties as input parameters was created on MATLAB. This structure was carried out by using Mamdani extraction method. DRI values obtained experimentally and estimated from the FIS model were compared. This comparison is given with statistically reliable (R2=0.9277) results. In order to prove the validity of the FIS model for DRI prediction, a validation process has been performed by using test data as well. The performance determination coefficients (R2) are found as 0.9513 by using test data. As a result, it was found that DRI values can be predicted very efficiently and accurately with the proposed prediction method.
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Acaroğlu O, Ozdemir L, Asbury B (2008) A fuzzy logic model to predict specific energy requirement for TBM performance prediction, Tunn. Undergr Space Tech 23:600–608
Adebayo B, Opafunso ZO, Akande JM (2010) Drillability and strength characteristics of selected rock in Nigeria. AU J T 14(1):56–60
Aldı A (2016) Kayaçların Delinebilirliğine Etki Eden Parametrelerin İncelenmesi, Yüksek Lisans Tezi, Bülent Ecevit Üniversitesi, Maden Mühendisliği Anabilim Dalı, Zonguldak, 105 s.
Altındağ R, Güney A (2010) Predicting the relationships between brittleness and mechanical properties (UCS, TS and SH) of rocks. Sci Res Essays 5:2107–2118
Akün ME, Karpuz C (2005) Drillability studies of surface set diamond drilling in Zonguldak region sandstones from Turkey. Technical Note. Int J Rock Mech Min Sci 42:473–479
Ardeshir A, Farnood Ahmadi P, Bayat H (2018) A prioritization Model for HSE risk assessment using combined failure mode and effect analysis and fuzzy ınference system: A case study in Iranian Construction Industry. Int J Eng 31(9):1487–1497
Andrews R, Harelve G, Nygaard R (2007) Methods of using logs to quantify drillability. Paper No. SPE 106571, SPE, Denver, CO, April 16–18.
Armaghani DJ, Mohamad ET, Momeni E, Narayanasamy MS (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ 74:1–19
Asadi M, Bagheripour MH, Eftekhari M (2013) Development of optimal fuzzy models for predicting the strength of intact rocks. Comput Geosci 54:107–112
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
Atici U (2011) Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network. Expert Syst Appl 38:9609–9618
Backstrom AL, Metcalf JG, McKelvie S (2009) What happens in Las Vegas: the Apex Tunnel geologic investigation. Proc. 2009 Rapid Excavation and Tunneling Conference, G.Almeraris & B. Mariucci (eds), SME, Littleton, CO, 534-547.
Bruland A (1998) Hard rock tunnel boring, drillability test methods. Norwegian University of Science and Technology, Dept. of Civil and Transport Engineering Project report 13A-98.15.
Collotta M, Bello LL, Pau G (2015) A novel approach for dynamic traffic lights management based on wireless sensor networks and multiple fuzzy logic con- trollers. Expert Syst Appl 42:5403–5415
Çapik M (2014) Cankurtaran Ve Salmankaş Tünellerindeki Kayaçlarin Delinebilirlik, Aşindiricilik, Mekanik Ve Petroğrafik Özelliklerinin Araştirilmasi, Net Delme Hizi Ve Bit Tüketimi Ile Ilişkilendirilmesi, Doktora Tezi, KARADENİZ TEKNİK ÜNİVERSİTESİ FEN BİLİMLERİ ENSTİTÜSÜ, Maden Mühendisliği Anabilim Dalı, Trabzon, 271 s.
Capik M, Yilmaz AO, Yasar S (2016) Relationships between the drilling rate index and physicomechanical rock properties. Bull Eng Geol Environ 76:253–261
Dahl F (2003) DRI Standards. NTNU, Angleggsdrift, Trondheim, p 21
Dahl F, Bruland A, Jakobsen PD, Nilsen B, Grov E (2012) Classifications of properties influencing the drillability of rocks based on the NTNU/SINTED test method. Tunn Undergr Space Tech 28:150–158
Deliormanlı AH (2012) Cerchar abrasivity index (CAI) and its relation to strength and abrasion test methods for marble stones. Constr Build Mater 30:16–21
Dipova N (2012) Investigation of the relationships between abrasiveness and strength properties of weak limestone along a tunnel route. J Geol Eng 36:23–34
Er S, Tugrul A (2016) Estimation of Cerchar abrasivity index of granitic rocks in Turkey by geological properties using regression analysis. Bull Eng Geol Environ 75(3):1325–1339
Fattahi H, Bazdar H (2017) Applying improved artificial neural network models to evaluate drilling rate index. Tunn Undergr Space Tech 70:114–124
Fattahi H, Bayat N (2019) Forecasting of rock drillability using a new computational intelligent method. Geotech Geol Eng 38:5693. https://doi.org/10.1007/s10706-019-00971-5
Ghasemi E, Ataei M (2013) Application of fuzzy logic for predicted roof fall rate in coal mines. Neural Comput & Applic 22(1):311–321
Heidari M, Mohseni H, Jalali SH (2018) Prediction of uniaxial compressive strength of some sedimentary rocks by fuzzy and regression models. Geotech Geol Eng 36:401–412
Hoseinie SH, Aghababaei H, Pourrahimian Y (2008) Development of a new classification system for assesing of rock mass drillability index (RDi). Int J Rock Mech Min Sci 45:1–10
Hoseinie SH, Ataei M, Osanloo M (2009) A new classification system evalating rock penetrability. Int J Rock Mech Rock Eng 46:1329–1340
Howarth DF, Adamson WR, Berndt JR (1986) Correlation of model tunnel boring and drilling machine performances with rock proporties. Int J Rock Mech Min Sci 23:57–85
Howarth DF, Rowland JC (1987) Quantitative assessment of rock texture and correlation with drillability and strength properties. Rock Mech Rock Eng 20:57–85
Huanga C, Moraga C (2005) Extracting fuzzy if–then rules by using the information matrix technique. J Comput Syst Sci 70:26–52
Iphar M, Çukurluoz AK (2018) Fuzzy risk assessment for mechanized underground coal mines in Turkey. Int J Occup Saf Ergon 26:256–271. https://doi.org/10.1080/10803548.2018.1426804
ISRM (1978) Suggested method for determining tensile strength of rock materials. Int J Rock Mech Min Sci Geomech 15:99–103
ISRM (1979) Suggested method for determining the uniaxial compressive strength and deformability of rock. Int J Rock Mech Min Sci Geomech 16:135–140
ISRM (2015) Suggested method for determining the abrasivity of rock by the Cerchar abrasivity test. The ISRM Suggested Method for Rock Characterization, Testing and Monitoring: 2007-2014, R. Ulusay [edt], Springer, 101-106.
Izquierdo SS, Izquierdo LR (2018) Mamdani fuzzy system for modelling and simulation: a critical assessment. J Artif Soc Soc Simul (JASSS) 21(3):2. https://doi.org/10.18564/jasss.3444
Kahraman S (1999) Rotary and percussive drilling prediction using regression analysis. Int J Rock Mech Min Sci 36:981–989
Kahraman S, Bilgin N, Feridunoğlu C (2003) Dominant rock proporties affecting penetration rate of percussive drills. Int J Rock Mech Min Sci 40:711–723
Kahraman S, Alber M, Fener M, Günaydın 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
Kahraman S, Fener M, Kozman E (2012) Predicting the compressive and tensile strength of rocks from indentation hardness index. J South Afr Insti Min Metalurgy 112:331–339
Karpuz C, Paşamehmetoğlu AC, Dinçer T, Müftüoğlu Y (1990) Drillability studies on the rotary blast hole drilling on lignite overburden series. Int J Surfine Min Reel 4:89–93
Khandelwal M, Armaghani DJ (2016) Predicition of drillability of rocks with strength propertiess using a Hybrid GA-AAN technique. Geotech Geol Eng 34(5):605–620
Kramadibrata S, Made AR, Juanda J, Simangunsong GM, Priagung N (2001) The use of dimensional analysis to anlyse the relationship between penetration rate of Jack Hammer and rock properties and operational characteristics. Proc. Indonesian Mining Conf. and Exh, Jakarta
Kruse R, Nauck D (1995) Learning methods for fuzzy systems. In: Proceedings of the 3rd German GI-workshop “Neuro-Fuzzy- Systeme”, Darmstadt, Germany, vol 3, pp 683–697
Mamdani EH, Assilian S (1975) Neuro-fuzzy and soft computing. A computational approach to learning and machine intelligence, Prentice-Hall
Matlab (2018) Version 2018b. The MathWorks, Inc., United States. www.mathworks.com
McFeat-Smith I, Fowell RJ (1977) Correlation of rock properties and the cutting performance of tunneling machines, In Proc. of a Conf. on Rock Eng., pp. 581-602.
Mikail M, Keskin İ (2009) Fuzzy logic applications in animal breeding. Selçuk Tarım ve Gıda Bilimleri Dergisi 23(47):89–95
Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 60:54–68
Moein MJA, Shaabani E, Rezaeian M (2014) Experimental evaluation of hardness models by drillability tests for carbonate rocks. J Pet Sci Eng 113:104–108
Najafi AB, Farsangi MAE, Saeedi GR (2015) A fuzzy logic model to predict the out-of seam dilution in longwall mining. Int J Min Sci Technol 25:91–98
Nazir R, Momeni E, Armaghani DJ, Amin MM (2013) Correlation between unconfined compressive strength and indirect tensile strength of limestone rock sample. Electron J Geotech Eng 18:1737–1746
Nilsen B, Özdemir L (1993) Hard rock tunnel boring prediction and field performance, Chapter 52 edn. RETC Conf. Proc, Boston, pp 832–852
Ozdogan MV, Deliormanli AH, Yenice H (2018) The correlations between the Cerchar abrasivity index and the geomechanical properties of building stones. Arab J Geosci 11(20):604
Rezaei M, Majdi A, Monjezi M (2014) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput & Applic 24:233–241
Ru Z, Zhao Z, Zhu C (2019) Probabilistic evaluation of drilling rate index based on a least square support vector machine and Monte Carlo simulation. Bull Eng Geol Environ 78:3111–3118
Rostami J, Kahraman S, Yu X, Copur H, Balcı C, Bamford W, Asbury B (2016) The relation between unaxial compressive and Brazilian tensile strength. Rock Mech Rock Eng: From the Past to the Future, Taylor and Francis Group, London, ISBN: 978-1-138-03265-1
Saedi B, Mohammadi SD, Shahbazi H (2019) Application of fuzzy inference system to predict unaxial compressive strength and elastic modulus of migmatites. Environ Earth Sci 78:208–214
Sakız U, Yaralı O, Aydın H (2017) Kayaç özelliklerine bağlı olarak kayaç delinebilirliğinin yapay sinir ağları (ysa) metodu ile tahmini. Karaelmas Fen ve Mühendislik Dergisi 7(1):12–22
Selmer-Olsen R, Lien R (1960) Bergartens borbarhet og sprengbarhet, 34th edn. Teknisk Ukeblad, Oslo, pp 3–11
Selmer-Olsen R, Blindheim OT (1970) On the drillability of rock by percussive drilling. In: Pro. of the Sec. Cong. Int. Soc. on Rock Mech., pp.65-70.
Shafique U, Bakar MA (2015) Evaluation of relationships between drilling rate index and physical and strength properties of selected rock units of Pakistan. Nucleus 52:79–84
Sonmez H, Tuncay E, Gokceoglu C (2004) Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate. Int J Rock Mech Min Sci 41:717–729
Su O (2016) Performance evaluation of button bits in coal measure rocks by using multiple regression analysis. Rock Mech Rock Eng 49(2):541–553
Takagi T, Sugeno M (1985) Fuzzy Identification of Systems and its applications to modeling and control. IEEE Trans Syst MAN Cybernet SMC-15(1):116–132
Tamrock (1987) Handbook of Underground Drilling. Tamrock Drills SF-33310 Tampere, Finland, p 327
Tanaino AS (2005) Rock classification by drillability. Part I: analysis of the avaible classification. J Min Sci 41(6):541–549
Teymen A (2020) The usability of Cerchar abrasivity index for the estimation of mechanical rock properties. Int J Rock Mech Min Sci 128:104258
Teymen A, Mengüç EC (2020) Comparative evaluation of different statistical tools for prediction of unaxial compressive strength of rocks. Int J Min Sci Technol 30:785–797. https://doi.org/10.1016/j.ijmst.2020.06.008
Thuro K (1997) Predictionof drillability in hardrock tunneling by drilling and blasting, In: Golse J. et. al, Hinkel and Schubert (edt.), Tunnels for people, pp.103-108.
Thuro K, Spaun G (1996) Introducing the ‘detruction work’ as a to new rock property of toughness refering to drillability in conventional drill and blast tunnelling. ed. M. Barla, Eurock’96 Pre. and Per. in Rock Mech. Rock Eng., 2 : 707-13.
Tumaç D (2016) Artificial neural network application to predict the sawability performance of large diameter circular saws. Measurement 80:12–20
Ustabaş Kaya G, Erkaymaz O, Saraç Z (2016) Optimization of digital holographic setup by a fuzzy logic prediction system. Expert Syst Appl 56:177–185
Ustabaş Kaya G, Erkaymaz O, Saraç Z (2019) New adaptive neuro-fuzzy solution for optimization of the parameters in the digital holography setup. Soft Comput 23:8827–8837
Wijk G (1989) The stamp test for rock drillability classification. Int J Rock Mech Min Sci Geomech 26:37–44
Yarali O, Soyer E (2007) Prediction of drilling rate index (DRI) using performance analysis of tunnel boring machines. In: Proceedings of the 2th Symposium on Underground Excavations for Transportation. Istanbul, Turkey, pp 169–179
Yaralı O, Akçın, NA, Bacak G, Su O. (2008). Mekanik Kazıda Kayaçların Petrografik Özellikleri ile Delinebilirlik ve Aşındırıcılık Özellikleri Arasındaki İlişkilerin İncelenmesi. TÜBİTAK Projesi, Proje No: 104M437, Final Raporu. Zonguldak.
Yaralı O, Kahraman S (2011) The drillability assessment of rocks using the different brittleness values. Tunn Undergr Space Techol 26:406–414
Yaralı O, Soyer E (2013) Assessment of relationships between drilling rate ındex and mechanical properties of rocks. Tunn Undergr Space Techol 33:46–53
Yaralı O, Duru H, Sakiz U (2014) Evaluation of the relationships among drilling rate index (DRI), mechanical properties, Cerchar abrasivity index and specific energy for rocks. Aachen Sixth International Mining Symposium, Germany, pp 205–220
Yasar S, Capik M, Yilmaz AO (2015) Cuttability assessment using the Drilling Rate Index (DRI). Bull Eng Geol Environ 74:1349–1361
Yenice H, Ozdogan MV, Ozfırat MK (2018) A sampling study on rock properties affecting drilling rate index (DRI). J Afr Earth Sci 141:1–8
Yenice H (2019) Determination of drilling rate ındex based on rock strength using regression analysis. An Acad Bras Cienc 91(3):e20181095
Yesiloglu-Gultekin N, Gokceoglu C, Sezer E (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci 62:113–122
Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46:803–810
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zadeh LA, Fu KS, Tanaka K, Shimura M (1975) Calculus of fuzzy restrictions. Fuzzy sets and their applications to cognitive and decision pro- cessing. Academic Press, New York, pp 1–40
Zare S, Bruland A (2013) Applications of NTNU/SINTEF drillability indices in hard rock tunneling. Rock Mech Rock Eng 46:179–187
Zhang W, Wu C, Zhong C, Li Y, Wang L (2020a) Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12:469–477
Zhang W, Zhang R, Wu C, Goh ATC, Lacasse S, Liu Z, Liu H (2020b) State-of-the-art review of soft computing applications in underground excavations. Geosci Front 11:1095–1106
Zhang WG, Zhang RH, Han L, Goh ATC (2019) Engineering properties of the Bukit Timah Granitic residual soil in Singapore. Undergr Space 4:98–108
Zhang W, Han L, Zong Z, Zhang Y (2020c) Digitalization of mechanical and physical properties of Singapore Bukit Timah granite rocks based on borehole data from four sites. Undergr Space. https://doi.org/10.1016/j.undsp.2020.02.003
Zhang X, Zhai YH, Xue CJ, Jiang TX (2012) A study of the distribution of formation drillability. Pet Sci Technol 29:149–159
Zorlu K, Gökçeoğlu C, Sonmez H (2004) Prediction of the uniaxial compressive strength of Greywacke by fuzzy inference system. Eng Geol Infrastr Plann Europe 104:203–210
The authors are grateful to TUBITAK for project 104M437 and Zonguldak Bulent Ecevit University for project BAP-2015-98150330-01 which supports this study. The author is grateful to Associate Professor Okan Su (Zonguldak Bulent Ecevit University) for his valuable comments and suggestions.
Conflict of interest
The authors declare that they have no competing interests.
Responsible Editor: Zeynal Abiddin Erguler
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Sakız, U., Kaya, G.U. & Yaralı, O. Prediction of drilling rate index from rock strength and cerchar abrasivity index properties using fuzzy inference system. Arab J Geosci 14, 354 (2021). https://doi.org/10.1007/s12517-021-06647-w
- Drilling rate index (DRI)
- Rock properties
- Uniaxial compressive strength (UCS)
- Brazilian tensile strength (BTS)
- Cerchar abrasivity index (CAI)
- Fuzzy inference system