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Evaluating the Relationships Between NTNU/SINTEF Drillability Indices with Index Properties and Petrographic Data of Hard Igneous Rocks

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Abstract

Thorough and realistic performance predictions are among the main requisites for estimating excavation costs and time of the tunneling projects. Also, NTNU/SINTEF rock drillability indices, including the Drilling Rate Index™ (DRI), Bit Wear Index™ (BWI), and Cutter Life Index™ (CLI), are among the most effective indices for determining rock drillability. In this study, brittleness value (S20), Sievers’ J-Value (SJ), abrasion value (AV), and Abrasion Value Cutter Steel (AVS) tests are conducted to determine these indices for a wide range of Iranian hard igneous rocks. In addition, relationships between such drillability parameters with petrographic features and index properties of the tested rocks are investigated. The results from multiple regression analysis revealed that the multiple regression models prepared using petrographic features provide a better estimation of drillability compared to those prepared using index properties. Also, it was found that the semiautomatic petrography and multiple regression analyses provide a suitable complement to determine drillability properties of igneous rocks. Based on the results of this study, AV has higher correlations with studied mineralogical indices than AVS. The results imply that, in general, rock surface hardness of hard igneous rocks is very high, and the acidic igneous rocks have a lower strength and density and higher S20 than those of basic rocks. Moreover, DRI is higher, while BWI is lower in acidic igneous rocks, suggesting that drill and blast tunneling is more convenient in these rocks than basic rocks.

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Abbreviations

g :

Index of interlocking

t :

Index of grain size homogeneity

IS:

Saturation Index

IF:

Feldspathic Index

IC:

Coloration Index

ρ :

Dry density

ϕ :

Porosity

V p :

P-wave velocity

R N :

Schmidt rebound number

Is(50) :

Point load strength index

S20 :

Brittleness value

SJ:

Sievers’ J-Value

AV:

Abrasion Value

AVS:

Abrasion Value Cutter Steel

DRI:

Drilling Rate Index

BWI:

Bit Wear Index

CLI:

Cutter Life Index

VHNR:

Vickers Hardness Number Rock

™:

Trademark

References

  • Aligholi S, Khajavi R, Razmara M (2015) Automated mineral identification algorithm using optical properties of crystals. Comput Geosci 85:175–183

    Article  Google Scholar 

  • Aligholi S, Lashkaripour GR, Khajavi R, Razmara M (2017a) Automatic mineral identification using color tracking. Pattern Recognit 65:164–174

    Article  Google Scholar 

  • Aligholi S, Lashkaripour GR, Ghafoori M (2017b) Strength/Brittleness classification of igneous intact rocks based on basic physical and dynamic properties. Rock Mech Rock Eng 50(1):45–65

    Article  Google Scholar 

  • Altindag R (2010) Assessment of some brittleness indexes in rock drilling efficiency. Rock Mech Rock Eng 43:361–370

    Article  Google Scholar 

  • Anon (1995) The description and classification of weathered rocks for engineering purposes. Geological Society Engineering Group Working Party Report. Q J Eng Geol 28:207–242

    Article  Google Scholar 

  • Asmussen P, Conrad O, Günther A, Kirsch M, Riller U (2015) Semi-automatic segmentation of petrographic thin section images using a “seeded-region growing algorithm” with an application to characterize weathered subarkose sandstone. Comput Geosci 83:89–99

    Article  Google Scholar 

  • Aydin A (2009) ISRM Suggested method for determination of the Schmidt hammer rebound hardness: revised version. Int J Rock Mech Min Sci 46:627–634

    Article  Google Scholar 

  • Azimian A, Ajalloeian R, Fatehi L (2014) An empirical correlation of uniaxial compressive strength with P-wave velocity and point load strength index on marly rocks using statistical method. Geotech Geol Eng 32(1):205–214

    Article  Google Scholar 

  • Barla G, Pelizza S (2000) TBM tunneling in difficult ground conditions, Proceedings of GeoEng 2000. Proceedings of the International Conference on Geotechnical & Geological Engineering. Technomic Publishing Company, Lancaster, Melbourne, pp 329–354

    Google Scholar 

  • Barton N (2000) TBM tunnelling in jointed and faulted rock. A.A. Balkema, Rotterdam. ISBN 9058093417

    Google Scholar 

  • Blindheim OT, Grov E, Nilsen B (2002) The effect of mixed face conditions (MFC) on hard rock TBM performance. In: AITES-ITA word tunnel congress, Sydney, pp 24–32

  • Bruland A (1998a) Hard rock tunnel boring. PhD thesis, Department of Civil and Transport Engineering, NTNU, Trondheim, Norway

  • Bruland A (1998b) Prediction model for performance and costs, in Norwegian TBM Tunnelling, Publication No. 11, Norwegian Tunnelling Society

  • Bruland A (1998c) Hard rock tunnel boring—Vol. 1—Background and discussion. NTNU Trondheim, p 49

  • Bruland A (1998d) Hard rock tunnel boring: drillability—test methods. Project report 13A-98, NTNU Trondheim

  • Bruland A (1998e) Hard rock tunnel boring: drillability—statistics of drillability test results. Project report 13C-98, NTNU Trondheim

  • Dahl F (2003) DRI, BWI, CLI Standards. NTNU, Angleggsdrift, Trondheim, Norway, p 20

  • Dahl F, Grov E, Breivik T (2007) Development of a new direct test method for estimating cutter life, based on the Sievers’ J miniature drill test. Tunn Undergr Space Technol 22:106–116

    Article  Google Scholar 

  • Dahl F, Bruland A, Grov E, Nilsen B (2010) Trademarking the NTNU/SINTEF drillability test indices. Tunn Tunn Int (June), 44–46

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

    Article  Google Scholar 

  • Davis JC (1973) Statistics and data analysis in geology. Wiley, New York, p 550

    Google Scholar 

  • Deere DU, Miller RP (1966) Engineering classification and index properties for intact rocks. Tech Rep no. AFNL-TR-65-116, Air Force Weapons Laboratory, New Mexico, p 300

  • Dursun AE, Gokay MK (2016) Cuttability assessment of selected rocks through different brittleness values. Rock Mech Rock Eng 49(4):1173–1190

    Article  Google Scholar 

  • Ellecosta P, Schneider S, Kasling H, Thuro K (2015) Hardness—a new method for characterising the interaction of TBM disc cutters and rocks?. In: Proceedings of the 13th congress on rock mechanics, ISRM Congress 2015. In-novation in applied and theoretical rock mechanics, Palais des Congres der Montre al, Canada, Paper 688, p 10. (ISBN: 978-1926872-25-4)

  • Ersoy A, Waller MD (1995) Textural characterisation of rocks. Eng Geol 39:123–136

    Article  Google Scholar 

  • Espallargas N, Jakobsen PD, Langmaack L, Macias FJ (2015) Influence of corrosion on the abrasion of cutter steels used in TBM tunnelling. Rock Mech Rock Eng 48(1):261–275

    Article  Google Scholar 

  • Farrokh E, Rostami J, Laughton C (2011) Analysis of unit supporting time and support installation time for Open TBMs. Rock Mech Rock Eng 44(4):431–445

    Article  Google Scholar 

  • Fueten F, Mason J (2007) An artificial neural net assisted approach to editing edges in petrographic images collected with the rotation polarizer stage. Comput Geosci 33:1176–1188

    Article  Google Scholar 

  • Gong QM, Zhao J (2009) Development of a rock mass characteristics model for TBM penetration rate prediction. Int J Rock Mech Min Sci 46(1):8–18

    Article  Google Scholar 

  • Hashemnejad A, Ghafoori M, Tarigh Azali S (2016) Utilizing water, mineralogy and sedimentary properties to predict LCPC abrasivity coefficient. Bull Eng Geol Environ 75(2):841–851

    Article  Google Scholar 

  • Hassanpour J (2009) Investigation of the effect of engineering geological parameters on TBM performance and Modifications to existing prediction models. Ph.D. Thesis, Tarbiat Modares University, Tehran, Iran

  • Hassanpour J, Rostami J, Tarigh Azali S, Zhao J (2014) Introduction of an empirical TBM cutter wear prediction model for pyroclastic and mafic igneous rock; a case history of Karaj water conveyance tunnel, Iran. Tunn Undergr Space Technol 43(2014):222–231

    Article  Google Scholar 

  • Howarth DF, Rowlands JC (1987) Quantitative assessment of rock texture and correlation with drillability and strength properties. Rock Mech Rock Eng 20:57–85

    Article  Google Scholar 

  • Hugman RH, Friedman M (1979) Effects of texture and composition on mechanical behavior of experimentally deformed carbonate rocks. Am Assoc Pet Geol Bull 63(9):1478–1489

    Google Scholar 

  • ISRM (1981) Rock characterization, testing and monitoring. Pergamon, Oxford, ISRM suggested methods, p 211

    Google Scholar 

  • ISRM (1985) Suggested methods for determining point load strength. Int J Rock Mech Min Sci Geomech Abstr 22(2):51–60

    Article  Google Scholar 

  • ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods. International Society for Rock Mechanics. Compilation Arranged by the ISRM Turkish National Group, Ankara, p 293

  • Izadi H, Sadri J, Mehran NA (2015) A new intelligent method for minerals segmentation in thin sections based on a novel incremental color clustering. Comput Geosci 81:38–52

    Article  Google Scholar 

  • Jakobsen PD, Bruland A, Dahl F (2013) Review and assessment of the NTNU/SINTEF Soil Abrasion Test (SAT™) for determination of abrasiveness of soil and soft ground. Tunn Undergr Space Technol 37:107–114

    Article  Google Scholar 

  • Jin X (2012) Filed Nov. 14, 2007, and issued Sept. 4 (2012) Segmentation-based image processing system. U.S. Patent 8,260,048

  • Jung J, Brousse R (1959) Classification modale des roches éruptive.s: roches éruptive.s utilizant les données fournies par le compteur de points. Paris: Masson & Cie

  • Kahraman S, Fener M, Kasling H, Thuro K (2016) The influences of textural parameters of grains on the LCPC abrasivity of coarse-grained igneous rocks. Tunn Undergr Space Technol 58:216–223

    Article  Google Scholar 

  • Karakus M, Kumral M, Kilic O (2005) Predicting elastic properties of intact rocks from index tests using multiple regression modeling. Int J Rock Mech Min Sci 42:323–330

    Article  Google Scholar 

  • Katz O, Reches Z, Roegiers JC (2000) Evaluation of mechanical rock properties using a Schmidt hammer. Int J Rock Mech Min Sci 37:723–728

    Article  Google Scholar 

  • Larsen ES, Miller FS (1935) The Rosiwal method and the modal determination of rocks. Am Mineral 20:260–273

    Google Scholar 

  • Lashkaripour GR (2002) Predicting mechanical properties of mudrock from index parameters. Bull Eng Geol Environ 61(1):73–77

    Article  Google Scholar 

  • Liu Z, Shao J, Xu W, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10(5):651–663

    Article  Google Scholar 

  • Macias FJ, Jakobsen PD, Bruland A (2014a) Rock mass variability and TBM prediction. ISRM Regional Symposium - EUROCK 2014, 27-29 May, Vigo, Spain

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

    Article  Google Scholar 

  • Macias FJ, Dahl F, Bruland A (2016) New rock abrasivity test method for tool life assessments on hard rock tunnel boring: the rolling indentation abrasion test (RIAT). Rock Mech Rock Eng 49(5):1679–1693

    Article  Google Scholar 

  • Middleton A, Freestone IC, Leese MN (1985) Textural analysis of ceramic thin sections: evaluation of grain sampling procedures. Archaeometry 27(1):64–74

    Article  Google Scholar 

  • Johannessen O, Jacobsen K, Ronn PE, Moe, HL (1995) Project Report 2C-95 tunnelling costs for drill and blast. NTNU Trondheim, Department of Building and Construction Engineering

  • Moradizadeh M, Cheshomi A, Ghafoori M, TrighAzali S (2016) Correlation of equivalent quartz content, Slake durability index and Is50 with Cerchar abrasiveness index for different types of rock. Int J Rock Mech Min Sci 86:42–47

    Google Scholar 

  • Nilsen B, Dahl F, Holzhauser J, Raleigh P (2007) The new test methodology for estimating the abrasiveness of soils for TBM tunnelling. In: Rapid excavation and tunneling conference (RETC), pp 104–116

  • NTH (1983) Hard Rock Tunnel Boring, Project Report 1-83. Div. of Construction Engineering, Trondheim, Norwegian Institute of Technology, p 94

    Google Scholar 

  • Petruk W (1989) Short course on image analysis applied to mineral and earth sciences. Mineralogical Association of Canada, Ottawa

    Google Scholar 

  • Prikryl R (2006) Assessment of rock geomechanical quality by quantitative rock fabric coefficients: limitations and possible source of misinterpretations. Eng Geol 87:149–162

    Article  Google Scholar 

  • Reedy CL (2006) Review of digital image analysis of petrographic thin sections in conservation research. J Am Inst Conserv 45(2):127–146

    Article  Google Scholar 

  • Rostami J (1997) Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure. Ph. D. Thesis, Colorado School of Mines, Golden, Colorado, USA, p. 249

  • Rostami J, Ozdemir L, Bruland A, Dahl F (2005) Review of issues related to Cerchar abrasivity testing and their implications on geotechnical investigations and cutter cost estimates. In: Proceedings of the RETC, pp 738–751

  • Rostami J, Ghasemi A, Gharahbagh E, Dogruoz C, Dahl F (2014) Study of dominant factors affecting Cerchar abrasivity index. Rock Mech Rock Eng 47(5):1905–1919

    Article  Google Scholar 

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

  • Shalabi F, Cording EJ, Al-Hattamleh OH (2007) Estimation of rock engineering properties using hardness tests. Eng Geol 90:138–147

    Article  Google Scholar 

  • Shorey PR, Barat D, Das MN, Mukherjee KP, Singh B (1984) Schmidt hammer rebound data for estimation of large scale in situ coal strength. Int J Rock Mech Min Sci Geomech Abstr 21:39–42

    Article  Google Scholar 

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

  • Streckeisen A (1976) To each plutonic rock its proper name. Earth Sci Rev 12:12–33

    Article  Google Scholar 

  • Tandon SR, Gupta V (2013) The control of mineral constituents and textural characteristics on the petrophysical & mechanical (PM) properties of different rocks of the Himalaya. Eng Geol 153:125–143

    Article  Google Scholar 

  • The Science of Rock Mechanics. Part I. The Strength Properties of Rocks. In: Series on Rock and Soil Mechanics, 2nd edn, vol. 1 (1971/73), No. 2. Trans Tech Publications, Clausthal

  • Thuro K (1997) Drillability Prediction: Geological Influences in Hard Rock Drill and Blast Tunneling, vol 86. Springer, Geol Rundsch, pp 426–438

    Google Scholar 

  • Thuro K, Plinninger RJ (2003) Hard rock tunnel boring, cutting, drilling and blasting: rock parameters for excavatability. In: Proceedings of the 10th ISRM Int. Congress on Rock Mechanics, Johannesburg, South Africa, pp 1227–1234

  • Tugrul A, Zarif IH (1999) Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey. Eng Geol 51:303–317

    Article  Google Scholar 

  • Ulusay R, Tureli K, Ider MH (1994) Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariate statistical techniques. Eng Geol 38(1–2):135–157

    Article  Google Scholar 

  • Villeneuve MC (2008) Examination of geological influence on machine excavation of highly stressed tunnels in massive hard rock. PhD thesis. Queen’s University, Kingston

  • Vincent L (1993) Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans Image Process 2(2):176–201

    Article  Google Scholar 

  • von Matern N, Hjelmer A (1943) Forsok med pagrus (‘‘Tests with Chippings’’), Medelande nr. 65, Statens vaginstitut, Stockholm, pp 65. (English summary, pp 56–60)

  • Yagiz S (2002) Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machines. Ph.D. Thesis, Department of Mining and Earth Systems Engineering, Colorado School of Mines, Golden, Colorado, USA, p 289

  • Yagiz S (2008) Utilizing rock mass properties for predicting TBM performance in hard rock condition. Tunn Undergr Space Technol 23(3):326–339

    Article  Google Scholar 

  • Yagiz S (2011) P-wave velocity test for assessment of geotechnical properties of some rock materials. Bull Mater Sci 34(4):947–953

    Article  Google Scholar 

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

    Article  Google Scholar 

  • 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(4):803–810

    Article  Google Scholar 

  • Zare S (2007) Prediction model and simulation tool for time and costs of drill and blast tunnelling. PhD thesis, Department of Civil and Transport Engineering, NTNU, Trondheim, Norway

  • Zare S, Bruland A (2013) Applications of NTNU/SINTEF drillability indices in hard rock tunneling. Rock Mech Rock Eng 46:179–187

    Article  Google Scholar 

  • Zare S, Bruland A, Rostami J (2016) Evaluating D&B and TBM tunnelling using NTNU prediction models. Tunn Undergr Space Technol 59:55–64

    Article  Google Scholar 

  • Zhao K, Janutolo M, Barla G (2012) A completely 3D model for the simulation of mechanized tunnel excavation. Rock Mech Rock Eng 45(4):475–497

    Article  Google Scholar 

  • Zhou Y, Starkey J, Mansinha L (2004) Segmentation of petrographic images by integrating edge detection and region growing. Comput Geosci 30:817–831

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge Mr. Filip Dahl (SINTEF, Norway) for his useful documents and guidance upon the NTNU/SINTEF rock drillability test procedures and apparatus.

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Correspondence to Gholam Reza Lashkaripour.

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Aligholi, S., Lashkaripour, G.R., Ghafoori, M. et al. Evaluating the Relationships Between NTNU/SINTEF Drillability Indices with Index Properties and Petrographic Data of Hard Igneous Rocks. Rock Mech Rock Eng 50, 2929–2953 (2017). https://doi.org/10.1007/s00603-017-1289-9

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