Abstract
In this study, we have established and demonstrated the relationships between petrographic and fabric features of igneous rocks and their engineering properties experimentally. To meet this purpose, we have examined several igneous rock specimens and have investigated their engineering properties, including their drillability (drilling rate index (DRI)), abrasivity (Cerchar abrasivity index (CAI)), mechanical features (uniaxial compressive strength (UCS), point load strength index (IS50), Brazilian test strength (BTS)), and their physical properties (dry density, porosity (N), and wave velocity (VP)). Then, we have investigated their petrographic features, including shape descriptors, size descriptors, rock fabric features, and mineralogical indexes. We tested 16 types of igneous rocks from 8 various locations in the Gelas tunnel route in Naghadeh City, west Azerbaijan, Iran. The Pearson correlation coefficient indicated a low drillability potential of fine-grained rocks compared to that of coarse-grained rocks. UCS displayed the best Pearson correlation with heterogeneity (H) and texture coefficient (TC) (R = − 0.88 and R = 0.86, respectively). Although the results obtained from multilinear regression (MLR) and multilinear log-linear regression (MLLR) models proved the efficiency of such models in predicting CAI, TC, H, index of interlocking (g), and Feldspathic index (IF). Their determination coefficient (R2) was 0.84 and R2 = 0.87, respectively. Nevertheless, in comparison, the artificial neural network (ANN) analysis is apparently more efficient than both MLR and MLLR (R2 = 0.90). The results revealed rock fabric features have a higher capability in identifying the engineering properties of igneous rocks than their mineralogical composition.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Some or all data generated or used during the study are available from the corresponding author by request, including laboratory data results.
Abbreviations
- DRI:
-
Drilling rate index
- CAI:
-
Cerchar abrasivity index
- UCS:
-
Uniaxial compressive strength
- I S50 :
-
Point load strength index
- BTS:
-
Brazilian test strength
- H :
-
Heterogeneity index
- TC:
-
Texture coefficient
- g :
-
Index of interlocking
- t :
-
Index of grain size homogeneity
- AR:
-
Aspect ratio
- AW:
-
Area weighting of grains
- AF:
-
Angle factor
- N 0 :
-
Numbers of grains with AR < 2
- N 1 :
-
Numbers of grains with AR > 2
- FF0 :
-
Arithmetic mean of discriminated FF
- FF:
-
Form factor
- AR1 :
-
Arithmetic mean of discriminated AR
- AF1 :
-
Angle factor/5
- N :
-
Porosity
- IS:
-
Saturation index
- IF:
-
Feldspathic index
- IC:
-
Coloration index
- S J :
-
Sievers’ J-miniature
- S 20 :
-
Brittleness tests
- MLR:
-
Multilinear regression
- MLLR:
-
Multilinear log-linear regression
- ROC:
-
Receiver operating characteristic
- Lpi :
-
Grain perimeter that contacts grain
- Ai:
-
Area neighboring grains
- A avg :
-
Average area of the grains
- A i :
-
Area of individual grain
- r i :
-
Mean grain size
- Qtz%:
-
Quartz percent
- Afs%:
-
Alkali-feldspars percent
- Pl%:
-
Plagioclase percent
- Ra:
-
Average grain size of different constituent minerals
- VP:
-
P-wave velocity
References
Akesson U, Stingh J, Lindqvist JE, Goransson M (2003) The influence of foliation on the fragility of igneous rocks, image analysis, and quantitative microscopy. Eng Geol 68(3–4):275–288. https://doi.org/10.1016/S0013-7952(02)00233-8
Akram MS, Farooq S, Naeem M, Ghazi S (2017) Prediction of mechanical behavior from the mineralogical composition of Sakesar limestone, Central Salt Range, Pakistan. Bull Eng Geol Environ 76:601–615. https://doi.org/10.1007/s10064-016-1002-3
Alber M, Kahraman S (2009) Predicting the uniaxial compressive strength and elastic modulus of a fault breccia from texture coefficient. Rock Mech Rock Eng 42:117–127. https://doi.org/10.1007/s00603-008-0167-x
Alber M, Yarali O, Dahl F, Bruland A, Kasling H, Michalakopoulos TN, Cardu M, Hagan P, Aydin H, Ozarslan A (2014) ISRM suggested method for determining the abrasivity of rock by the CERCHAR abrasivity test. Rock Mech Rock Eng 47:261–266. https://doi.org/10.1007/s00603-013-0518-0
Aligholi S, Lashkaripour GR, Ghafoori M, Tarigh Azali S (2017) Evaluating the relationships between NTNU/SINTEF drillability indices with index properties and petrographic data of hard igneous rocks. Rock Mech Rock Eng 50(11):2929–2953. https://doi.org/10.1007/s00603-017-1289-9
Aligholi S, Lashkaripour GR, Ghafoori M (2018) Estimating engineering properties of igneous rocks using semi-automatic petrographic analysis. Bull Eng Geol Environ 78(1):2299–2314. https://doi.org/10.1007/s10064-018-1305-7
Ajalloeian R, Kamani M (2017) an investigation of the relationship between Los Angeles abrasion loss and rock texture for carbonate aggregates. Bull Eng Geol Environ 78(1):1555–1563. https://doi.org/10.1007/s10064-017-1209-y
Ajalloeian R, Mansouri H, Baradaran E (2016) Some carbonate rock texture effects on mechanical behavior, based on Koohrang tunnel data Iran. Bull Eng Geol Environ 76(1):295–307. https://doi.org/10.1007/s10064-016-0861-y
Apaydin OF, Yilmaz M (2021) Correlation of petrographic and chemical characteristics with strength and durability of basalts as railway aggregates determined by ballast fouling. Bull Eng Geol Environ 80:4197–4205. https://doi.org/10.1007/s10064-019-01654-4
ASTM (2005) Standard test method for laboratory determination of pulse velocities and ultrasonic elastic constants of rock. American Society for Testing Materials D 2845–08
Azzoni A, Bilo F, Rondena E, Zaninetti A (1996) Assessment of texture coefficient for different rock types and correlation with uniaxial compressive strength and rock weathering. Rock Mech Rock Eng 29:36–46. https://doi.org/10.1007/BF01019938
Basu A, Celestino T, Bortolucci A (2009) Evaluation of rock mechanical behaviors under uniaxial compression with reference to assessed weathering grades. Rock Mech Rock Eng 42:73–93. https://doi.org/10.1007/s00603-008-0170-2
Capik M, Yilmaz AO (2017) Correlation between Cerchar abrasivity index, rock properties, and drill bit lifetime. Arab J Geo Sci 10(15):1–12. https://doi.org/10.1007/s12517-016-2798-7
Cobanoglu I, Celik SB (2008) Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull Eng Geol Environ 67(1):491–498. https://doi.org/10.1007/s10064-008-0158-x
Comakli R, Cayirli S (2018) A correlative study on textural properties and crushability of rocks. Bull Eng Geol Environ 78:3541–3557. https://doi.org/10.1007/s10064-018-1357-8
Cowie S, Walton G (2018) The effect of mineralogical parameters on the mechanical properties of granitic rocks. Eng Geol 240:204–225. https://doi.org/10.1016/j.enggeo.2018.04.021
Dahl F (2003) DRI, BWI, CLI Standards. NTNU, Angleggs drift, Trondheim, Norway
Ersoy A, Waller MD (1995) Textural characterisation of rocks. Eng Geol 39(3–4):123–136. https://doi.org/10.1016/0013-7952(95)00005-Z
Fener M, Ince I (2012) Influence of orthoclase phenocrysts on point load strength of granitic rocks. Eng Geol 141–142:24–32. https://doi.org/10.1016/J.ENGGEO.2012.05.003
Friedman M, Perkins RD, Green SJ (1970) Observation of brittle-deformation features at the maximum stress of westerly granite and solenhofen limestone. Int J Rock Mech Min Sci Geomech Abstr 7(3):297–302. https://doi.org/10.1016/0148-9062(70)90043-4
Gajowniczek K, Zabkowski T, Szupiluk R (2014) Estimating the ROC curve and its significance for classification models assessment. Quantitative Methods in Economics 15(2):382–391
Gupta V, Sharma R (2012) Relationship between textural, petrophysical and mechanical properties of quartzite: a case study from northwestern Himalaya. Eng Geol 135–136:1–9. https://doi.org/10.1016/j.enggeo.2012.02.006
Hecht-Nielsen R (1987) Kolmogorov's mapping neural network existence theorem. In Proceedings, IEEE International Conference on Neural Networks (IEEE, New York, 1987)
Hemmati A, Ghafoori M, Moomivand H, Lashkaripour GR (2020) The effect of mineralogy and textural characteristics on the strength of crystalline igneous rocks using image-based textural quantification. Eng Geol 266:105467. https://doi.org/10.1016/j.enggeo.2019.105467
Howarth DF, Rowlands JC (1987) Quantitative assessment of rock texture and correlation with drillability and strength properties. Rock Mech Rock Eng 20:57–85. https://doi.org/10.1007/BF01019511
Hutchison CS (1974) Laboratory handbook of petrographic techniques. Wiley, New York
ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring, 1974–2006, suggested methods prepared by the commission o testing methods, International Society for Rock Mechanics, R. Ulusay and J. A. Hudson (Eds.), Compilation Arranged by The ISRM Turkish National Group, Ankara, Turkey
Johansson E, Miskovsky K, Bergknut M, Sachlova S (2016) Petrographic characteristics of intrusive rocks as an evaluation tool of their technical properties. Geological Society, London, Special Publications 416:217–227. https://doi.org/10.1144/SP416.19
Kamani M, Ajalloeian R (2019) Evaluation of engineering properties of some carbonate rocks trough corrected texture coefficient. Geotech Geol Eng 37:599–614. https://doi.org/10.1007/s10706-018-0630-8
Kamani M, Khaleghi Esfahani M, Ajalloeian R (2020) Prediction of carbonate aggregates properties through physical tests. Geotech Geol Eng 38:2169–2186. https://doi.org/10.1007/s10706-019-01155-x
Khandelwal M, Ranjith PG (2010) Correlating index properties of rocks with P-wave measurements. J Appl Geophys 71:1–5. https://doi.org/10.1016/j.jappgeo.2010.01.007
Middleton AP, Freestone IC, Leese MN (1985) Textural analysis of ceramic thin sections: evaluation of grain sampling procedures. Archaeometry 27(1):64–74. https://doi.org/10.1111/j.1475-4754.1985.tb00348.x
Minaeian B, Ahangari K (2013) Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab J Geosci 6:1925–1931. https://doi.org/10.1007/s12517-011-0460-y
Mishra DA, Srigyan M, Basu A, Rokade PJ (2015) Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int J Rock Mech Min Sci 80:418–424
Miskovsky K, Taborda Duarte M, Kou SQ, Lindqvist PA (2004) Influence of the mineralogical composition and textural properties on the quality of coarse aggregates. JMEPEG 13:144–150. https://doi.org/10.1361/10599490418334
Moradizadeh M, Cheshomi A, Ghafoori M, Trigh-Azali 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. https://doi.org/10.1016/j.ijrmms.2016.04.003
Omar M (2016) Empirical correlations for predicting strength properties of rocks-United Arab Emirates. Int J Geotech Eng. https://doi.org/10.1080/19386362.2016.1214339
Ozturk CA, Nasuf E (2013) Strength classification of rock material based on textural properties. Tunn Undergr Space Technol 37:45–54. https://doi.org/10.1016/j.tust.2013.03.005
Ozturk CA, Nasuf E, Kahraman E (2014) Estimation of rock strength from quantitative assessment of rock texture. J South Afr Inst Min Metall 114(6):471–480
Pappalardo G, Punturo R, Mineo S, Ortolano G, Castelli F (2016) Engineering geological and petrographic characterization of migmatites belonging to the Calabria-Peloritani Orogen (southern Italy). Rock Mech Rock Eng 49(4):1143–1160. https://doi.org/10.1007/s00603-015-0808-9
Peng J, Wong NY, The CI (2017) Influence of grain size heterogeneity on strength and micro-cracking behavior of crystalline rocks. J Geophys Res Solid Earth 122:1054–1073. https://doi.org/10.1002/2016JB013469
Prikryl R (2001) Some microstructural aspects of strength variation in rocks. Int J Rock Mech Min Sci 38(5):671–682. https://doi.org/10.1016/S1365-1609(01)00031-4
Prikryl R (2006) Assessment of rock geomechanical quality by quantitative rock fabric coefficients: limitations and possible source of misinterpretations. Eng Geol 87:149–162. https://doi.org/10.1016/j.enggeo.2006.05.011
Roduit N (2009) JMicroVision: image analysis toolbox for measuring and quantifying components of high-definition images. Version 1.2.7. Software available for free download. http://www.jmicrovision.com
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. https://doi.org/10.1007/s00603-013-0487-3
Rostami K, Hamidi JK, Nejati HR (2020) Use of rock microscale properties for introducing a cuttability index in rock cutting with a chisel pick. Arab J Geosci 13:960. https://doi.org/10.1007/s12517-020-05937-z
Sajid M, Arif M (2015) Reliance of physico-mechanical properties on petrographic characteristics: consequences from the study of Utla granites, north-west Pakistan. Bull Eng Geol Environ 74(4):1321–1330. https://doi.org/10.1007/s10064-014-0690-9
Sajid M, Coggan J, Arif M, Anderson J, Rollinson G (2016) Petrographic features as an effective indicator for the variation in strength of granites. Eng Geol 202:44–54. https://doi.org/10.1016/j.enggeo.2016.01.001
Singh TN, Verma AK (2012) Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 28:1–12. https://doi.org/10.1007/s00366-011-0210-5
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. https://doi.org/10.1016/j.ijrmms.2005.06.007
Sprunt ES, Brace WF (1974) Direct observation of microcavities in crystalline rocks. Int J Rock Mech Min Sci Geomech Abstr 11(4):139–150. https://doi.org/10.1016/0148-9062(74)92874-5
Streckeisen A (1976) To each plutonic rock its proper name. Earth Sci Rev 12:12–33. https://doi.org/10.1016/0012-8252(76)90052-0
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. https://doi.org/10.1016/j.enggeo.2012.11.005
Ter Berg P (1980) On the log linear Poisson and gamma model. Astin Bulletin 11:35–40
Thuro K (1997) Drillability prediction: geological influences in hard rock drill and blast tunneling, vol 86. Springer, Geol Rundsch, pp 426–438. https://doi.org/10.1007/s005310050151
Tugrul A, Zarif IH (1999) Correlation of mineralogical and textural characteristics with engineering properties of selected igneous rocks from Turkey. Eng Geol 51:303–317. https://doi.org/10.1016/S0013-7952(98)00071-4
Tumac D, Copur H, Balci C (2017) Investigation into the effects of textural properties on cuttability performance of a chisel tool. Rock Mech Rock Eng 51:1227–1248. https://doi.org/10.1007/s00603-017-1376-y
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. https://doi.org/10.1016/00137952(94)90029-9
Undul O (2016) Assessment of mineralogical and petrographic factors affecting petrophysical properties, strength and cracking processes of volcanic rocks. Eng Geol 210:10–22. https://doi.org/10.1016/j.enggeo.2016.06.001
Villeneuve MC (2008) Examination of geological influence on machine excavation of highly stressed tunnels in massive hard rock. Queen University Kingston, Ontario, Canada
Yesiloglu-Gultekin N, Sezer EA, Gokceoglu C, Bayhan H (2013) An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents. Expert Syst Appl 40(3):921–928. https://doi.org/10.1016/j.eswa.2012.05.048
Yilmaz NG, Goktan RM, Kibici Y (2011a) Relation between some quantitative petrographic characteristics and mechanical strength properties of igneous building stones. Int J Rock Mech Min Sci 48(3):506–513. https://doi.org/10.1016/j.ijrmms.2010.09.003
Yilmaz NG, Goktan RM, Kibici Y (2011b) An investigation of the petrographic and physico-mechanical properties of true granites influencing diamond tool wear performance, and development of a new wear index. Wear 271(5–6):960–969. https://doi.org/10.1016/j.wear.2011.04.007
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. https://doi.org/10.1016/j.enggeo.2007.10.009
Acknowledgements
Part of this research was funded by a grant from the Vice President of Research Affairs of Bu-Ali Sina University to M. Heidari. An original draft of the manuscript benefited from rendered editorial correction by H. Mohseni (Bu-Ali Sina University, Iran) and N. Mohseni (University of Lund, Sweden). We would also like to thank the anonymous reviewers for their critical reviews and constructive comments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declares no competing interests.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Karrari, S.S., Heidari, M., Hamidi, J.K. et al. Predicting geomechanical, abrasivity, and drillability properties in some igneous rocks using fabric features and petrographic indexes. Bull Eng Geol Environ 82, 124 (2023). https://doi.org/10.1007/s10064-023-03144-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10064-023-03144-0