Reçu le : 2019-04-22

Révisé le : 2019-07-12

Accepté le : 2019-11-27

Publié le : 2020-03-30

DOI :
https://doi.org/10.5802/crmeca.3

Mots clés: Uniaxial compressive strength (UCS), Indirect tests, Statistical analysis, Random forest algorithm

Révisé le : 2019-07-12

Accepté le : 2019-11-27

Publié le : 2020-03-30

Mots clés: Uniaxial compressive strength (UCS), Indirect tests, Statistical analysis, Random forest algorithm

@article{CRMECA_2020__348_1_3_0, author = {Min Wang and Wen Wan and Yanlin Zhao}, title = {Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model}, journal = {Comptes Rendus. M\'ecanique}, publisher = {Acad\'emie des sciences, Paris}, volume = {348}, number = {1}, year = {2020}, pages = {3-32}, doi = {10.5802/crmeca.3}, language = {en}, url = {comptes-rendus.academie-sciences.fr/mecanique/item/CRMECA_2020__348_1_3_0/} }

Min Wang; Wen Wan; Yanlin Zhao. Prediction of the uniaxial compressive strength of rocks from simple index tests using a random forest predictive model. Comptes Rendus. Mécanique, Tome 348 (2020) no. 1, pp. 3-32. doi : 10.5802/crmeca.3. https://comptes-rendus.academie-sciences.fr/mecanique/item/CRMECA_2020__348_1_3_0/

[1] Drillability prediction: geological influences in hard rock drill and blast tunneling, Geol. Rundsch., Tome 86 (1997), pp. 426-438 | Article

[2] Standard test method for unconfined compressive strength of intact rock core specimens, Soil and Rock, Building Stones: Annual Book of ASTM Standards 4.08, ASTM, Philadelphia, Pennsylvania, 1984

[3] Rock Characterisation Testing and Monitoring (E. T. Brown, ed.), Pergamon Press, Oxford, 1981

[4] The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006, Suggested Methods Prepared by the Commission on Testing Methods, International Society for Rock Mechanics (R. Ulusay; J. A. Hudson, eds.), ISRM Turkish National Group, Ankara, Turkey, 2007

[5] A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock, Eng. Appl. Artif. Intell., Tome 17 (2004), pp. 61-72 | Article

[6] Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method, Arab. J. Geosci., Tome 6 (2013), pp. 1925-1931 | Article

[7] The determination of uniaxial compressive strength from point load strength for pyroclastic rocks, Eng. Geol., Tome 170 (2014), pp. 33-42 | Article

[8] Estimation of elastic constant of rocks using an ANFIS approach, Appl. Soft Comput., Tome 12 (2012) no. 1, pp. 40-45 | Article

[9] A neuro-genetic network for predicting uniaxial compressive strength of rocks, Geotech. Geol. Eng., Tome 30 (2012) no. 4, pp. 1053-1062 | Article

[10] Predicting the uniaxial compressive strength and elastic modulus of a fault breccia from texture coefficient, Rock Mech. Rock Eng., Tome 42 (2009), pp. 117-127 | Article

[11] Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate, Int. J. Rock Mech. Min. Sci., Tome 41 (2004) no. 5, pp. 717-729 | Article

[12] Estimating the uniaxial compressive strength of a volcanic bimrock, Int. J. Rock Mech. Min. Sci., Tome 43 (2006) no. 4, pp. 554-561 | Article

[13] Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness, Int. J. Rock Mech. Min. Sci., Tome 36 (1999) no. 1, pp. 29-39 | Article

[14] Prediction of compression strength from other rock properties, Q. Colo. Sch. Mines, Tome 59 (1964) no. 4b, pp. 623-640

[15]

(“Engineering classification and index properties for intact rock”, Air Force Weapons Lab. Tech. Report, AFWL-TR 65-116., Kirtland Base, New Mexico, 1966)[16] Point-load strength test, Int. J. Rock Mech. Min. Sci., Tome 9 (1972) no. 6, pp. 669-697 | Article

[17] Point load test in geotechnical practice, Eng. Geol., Tome 9 (1975) no. 1, pp. 1-11 | Article

[18] Application of point-load index test to strength determination of rock and proposals for new size-correction chart, Proc. 21st US Symp. Rock Mech., Rolla, Missouri (D. A. Summers, ed.), 1980, pp. 543-553

[19] A rational approach to the point load test, Proc. 3rd Australian-New Zealand Geomechanics Conference, Tome 2, 1980, pp. 35-39

[20] Determination of some engineering properties of weak rocks, Proc. Int. Symp. Weak Rock, Tokyo, 1981, pp. 21-24

[21] The influence of core sample geometry on the axial point-load test, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., Tome 20 (1983) no. 6, pp. 291-295 | Article

[22] Rock compressive strength, Colliery Eng., Tome 41 (1964), pp. 287-292

[23] A systematic determination of engineering criteria for rocks, Bull. Assoc. Eng. Geol., Tome 11 (1973), pp. 235-245

[24] Assessment of the degree of weathering in granite using petrographic and physical index tests, Proc. Int. Symp. On Deterioration and Protection of Stone Monuments. Unesco, Paris, 1978, pp. 1-35

[25] Correlations of Rock Index Values with Engineering Properties and the Classification of Intact Rock, FHWA, Washington, DC, 1979

[26] Bursting liability indices of coal, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., Tome 17 (1980), pp. 157- 161

[27] The application of strength and deformation index testing to the stability assessment of coal measure excavations, Proc. 24th US Symp. Rock Mech., Texas A&M University, Texas, 1983, pp. 599-609

[28] Schmidt hammer rebound data for estimation of large-scale in situ coal strength, Int. J. Rock Mech. Min. Sci., Tome 21 (1984), pp. 39-42 | Article

[29] Suggested method for determining point load strength, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., Tome 22 (1985) no. 2, pp. 53-60

[30] Use of the Schmidt hammer for rock and coal testing, 26th US Symp. on Rock Mech., Rapid City, 1985, pp. 549-555

[31] Empirical strength indices of Indian coals-an investigation, Proc. 27th US Symp. Rock Mech., Balkema, Rotterdam, 1986, pp. 59-61

[32] Correlation between unconfined compressive and point load strength for Appalachian rocks, Proc. 30th US Symp. Rock Mech., Morgantown, 1989, pp. 461-468

[33] Rock index properties for geo-engineering in underground development, Min. Eng., Tome 41 (1989) no. 2, pp. 106-110

[34] Evaluation of empirical methods for measuring the uniaxial compressive strength of rock, Int. J. Rock Mech. Min. Sci., Tome 27 (1990) no. 6, pp. 495-503 | Article

[35] Correlating Schmidt hardness with compressive strength and young’s modulus of carbonate rocks, Bull. Int. Assoc. Eng. Geol., Tome 42 (1990), pp. 75-84 | Article

[36] Use of Schmidt hammer for estimating mechanical properties of weak rock, Proc. 6th Int. Assoc. Eng. Geol. Congr., Balkema, Rotterdam, 1990, pp. 511-519

[37] Point load-uniaxial compressive strength correlation, Proc. 7th ISRM Congress, Aachen, Germany, Tome 1, 1991, pp. 637-639

[38] Point-load strength: an index for classification of rock material, Bull. Int. Assoc. Eng. Geol., Tome 44 (1991), pp. 27-33 | Article

[39] Problems and promises of index testing of rocks, Proc. 33rd US Symp. Rock Mech., Sante Fe, NM, Balkema, Rotterdam, 3–5, 1992, pp. 879-888

[40] An example of artificial neural network (ANN) application for indirect estimation of rock parameters, Rock Mech. Rock Eng., Tome 41 (2008) no. 5, pp. 781-795 | Article

[41] Correlation between point load index and uniaxial compressive strength for different rock types, Rock Mech. Rock Eng., Tome 45 (2012), pp. 259-264 | Article

[42] Prediction of uniaxial compressive strength of sandstones using petrography-based models, Eng. Geol., Tome 96 (2008) no. 3–4, pp. 141-158 | Article

[43] Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks, Int. J. Rock Mech. Min., Tome 20 (2010), pp. 41-46

[44] Application of neural network technique for prediction of uniaxial compressive strength using reservoir formation properties, Int. J. Rock Mech. Min. Sci., Tome 56 (2012), pp. 100-111 | Article

[45] Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks, Environ. Earth Sci., Tome 68 (2012) no. 3, pp. 807-819 | Article

[46] Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks, Int. J. Numer. Anal. Methods, Tome 36 (2012), pp. 1636-1650 | Article

[47] Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models, Int. J. Rock Mech. Min. Sci., Tome 46 (2009) no. 4, pp. 803-810 | Article

[48] An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining, Neural Comput. Appl., Tome 24 (2012) no. 1, pp. 233-241 | Article

[49] Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system, Eng. Geol., Tome 160 (2013), pp. 54-68 | Article

[50] Prediction of uniaxial compressive strength of some sedimentary rocks by fuzzy and regression models, Geotech. Geol. Eng., Tome 36 (2018), pp. 401-412 | Article

[51] Knowledge-based model of material behaviour with neural networks, J. Eng. Mech., Tome 117 (1991) no. 1, pp. 132-153 | Article

[52] Artificial Neural System: Foundation, Paradigms, Applications and Implementations, Pergamon, New York, 1990

[53] Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks, Int. J. Rock Mech. Min. Sci., Tome 38 (2001) no. 2, pp. 269-284 | Article

[54] Predicting of compressive and tensile strength of limestone via genetic programming, Expert Syst. Appl., Tome 35 (2008), pp. 111-123 | Article

[55] Improving the prediction of the UCS by Equotip readings using statistical and neural network models, Mem. Centre Eng. Geol. Net, Tome 162 (1997), 127 pages

[56] Estimating the unconfined compressive strength and elastic modulus of a fault breccia mixture of weak rocks and strong matrix, Int. J. Rock Mech. Min. Sci., Tome 43 (2006), pp. 1277-1287 | Article

[57] Estimation of strength parameters of rock using artificial neural networks, Bull. Eng. Geol. Environ., Tome 69 (2010), pp. 599-606 | Article

[58] Numerical methods in rock mechanics, Int. J. Rock Mech. Min. Sci., Tome 39 (2010), pp. 409-427 | Article

[59] The effect of initial weights on premature saturation in back-propagation learning, Proc. IEEE Int. Joint Conf. on Neural Networks, Seattle, WA, USA, 18–21, 1991, pp. 765-770 | Article

[60] Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization, Appl. Acoust., Tome 80 (2014), pp. 57-67 | Article

[61] Fuzzy sets, Inf. Control, Tome 8 (1965), pp. 338-353 | Article | Zbl 0139.24606

[62] ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern., Tome 23 (1993), pp. 665-685 | Article

[63] Neuro-Fuzzy and Soft Computing, Prentice-Hall, Upper Saddle River, 1997

[64] Modeling tunnel boring machine performance by neuro-fuzzy methods, Tunn. Undergr. Space Technol., Tome 15 (2000) no. 3, pp. 260-269

[65] Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system, Environ. Geol., Tome 56 (2008), pp. 97-107 | Article

[66] An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models, Appl. Soft Comput., Tome 24 (2014), pp. 126-134 | Article

[67] A new optimizer using particle swarm theory, Proc 6th Int. Symp. on Micro Machine and Human Science, Nagoya, Japan, 4–6, 1995, pp. 39-43 | Article

[68] Particle swarms for feed forward neural net training, Proc. IEEE Int. Joint Conf. on Neural Networks, Honolulu, HI, USA, 12–17, 2002, pp. 1895-1899

[69] Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization, Arab. J. Geosci., Tome 7 (2014) no. 12, pp. 5383-5396 | Article

[70] Where and why artificial neural networks are applicable in civil engineering, J. Comput. Civ. Eng., Tome 8 (1994), p. 129-130

[71] Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances, Int. J. Rock Mech. Min. Sci., Tome 62 (2013) no. 9, pp. 113-122 | Article

[72] Prediction of the strength of mineral admixture concrete using multivariable regression analysis and an artificial neural network, Expert Syst. Appl., Tome 38 (2011), pp. 9609-9618 | Article

[73] Development of optimal fuzzy models for predicting the strength of intact rocks, Comput. Geosci., Tome 54 (2013), pp. 107-112 | Article

[74] A novel approach for blast-induced flyrock prediction based on imperialist competitive algorithm and artificial neural network, Sci. World J., Tome 2014 (2014) (article ID 643715) | Article

[75] Suggested methods for the quantitative description of discontinuities in rock masses, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., Tome 15 (1978), pp. 319-368

[76] Gene selection and classification of micro array data using random forest, BMC Bioinforma., Tome 7 (2006), 3 pages | Article

[77] Random forests, Mach. Learn., Tome 45 (2001), pp. 5-32 | Article | Zbl 1007.68152

[78] Bagging predictors, Mach. Learn., Tome 24 (1996), pp. 123-140 | Article | Zbl 0858.68080

[79] An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., Tome 67 (2012), pp. 93-104 | Article

[80] Refining a reconnaissance soil map by calibrating regression models with data from the same map (Normandy, France), Geoderma Regional., Tome 1 (2014), pp. 21-30 | Article

[81] Soil organic carbon concentrations and stocks on Barro Colorado Island - digital soil mapping using random forests analysis, Geoderma, Tome 146 (2008), pp. 102-113 | Article

[82] Digital mapping of soil organic matter stocks using random forest modeling in a semi-arid steppe ecosystem, Plant Soil, Tome 340 (2011), pp. 7-24 | Article

[83] Newer classification and regression tree techniques: bagging and randomforests for ecological prediction, Ecosystems, Tome 9 (2006), pp. 181-199 | Article

[84] Random forests for classification in ecology, Ecology, Tome 88 (2007), pp. 2783-2792 | Article

[85] Random forest: a classification and regression tool for compound classification and QSAR modeling, J. Chem. Inf. Comput. Sci., Tome 43 (2003), pp. 1947-1958 | Article

[86] Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks, Surgery, Tome 149 (2011) no. 1, pp. 87-93 | Article

[87] Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models, Amino Acids, Tome 47 (2015) no. 7, pp. 1485-1493 | Article

[88] Using random forest to model the domain applicability of another random forest model, J. Chem. Inf. Model., Tome 53 (2013) no. 11, pp. 2837-2850 | Article

[89] Predition of RNA-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature, Proteins-Struct. Funct. Bioinform., Tome 79 (2011) no. 4, pp. 1230-1239 | Article

[90] Fault diagnosis in spur gears based on genetic algorithm and random forest, Mech. Syst. Signal Process., Tome 70–71 (2016), pp. 87-103 | Article

[91] Test Methods for Ultra Violet Velocities Determination, American Society for Testing and Materials, 1983, D2845 pages

[92] Suggested methods for determining the uniaxial compressive strength and deformability of rock materials, Int. J. Rock Mech. Min. Sci. Geomech. Abstr., Tome 16 (1979), pp. 135-140

[93] Study on uniaxial compressive strength, point load strength index, dynamic and physical properties of serpentinites from Central Greece: test results and empirical relations, Eng. Geol., Tome 108 (2009), pp. 199-207 | Article

[94] A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition, Eng. Geol., Tome 66 (2002), pp. 39-51 | Article

[95] Applying soft computing methods to predict the uniaxial compressive strength of rock from schmidt hammer rebound values, Comput. Geosci., Tome 21 (2017) no. 1, pp. 1-17 | MR 3671649

[96] A comparative evaluation of rock strength measures, Int. J. Rock Mech. Min. Sci., Tome 21 (1984), pp. 233-248 | Article

[97] Prediction of engineering properties of a selected litharenite sandstone from its petrographic characteristics using correlation and multivariate statistical techniques, Eng. Geol., Tome 38 (1994), pp. 135-157 | Article

[98] Genetic algorithms and simulated annealing: a marriage proposal, Int. Symp. Neural Netw., Tome 2 (1993), pp. 1104-1109 | Article

[99] Point load test on meta-sedimentary rocks and correlation to UCS and BTS, Rock Mech. Rock Eng., Tome 46 (2013) no. 4, pp. 889-896 | Article

[100] Rock strength as a metric of welding intensity in pyroclastic deposits, Eur. J. Mineral., Tome 15 (2003), pp. 855-864 | Article

[101] Considerations on strength of intact sedimentary rocks, Eng. Geol., Tome 72 (2004), pp. 261-273 | Article

[102] The influence of porosity on tensile and compressive strength of porous chalk, Rock Mech. Rock Eng., Tome 37 (2004) no. 4, pp. 331-341 | Article

[103] Development of a new method for estimating the indirect uniaxial compressive strength of rock using Schmidt hammer, BHM Berg- Huettenmaenn Monatsh, Tome 156 (2011) no. 4, pp. 142-146 | Article

[104] Uniaxial compressive strength and point load strength, Int. J. Rock Mech. Min. Sci., Tome 33 (1996), pp. 183-188 | Article

[105] Schmidt sertlik cekici kullanılarak tahmin edilen tek eksenli basınç dayanımı verilerinin guvenirligi uzerine bir degerlendirme, Jeol. Muh., Tome 48 (1996), pp. 78-81

[106] Correlating uniaxial compressive strength with Schmidt hammer rebound number, point load index, Young’s modulus, and mineralogy of gabbros and basalts (Northern Greece), Bull. Eng. Geol., Tome 54 (1996), pp. 3-11

[107] Basınc direnci tahmininde Schmidt venokta yuk indeksi kullanmanın guvenirligi, KTU Jeoloji Muhendisligi Bolumu 30. Yıl Sempozyumu BildirilerKitabı, Trabzon (S. Korkmazve; M. Akcay, eds.), 1996, pp. 362-369

[108] The point load test for weak rock in dredging applications, Int. J. Rock Mech. Min. Sci., Tome 34 (1997) no. 3–4, 702 pages

[109] Correlation of mineralogical and textural, characteristics with engineering properties of selected granitic rocks from Turkey, Eng. Geol., Tome 51 (1999), pp. 303-317 | Article

[110] Evaluation of mechanical rock properties using a Schmidt hammer, Int. J. Rock Mech. Min. Sci., Tome 37 (2000), pp. 723-728 | Article

[111] Evaluation of the block punch index test with particular reference to the size effect, failure mechanism and its effectiveness in predicting rock strength, Int. J. Rock Mech. Min. Sci., Tome 38 (2001), pp. 1091-1111 | Article

[112] Evaluation of simple methods for assessing the uniaxial compressive strength of rock, Int. J. Rock Mech. Min. Sci., Tome 38 (2001), pp. 981-994 | Article

[113] Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey), Eng. Geol., Tome 66 (2002), pp. 211-219 | Article

[114] Estimation of rock physicomechanical properties using hardness methods, Eng. Geol., Tome 71 (2004), pp. 281-288 | Article

[115] Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks, Int. J. Rock Mech. Min. Sci., Tome 5 (2004), pp. 871-875 | Article

[116] Correlation between Schmidt hardness, uniaxial compressive strength and Young’s modulus for andesites, basalts and tuffs, Bull. Eng. Geol. Environ., Tome 63 (2004), pp. 141-148 | Article

[117] The Schmidt hammer in rock material characterization, Eng. Geol., Tome 81 (2005), pp. 1-14 | Article

[118] The relationship between effective porosity, uniaxial compressive strength and sonic velocity of intact Borrowdale volcanic group core samples from Sellafield, Geotech. Geol. Eng., Tome 23 (2005) no. 6, pp. 793-809 | Article

[119] The effect of porosity on the relation between uniaxial compressive strength and point load index, Int. J. Rock Mech. Min. Sci., Tome 42 (2005) no. 4, pp. 584-589 | Article

[120] A comparative evaluation of indirect methods to estimate the compressive strength of rocks, Rock Mech. Rock Eng., Tome 38 (2005) no. 4, pp. 329-343 | Article

[121] Predicting uniaxial compressive strength by point load test: significance of cone penetration, Rock Mech. Rock Eng., Tome 39 (2006) no. 5, pp. 483-490 | Article

[122] Correlation between uniaxial compressive strength and point load index for salt-range rocks, Pak. J. Eng. Appl. Sci., Tome 1 (2007), pp. 1-8

[123] Estimation of rock engineering properties using hardness tests, Eng. Geol., Tome 90 (2007), pp. 138-147 | Article

[124] The uniaxial compressive strength of soft rock, Civ. Eng. Dimens., Tome 9 (2007), pp. 9-14

[125] Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity, Bull. Eng. Geol. Environ., Tome 67 (2008), pp. 491-498 | Article

[126] A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength, Bull. Eng. Geol. Environ., Tome 67 (2008), pp. 17-22 | Article

[127] Determination of mechanical properties of rocks using simple methods, Bull. Eng. Geol. Environ., Tome 67 (2008), pp. 237-244 | Article

[128] Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer, Bull. Eng. Geol. Environ., Tome 68 (2009), pp. 55-63 | Article

[129] Index properties and strength variation controlled by microstructure for sedimentary rocks, Eng. Geol., Tome 97 (2009), pp. 80-90 | Article

[130] Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test, Int. J. Geomech., Tome 9 (2009) no. 1, pp. 14-19 | Article

[131] Non-destructive testing of some higher Himalayan rocks in the Satluj Valley, Bull. Eng. Geol. Environ., Tome 68 (2009), pp. 409-416 | Article

[132] Correlating static properties of coal measures rocks with P-wave velocity, Int. J. Coal Geol., Tome 79 (2009), pp. 55-60 | Article

[133] Predicting the relationships between brittleness and mechanical properties (UCS, TS and SH) of rocks, Sci. Res. Essays, Tome 5 (2010), pp. 2107-2118

[134] Application of Schmidt rebound number for estimating rock strength under specific geological conditions, J. Min. Sci., Tome 1 (2010) no. 2, pp. 1-8

[135] P-wave velocity test for assessment of geotechnical properties of some rock materials, Bull. Mater. Sci., Tome 34 (2011) no. 4, pp. 947-953 | Article

[136] Physical and mechanical properties of Gokcseda: Imbros (NE Aegean Sea) Island andesites, Bull. Eng. Geol. Environ., Tome 69 (2011), pp. 321-324 | Article

[137] Correlating wave velocities with physical, mechanical properties and petrographic characteristics of peridotites from the central Greece, Geotech. Geol. Eng., Tome 29 (2011) no. 6, pp. 1049-1062 | Article

[138] Correlations Between Index Properties and Unconfined Compressive Strength of Weathered Ocala Limestone, University of North Florida, Jacksonville, 2011, 142 pages

[139] Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing, Rock Mech. Rock Eng., Tome 45 (2012) no. 2, pp. 265-273 | Article

[140] Use of the block punch test to predict the compressive and tensile strengths of rocks, Int. J. Rock Mech. Min. Sci., Tome 51 (2012), pp. 119-127 | Article

[141] Relationship between point load strength index and uniaxial compressive strength of hydrothermally altered soft rocks, Int. J. Rock Mech. Min. Sci., Tome 50 (2012), pp. 147-157 | Article

[142] Predicting the compressive and tensile strength of rocks from indentation hardness index, J. South. Afr. Ins. Min. Metall., Tome 112 (2012) no. 5, pp. 331-339

[143] Correlating P-wave velocity with the physico-mechanical properties of different rocks, Pure Appl. Geophys., Tome 170 (2013), pp. 507-514 | Article

[144] Correlation between unconfined compressive strength and indirect tensile strength of limestone rock samples, Electr. J. Geotech. Eng., Tome 18 (2013), pp. 1737-1746

[145] Statistical method for assessing the uniaxial compressive strength of carbonate rock by Schmidt hammer tests performed on core samples, Rock Mech. Rock Eng., Tome 46 (2013) no. 1, pp. 199-206 | Article

[146] Using the Schmidt hammer on rock mass characteristic in sedimentary rock at Tutupan Coal Mine, Procedia. Earth Planet. Sci., Tome 6 (2013), pp. 390-395 | Article

[147] Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach, Bull. Eng. Geol. Environ., Tome 74 (2015) no. 3, pp. 745-757 | Article

[148] An adaptive neurofuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite, Bull. Eng. Geol. Environ., Tome 74 (2015) no. 4, pp. 1301-1319 | Article

[149] Correlation of Schmidt rebound hardness with uniaxial compressive strength and P-wave velocity of rock materials, Arab. J. Sci. Eng., Tome 40 (2015) no. 7, pp. 1897-1906 | Article

[150] Prediction of the strength and elasticity modulus of granite through an expert artificial neural network, Arab. J. Geosci., Tome 9 (2016), 48 pages | Article

[151] Rock strength assessment based on regression tree technique, Eng. Comput., Tome 32 (2016), pp. 343-354 | Article

[152] Application of statistical methods for predicting uniaxial compressive strength of limestone rocks using nondestructive tests, Acta Geotechnica, Tome 12 (2017) no. 2, pp. 1-13 | Article

[153] Estimation of uniaxial compressive strength of North Algeria sedimentary rocks using density, porosity, and Schmidt hardness, Arab. J. Geosci., Tome 10 (2017), 383 pages | Article

[154] A validation study for the estimation of uniaxial compressive strength based on index tests, Rock Mech. Rock Eng., Tome 51 (2018), pp. 2289-2297 | Article

[155] Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity, Rock Mech. Rock Eng., Tome 39 (2006) no. 1, pp. 45-57 | Article

[156] Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees, Eng. Geol., Tome 99 (2008), pp. 51-60 | Article

[157] Estimating the uniaxial compressive strength of some clay-bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models, Expert Syst., Tome 26 (2009), pp. 176-190 | Article

[158] Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming, Neural Comput. Appl., Tome 18 (2009), pp. 1031-1041 | Article

[159] Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network, Appl. Soft Comput., Tome 11 (2011), pp. 2587-2594 | Article

[160] Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks, Int. J. Rock Mech. Min. Sci., Tome 63 (2013), pp. 159-169 | Article

[161] Modeling uniaxial compressive strength of building stones using non-destructive test results as neutral networks input parameters, Constr. Build. Mater., Tome 47 (2013), pp. 1010-1019 | Article

[162] Selection of regression models for predicting strength and deformability properties of rocks using GA, Int. J. Min. Sci. Technol., Tome 23 (2013), pp. 495-501 | Article

[163] Application of support vector machines and relevance vector machines in predicting uniaxial compressive strength of volcanic rocks, J. Afr. Earth Sci., Tome 100 (2014), pp. 634-644 | Article

[164] Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones, Arab. J. Geosci., Tome 8 (2015) no. 5, pp. 2889-2897 | Article

[165] Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artifical neural networks, Measurement, Tome 60 (2015), pp. 50-63 | Article

[166] Prediction of the uniaxial compressive strength of sandstone using various modeling techniques, Int. J. Rock Mech. Min. Sci., Tome 85 (2016), pp. 174-186 | Article

[167] Predicting elastic properties of intact rocks from index tests using multiple regression modelling, Int. J. Rock Mech. Min. Sci., Tome 42 (2005), pp. 323-330 | Article

[168] The effect of Schmidt hammer type on uniaxial compressive strength prediction of rock, Int. J. Rock Mech. Min. Sci., Tome 44 (2007), pp. 299-307 | Article

[169] Estimating the unconfined compressive strength of intact rocks from Equotip hardness, Bull. Eng. Geol. Environ., Tome 67 (2008), pp. 23-29 | Article

[170] Nail penetration test for determining the uniaxial compressive strength of rock, Int. J. Rock Mech. Min. Sci., Tome 47 (2010) no. 2, pp. 265-271 | Article

[171] Function identification for the intrinsic strength and elastic properties of granitic rock via genetic programming (GP), Comput. Geosci., Tome 37 (2011) no. 9, pp. 1318-1323 | Article

[172] Relationship between textural, petrophysical and mechanical properties of quartzites: a case study from northwestern Himalaya, Eng. Geol., Tome 135–136 (2012), pp. 1-9 | Article

[173] Soft computing method for assessment of compressional wave velocity, Scientia Iranica, Tome 19 (2012) no. 4, pp. 1018-1024 | Article

[174] Regression analysis and ANN models to predict rock properties from sound levels produced during drilling, Int. J. Rock Mech. Min. Sci., Tome 58 (2013), pp. 61-72 | Article

[175] Assessment of relationships between drilling rate index and mechanical properties of rocks, Tunn. Undergr. Space Tech., Tome 33 (2013), pp. 46-53 | Article

[176] Predictive model for uniaxial compressive strength for Grade III granitic rocks from Macao, Eng. Geol., Tome 199 (2015), pp. 28-37 | Article

[177] Drilling rate prediction of an open pit mine using the rock mass drillability index, Int. J. Rock Mech. Min. Sci., Tome 73 (2015), pp. 130-138 | Article

[178] Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests, Int. J. Rock Mech. Min. Sci., Tome 80 (2015), pp. 418-424 | Article

[179] Estimation of strength characteristics of different Himalayan rocks from Schmidt hammer rebound, point load index, and compressional wave velocity, Bull. Eng. Geol. Environ., Tome 74 (2015), pp. 521-533 | Article

[180] Correlating physico-mechanical properties of intact rocks with P-wave velocity, Acta Geod. Geophys., Tome 51 (2016), pp. 571-582 | Article

[181] Influences of petrographic and textural properties on the strength of very strong granite rocks, Environ. Earth Sci., Tome 75 (2016) no. 22, 1461 pages | Article

[182] Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances, Eng. Comput., Tome 32 (2016), pp. 189-206 | Article

[183] The use of index tests to determine the mechanical properties of crushed aggregates from precambrian basement complex rocks, Ado-Ekiti, SW Nigeria, J. Afr. Earth Sci., Tome 129 (2017), pp. 659-667 | Article

[184] Prediction of mechanical behaviour from mineralogical composition of Sakesar limestone, Central Salt Range, Pakistan, Bull. Eng. Geol. Environ., Tome 76 (2017), pp. 601-615 | Article

[185] Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks, Bull. Eng. Geol. Environ., Tome 77 (2018), pp. 331-343 | Article