Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances

International Journal of Rock Mechanics and Mining Sciences

Volumn 62, Issue , 2013, Pages 113-122

  Yesiloglu Gultekin, N ^a   Gokceoglu, C ^b   Sezer, E A ^b  

^a AKSARAY UNIVERSITY   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

Author keywords

ANFIS; ANN; Granite; Multiple regression; Prediction method; Uniaxial compressive strength

Indexed keywords

ACOUSTIC WAVE VELOCITY; COMPRESSIVE STRENGTH; FORECASTING; FUZZY INFERENCE; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; GRANITE; SEISMIC WAVES; WAVE PROPAGATION;

ADAPTIVE NEURO FUZZY INFERENCE SYSTEM; ADAPTIVE NEURO-FUZZY INFERENCE; GRANITIC ROCKS; MULTIPLE REGRESSIONS; NEURO-FUZZY INFERENCE SYSTEMS; P-WAVE VELOCITY; PERFORMANCE; PREDICTION METHODS; UNIAXIAL COMPRESSIVE STRENGTH; VELOCITY MODELING;

TENSILE STRENGTH;

ARTIFICIAL NEURAL NETWORK; COMPRESSIVE STRENGTH; FUZZY MATHEMATICS; GRANITE; MULTIPLE REGRESSION; P-WAVE; PERFORMANCE ASSESSMENT; PREDICTION; ROCK MECHANICS; SEISMIC VELOCITY; TENSILE STRENGTH; UNIAXIAL STRENGTH;

EID: 84879335515     PISSN: 13651609     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijrmms.2013.05.005     Document Type: Article

References (101)

1. Verma AK, Singth TN. A neuro-fuzzy approach for prediction of longitudinal wave velocity. Neural Comput Appl 2013;22(7-9):1685-93.
2. Gokceoglu C., Aksoy H. New approaches to the characterization of clay-bearing, densely jointed and weak rock masses. Eng Geol 2000, 58:1-23.
3. Koncagül E.C., Santi P.M. Predicting the unconfined compressive strength of the Breathitt shale using slake durability, Shore hardness and rock structural properties. Int J Rock Mech Min Sci 1999, 36(2):139-153.
4. Zorlu K., Gokceoglu C., Ocakoglu F., Nefeslioglu H.A., Acikalin S. Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 2008, 96(3-4):141-158.
5. Gokceoglu C., Sonmez H., Zorlu K. Estimating the uniaxial compressive strength of some clay bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models. Expert Syst 2009, 26(2):176-190.
6. Tugrul A., Zarif I.H. Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey. Eng Geol 1999, 51:303-317.
7. Tan X., Chen W., Yang J., Cao J. Laboratory investigations on the mechanical properties degradation of granite under freeze-thaw cycles. Cold Reg Sci Tech 2011, 68:130-138.
8. Sousa L.M.O., Rio L.M.S., Calleja L., Argandona V.G.R., Rey A.R. Influence of microfractures and porosity on the physico-mechanical properties and weathering of ornamental granites. Eng Geol 2005, 77:153-168.
9. Ceryan S., Zorlu K., Gokceoglu C., Temel A. The use of cation packing index for characterizing the weathering degree of granitic rocks. Eng Geol 2008, 98:60-74.
10. Martins F.F., Begonha A., Braga M.A.S. Prediction of the mechanical behavior of the Oporto granite using Data Mining techniques. Expert Syst Appl 2012, 39:8778-8783.
11. Begonha A., Sequeira Braga M.A. Weathering of the Oporto granite: geotechnical and physical properties. Catena 2002, 49:57-76.
12. Dagdelenler G., Sezer E.A., Gokceoglu C. Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters. Expert Syst Appl 2011, 38:7476-7485.
13. Gokceoglu C., Zorlu K., Ceryan S., Nefeslioglu H.A. A comparative study on indirect determination of degree of weathering of granites from some physical and strength parameters by two soft computing techniques. Mater Charact 2009, 60:1317-1327.
14. Bieniawski Z.T. Rock mass classification in rock engineering. Exploration for rock engineering 1976, 97-106. Balkema, Cape Town. Z.T. Bieniawski (Ed.).
15. Barton N.R., Lien R., Lunde J. Engineering classification of rock masses for the design of tunnel support. Rock Mech 1974, 6(4):189-239.
16. Yesiloglu-Gultekin N., Sezer E.A., Gokceoglu C., Bayhan H. 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 2013, 40(3):921-928.
17. Sharma P.K., Singh T.N. A correlation between P-wave velocity, impact strength index, slake durability index and uniaxial compressive strength. Bull Eng Geol Environ 2008, 67:17-22.
18. Yagiz S. P-wave velocity test for assessment of geotechnical properties of some rock materials. Bull Mater Sci 2011, 34(4):947-953.
19. Broch E., Franklin J.A. The point load strength test. Int J Rock Mech Min Sci 1972, 28:669-697.
20. Bieniawski Z.T. Point load test in geotechnical practice. Eng Geol 1975, 9(1):1-11.
21. Pells P.J.N. The use of the point load test in predicting the compressive strength of rock materials. Aust Geomech J 1975, 65(1):54-56.
22. Norbury D.R. The point load test. Eng Geol 1986, 2:325-329.
23. Hawkins A.B. Aspects of rock strength. Bull Eng Geol Environ 1998, 57(1):17-30.
24. D'Andrea D.V., Fisher R.L., Fogelson D.E. Prediction of compression strength from other rock properties. Colo Sch Mines Q 1964, 59(4B):623-640.
25. Cargill J.S., Shakoor A. Evaluation of empirical methods for measuring the uniaxial compressive strength of rock. Int J Rock Mech Min Sci 1990, 27:495-503.
26. Singh V.K., Singh D.P. Correlation between point load index and compressive strength for quartzite rocks. Geo Tech Geol Eng 1993, 11:269-272.
27. Kahraman S. Evaluation of simple methods for assessing the uniaxial compressive strength of rock. Int J Rock Mech Min Sci 2001, 38:981-994.
28. Tsiambaos G., Sabatakakis N. Considerations on strength of intact sedimentary rocks. Eng Geol 2004, 72:261-273.
29. Kahraman S., Gunaydin O., Fener M. The effect of porosity on the relation between uniaxial compressive strength and point load index. Int J Rock Mech Min Sci 2005, 42(4):584-589.
30. Çobanoǧlu İ., Çelik S.B. Estimation of uniaxial compressive strength from point load strength, Schmidt hardness and P-wave velocity. Bull Eng Geol Environ 2008, 67:491-498.
31. Dinçer İ., Acar A., Ural S. Estimation of strength and deformation properties of quaternary caliche deposits. Bull Eng Geol Environ 2008, 67:353-366.
32. Diamantis K., Gartzos E., Migiros G. 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 2009, 108:199-207.
33. Kahraman S., Gunaydin O. The effect of rock classes on the relation between uniaxial compressive strength and point load index. Bull Eng Geol Environ 2009, 68(3):345-353.
34. Basu A., Kamran M. Point load test on schistose rocks and its applicability in predicting uniaxial compressive strength. Int J Rock Mech Min Sci 2010, 47:823-828.
35. Singh T.N., Kainthola A., Venkatesh A. Correlation between point load index and Uniaxial compressive strength for different rock types. Rock Mech Rock Eng 2012, 45:259-264.
36. Heidari M., Khanlari G.R., Kaveh M.T., Kargarian S. Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing. Rock Mech Rock Eng 2012, 45:265-273.
37. Singh T.N., Dubey R.K. A study of transmission velocity of primary wave (P-wave) in Coal Measures sandstone. J Sci Ind Res 2000, 59:482-486.
38. Singh T.N., Kanchan R., Saigal K., Verma A.K. Prediction of P-wave velocity and anisotropic properties of rock using Artificial Neural Networks technique. J Sci Ind Res 2004, 63(1):32-38.
39. Andreev G.E. A review of the Brazilian test for rock tensile strength determination. Part I: calculation formula. Min Sci Technol 1991, 13(3):445-456.
40. Chen C.S., Pan E., Amadei B. Determination of deformability and tensile strength of anisotropic rock using Brazilian tests. Int J Rock Mech Min Sci 1998, 35(1):43-61.
41. Claesson J., Bohloli B. Brazilian test: stress field and tensile strength of anisotropic rocks using an analytical solution. Int J Rock Mech Min Sci 2002, 39(8):991-1004.
42. Çanakci H., Pala M. Tensile strength of basalt from a neural network. Eng Geol 2007, 94(1-2):10-18. 16.
43. Baykasoǧlu A., Güllü H., Çanakçi H., Özbakir L. Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 2008, 35(1-2):111-123.
44. Gokceoglu C., Zorlu K. A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock. Eng Appl Artif Intell 2004, 17:61-72.
45. Sulukcu S., Ulusay R. 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 2001, 38:1091-1111.
46. Ulusay R., Gokceoglu C., Sulukcu S. Draft ISRM suggested method for determining block punch strength index (BPI). Int J Rock Mech Min Sci 2001, 38:1113-1119.
47. Sonmez H., Tunusluoglu C. New considerations on the use of block punch index for predicting the uniaxial compressive strength of rock material. Int J Rock Mech Min Sci 2008, 45:1007-1014.
48. Mishra D.A., Basu A. Use of the block punch test to predict the compressive and tensile strengths of rocks. Int J Rock Mech Min Sci 2012, 51:119-127.
49. Gokceoglu C. A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition. Eng Geol 2002, 66(1-2):39-51.
50. Sonmez H., Tuncay E., Gokceoglu C. Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara agglomerate. Int J Rock Mech Min Sci 2004, 41(5):717-729.
51. Tiryaki B. Predicting intact rock strength for mechanical excavation using multivariate statistics. artificial neural networks, and regression trees. Eng Geol 2008, 99:51-60.
52. Cevik A., Akcapinar-Sezer E., Cabalar A.F., Gokceoglu C. Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 2011, 11:2587-2594.
53. Yagiz S., Sezer E.A., Gokceoglu C. 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 Geomech 2012, 36:1636-1650.
54. Manouchehrian A., Sharifzadeh M., Moghadam R.H. Application of artificial neural networks and multivariate statistics to estimate UCS using textural characteristics. Int J Min Sci Technol 2012, 22(2):229-236.
55. Karakus M., Tutmez B. Fuzzy and multiple regression modelling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity. Rock Mech Rock Eng 2006, 39(1):45-57.
56. Basu A., Aydin A. Predicting uniaxial compressive strength by point load test: significance of cone penetration. Rock Mech Rock Eng 2006, 39(5):483-490.
57. Sharma P.K., Khandelwal M., Singh T.N. A correlation between Schmidt hammer rebound numbers with impact strength index, slake durability index and P-wave velocity. Int J Earth Sci (Geol Rundsch) 2011, 100:189-195.
58. Dehghan S., Sattari Gh., Chehreh Chelgani S., Aliabadi M.A. Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks. Min Sci Technol 2010, 20:41-46.
59. Sarkar K., Tiwary A., Singh T.N. Estimation of strength parameters of rock using artificial neural networks. Bull Eng Geol Environ 2010, 69:599-606.
60. Minaeian B., Ahangari K. Estimation of uniaxial compressive strength based on P-wave and Schmidt hammer rebound using statistical method. Arab J Geocsi 2011, 10.1007/s12517-011-0460-y.
61. Singh T.N., Verma A.K. Comparative analysis of intelligent algorithms to correlate strength and petrographic properties of some schistose rocks. Eng Comput 2012, 28:1-12.
62. Singh R., Vishal V., Singh T.N., Ranjith P.G. A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks. Neural Comput Appl 2012, 10.1007/s00521-012-0944-z.
63. Gultekin N. Investigation of mineralogical and petrographical effects on uniaxial compressive strength of granitic rocks. PhD Thesis. Hacettepe University, Ankara, Turkey; 2011.
64. Streckeisen A. To each plutonic rocks: its proper name. Earth Sci Rev 1976, 12:1-33.
65. Yilmaz I., Yuksek G. Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 2009, 46:803-810.
66. Sarkar K., Vishal V., Singh T.N. An empirical correlation of index geomechanical parameters with the compressional wave velocity. Geotech Geol Eng 2012, 30:469-479.
67. ISRM (International Society for Rock Mechanics). The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974-2006. In: R Ulusay, JA Hudson, editors; 2007.
68. CANMET. Laboratory classification tests. In: Pit Slope Manuel of CANMET, Suppl. 3-1. Canada Centre for Mineral and Energy Technology Report; 1977.
69. ASTM (American Society for Testing and Materials). Annual book of ASTM standards-construction: soil and rock. ASTM Publication; 1994.
70. Swingler K. Applying neural networks: a practical guide 1996, Academic Press, New York.
71. Looney C.G. Pattern recognition using neural networks. Theory and algorithms for engineers and scientists 1996, Oxford University Press, New York.
72. Nelson M., Illingworth W.T. A practical guide to neural nets 1990, Addison Wesley, Reading, Mass.
73. SPSS Inc. SPSS regression models (version 11.5). Available from: [accessed 08.06.12]. http://www.spss.com.
74. Basheer I.A., Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 2000, 43:3-31.
75. Neaupane K.M., Achet S.H. Use of backpropagation neural network for landslide monitoring: a case study in the higher Himalaya. Eng Geol 2004, 74(3-4):213-226.
76. Negnevitsky M. Artificial intelligence: a guide to intelligent systems 2002, Addison Wesley, England.
77. Wasserman P.D. Neural computing theory and practice 1989, Van Nostrand Reinhold, New York.
78. Hecht-Nielsen R. Kolmogorov's mapping neural network existence theorem. In: Proceedings of the First IEEE International Conference on Neural Networks, San Diego, CA, 1987;11-4.
79. Basheer I.A. Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aided Civ Infrastructure Eng 2000, 5(6):445-463.
80. Sonmez H., Gokceoglu C., Medley E.W., Tuncay E., Nefeslioglu H.A. Estimating the uniaxial compressive strength of a volcanic bimrock. Int J Rock Mech Min Sci 2006, 43:554-561.
81. MATLAB R2008a. http://www.mathworks.com/products/matlab/?sec=apps.12.03.2012.
82. Singh V.K., Singh D., Singh T.N. Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 2001, 38:269-284.
83. Gokceoglu C., Yesilnacar E., Sonmez H., Kayabasi A. A neuro-fuzzy model for the deformation modulus of jointed rock masses. Comput Geotech 2004, 31(5):375-383.
84. Singh T.N., Sinha S., Singh V.K. Prediction of thermal conductivity of rock through physico-mechanical properties. Build Environ 2007, 42(1):146-155.
85. Cui Z.D., Tang Y.Q., Yan X.X., Yan C.L., Wang H.M. Evaluation of the geology-environmental capacity of buildings based on the ANFIS model of the floor area ratio. Bull Eng Geol Environ 2010, 69(1):111-118.
86. Sobhani J., Najimi M., Pourkhorshidi A.R., Parhizkar T. Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Construct Build Mater 2010, 24(5):709-718.
87. Talei A., Chua L.H.C. Wong TSW. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall-runoff modeling. J Hydrol 2010, 391(3-4):248-262.
88. Jalalifar H., Mojedifar S., Sahebi A.A., Nezamabadi-pour H. Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 2011, 38:783-790.
89. Kalinli A., Acar M.C., Gündüz Z. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Eng Geol 2011, 117(1-2):29-38.
90. Yang Z., Liu Y., Li C. Interpolation of missing wind data based on ANFIS. Renewable Energy 2011, 36(3):993-998.
91. Yilmaz I., Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 2011, 38(5):5958-5966.
92. Bui D.T., Pradhan B., Lofman O., Revhaug I., Dick O.B. Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 2012, 45:199-211.
93. Cabalar A.F., Cevik A., Gokceoglu C. Some applications of Adaptive Neuro-Fuzzy Inference System (ANFIS) in geotechnical engineering. Comput Geotech 2012, 40:14-33.
94. Singh R., Kainthola A., Singh T.N. Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 2012, 12(1):40-45.
95. Karami A., Afiuni-Zadeh S. Sizing of rock fragmentation modeling due to bench blasting using adaptive neuro-fuzzy inference system and radial basis function. Int J Min Sci Technol 2012, 22(4):459-463.
96. Madandoust R., Bungey J.H., Ghavidel R. Prediction of the concrete compressive strength by means of core testing using GMDH-type neural network and ANFIS models. Comput Mater Sci 2012, 51(1):261-272.
97. Sezer E.A., Pradhan B., Gokceoglu C. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl 2011, 38(7):8208-8219.
98. Singh T.N., Verma A.K., Sharma P.K. A neuro-genetic approach for prediction of time dependent deformational characteristic of rock and its sensitivity analysis. Geotech Geol Eng 2007, 25:395-407.
99. Jang J.S.R. ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybernet 1993, 23:665-685.
100. MATLAB R2010a. [accessed 05.04.12]. http://www.mathworks.com/products/matlab/?sec=apps.
101. Johnson R.B., DeGraff J.V. Principles of engineering geology 1988, Wiley, New York.