An application of adaptive neuro fuzzy inference system for estimating the uniaxial compressive strength of certain granitic rocks from their mineral contents

Expert Systems with Applications

Volumn 40, Issue 3, 2013, Pages 921-928

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

^a AKSARAY UNIVERSITY   (Turkey)

^b HACETTEPE UNIVERSITY   (Turkey)

Author keywords

Adaptive neuro fuzzy inference system; Granitic rock; Nonlinear multiple regression; Uniaxial compressive strength

Indexed keywords

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM; ANFIS MODEL; DATA SETS; ENGINEERING APPLICATIONS; GRANITIC ROCKS; INTACT ROCKS; MINERAL CONTENT; MULTIPLE REGRESSION ANALYSIS; MULTIPLE REGRESSIONS; OPTIMAL MODEL; PERFORMANCE INDICES; ROOT MEAN SQUARE ERRORS; SOFT COMPUTING METHODS; TEXTURAL CHARACTERISTIC; UNIAXIAL COMPRESSIVE STRENGTH;

COMPRESSIVE STRENGTH; GRANITE; MEAN SQUARE ERROR; QUARTZ; REGRESSION ANALYSIS; ROCKS; SOFT COMPUTING;

ESTIMATION;

EID: 84867852217     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2012.05.048     Document Type: Conference Paper

References (47)

1. A. Basu, and M. Kamran Point load test on schistose rocks and its applicability in predicting uniaxial compressive strength International Journal of Rock Mechanics & Mining Sciences 47 2010 823 828
2. Z.T. Bieniawski Estimating the strength of rock materials Journal of the South African Institute of Mining and Metallurgy 74 1974 312 320
3. M. Buragohain, and C. Mahanta A novel approach for ANFIS modelling based on full factorial design Applied Soft Computing 8 2008 609 625
4. A. Cevik, E.A. Sezer, A.F. Cabalar, and C. Gokceoglu Modelling of the uniaxial compressive strength of some clay-bearing rocks using neural network Applied Soft Computing 11 2 2011 2587 2594
5. G. Dagdelenler, E.A. Sezer, and C. Gokceoglu Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters Expert Systems with Applications 38 2011 7476 7485
6. J.C. Davis Statistics and data analysis in geology Third ed. 2002 John Wiley and Sons USA 638p.
7. S. Dehghan, Gh. Sattari, Chelgan.i.S. Chehreh, and M.A. Aliabadi Prediction of uniaxial compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural networks Mining Science and Technology 20 2010 41 46
8. F. Fueten A computer-controlled rotating polarizer stage for the petrographic microscope Computers & Geosciences 23 2 1997 203 208
9. C. Gokceoglu A fuzzy triangular chart to predict the uniaxial compressive strength of the Ankara agglomerates from their petrographic composition Engineering Geology 66 1-2 2002 39 51
10. C. Gokceoglu, and K. Zorlu A fuzzy model to predict the uniaxial compressive strength and the modulus of elasticity of a problematic rock Engineering Applications of Artificial Intelligence 17 2004 61 72
11. C. Gokceoglu, E. Yesilnacar, H. Sonmez, and A. Kayabasi A neuro-fuzzy model for modulus of deformation of jointed rock masses Computers and Geotechnics 31 2004 375 383
12. C. Gokceoglu, H. Sonmez, and K. Zorlu Estimating the uniaxial compressive strength of some clay bearing rocks selected from Turkey by nonlinear multivariable regression and rule-based fuzzy models Expert Systems 26 2 2009 176 190
13. J.S. Goodchild, and F. Fueten Edge detection in petrographic images using the rotating polarizer stage Computers & Geosciences 24 8 1998 745 751
14. Google Earth (2011). http://www.google.com/intl/tr/earth/index.html.
15. ISRM (International Society for Rock Mechanics) (2007). The complete ISRM suggested methods for rock characterization. Testing and Monitoring: 1974-2006, 628 p.
16. H. Jalalifar, S. Mojedifar, A.A. Sahebi, and H. Nezamabadi-pour Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system Computers and Geotechnics 38 2011 783 790
17. J.S.R. Jang ANFIS: Adaptive network-based fuzzy inference systems IEEE Transactions on Systems, Man and Cybernetics 23 1993 665 685
18. F.S. Jeng, M.C. Weng, M.L. Lin, and T.H. Huang Influence of petrographic parameters on geotechnical properties of tertiary sandstones from Taiwan Engineering Geology 73 2004 71 91
19. R.B. Johnson, and V.J. De Graff Principles of engineering geology 1988 Wiley New York
20. K. Kayabali, and L. Selcuk Nail penetration test for determining the uniaxial compressive strength of rock International Journal of Rock Mechanics & Mining Sciences 47 2010 265 271
21. C.G. Looney Advances in feed-forward neural networks: Demystifying knowledge acquiring black boxes IEEE Transactions on Knowledge Data Engineering 8 2 1996 211 226
22. MATLAB (2010). .
23. F. Meyer, and S. Beucher Morphological segmentation Journal of Visual Communication and Representation 1 1990 21 46
24. D.A. Mishra, and A. Basu Use of the block punch test to predict the compressive and tensile strengths of rocks International Journal of Rock Mechanics & Mining Sciences 2012 10.1016/j.ijrmms.2012.01.016
25. M. Młynarczuk Some remarks on the application of image analysis and image processing for the description of the geometrical structures of rock Physicochemical Problems of Mineral Processing 33 1999 107 116
26. M. Negnevitsky Artificial intelligence. A guide to intelligent systems 2002 Addison Wesley England 394 p.
27. M. Nelson, and W.T. Illingworth A practical guide to neural nets 1990 Addison Wesley Reading, MA
28. B. Obara A new algorithm using image colour system transformation for rock grain segmentation Mineralogy and Petrology 91 3-4 2007 271 285
29. T.F. Onodera, and H.M. Asoka Kumara Relation between texture and mechanical properties of crystalline rocks Bulletin International Association of Engineering Geologists 22 1980 173 177
30. V.K. Singh, D. Singh, and T.N. Singh Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural Networks International Journal of Rock Mechanics & Mining Sciences 38 2001 269 284
31. T.N. Singh, S. Sinha, and V.K. Singh Prediction of thermal conductivity of rock through physico-mechanical properties Building and Environment 42 1 2007 146 155
32. R. Singh, A. Kainthola, and T.N. Singh Estimation of elastic constant of rocks using an ANFIS approach Applied Soft Computing 12 2012 40 45
33. H. Sonmez, E. Tuncay, and C. Gokceoglu Models to predict the uniaxial compressive strength and the modulus of elasticity for Ankara Agglomerate International Journal of Rock Mechanics & Mining Sciences 41 2004 717 729
34. H. Sonmez, C. Gokceoglu, E.W. Medley, E. Tuncay, and H.A. Nefeslioglu Estimating the uniaxial compressive strength of a volcanic bimrock International Journal of Rock Mechanics & Mining Sciences 43 2006 554 561
35. SPSS Inc. (2007). SPSS regression models (version 11.5). .
36. A. Streckeisen To each plutonic rocks: Its proper name Earth Sciences Review 12 1976 1 33
37. K. Swingler Applying neural networks: A practical guide 1996 Academic Press New York
38. B. Tiryaki Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks, and regression trees Engineering Geology 99 2008 51 60
39. A. Tugrul, and I.H. Zarif Correlation of mineralogical and textural characteristics with engineering properties of selected granitic rocks from Turkey Engineering Geology 51 1999 303 317
40. Yagiz, S. (2008). Predicting uniaxial compressive strength, modulus of elasticity and index properties of rocks using the Schmidt hammer. Bulletin of Engineering Geology and Environment. The Official journal of the IAEG. http://dx.doi.org/10.1007/s10064-008-0172-z.
41. S. Yagiz, E.A. Sezer, and C. Gokceoglu 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 International Journal for Numerical and Analytical Methods in Geomechanics 2012 10.1002/nag.1066
42. N. Yesiloglu-Gultekin, A.S. Keceli, E.A. Sezer, A.B. Can, C. Gokceoglu, and H. Bayhan A computer programme (TSecSoft) to determine mineral percentages using photographs obtained from thin sections Computers & Geosciences 2012 10.1016/j.cageo.2012.01.001
43. I. Yilmaz, and A.G. Yuksek Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, ANFIS models and their comparison International Journal of Rock Mechanics and Mining Sciences 46 4 2009 803 810
44. I. Yilmaz, M. Marschalko, M. Bednarik, O. Kaynar, and L. Fojtova Neural computing models for prediction of permeability coefficient of coarse grained soils Neural Computing and Applications 2011 10.1007/s00521-011-0535-4
45. Y. Zhou, J. Starkey, and L. Mansinha Identification of mineral grains in a petrographic thin section using phi-and max-images Mathematical Geology 36 7 2004 781 801
46. Y. Zhou, J. Starkey, and L. Mansinha Segmentation of petrographic images by integrating edge detection and region growing Computers & Geosciences 30 2004 817 831
47. K. Zorlu, C. Gokceoglu, F. Ocakoglu, H.A. Nefeslioglu, and S. Acikalin Prediction of uniaxial compressive strength of sandstones using petrography-based models Engineering Geology 96 3-4 2008 141 158