Geomechanics and Engineering

  • 제15권1호
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  • Pages.631-643
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  • 2018
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of the mechanical properties of granites under tension using DM techniques

  • Martins, Francisco F. (ISISE, Department of Civil Engineering, University of Minho) ;
  • Vasconcelos, Graca (ISISE, Department of Civil Engineering, University of Minho) ;
  • Miranda, Tiago (ISISE, Department of Civil Engineering, University of Minho)
  • 투고 : 2017.06.22
  • 심사 : 2017.11.08
  • 발행 : 2018.05.20
⟨ 이전 논문 다음 논문 ⟩

초록

The estimation of the strength and other mechanical parameters characterizing the tensile behavior of granites can play an important role in civil engineering tasks such as design, construction, rehabilitation and repair of existing structures. The purpose of this paper is to apply data mining techniques, such as multiple regression (MR), artificial neural networks (ANN) and support vector machines (SVM) to estimate the mechanical properties of granites. In a first phase, the mechanical parameters defining the complete tensile behavior are estimated based on the tensile strength. In a second phase, the estimation of the mechanical properties is carried out from different combination of the physical properties (ultrasonic pulse velocity, porosity and density). It was observed that the estimation of the mechanical properties can be optimized by combining different physical properties. Besides, it was seen that artificial neural networks and support vector machines performed better than multiple regression model.

키워드

과제정보

연구 과제 주관 기관 : FEDER, FCT-Foundation for Science and Technology

참고문헌

  1. Aleksander, I. and Morton, H. (1990), An Introduction to Neural Computing, Chapman & Hall, London, U.K.
  2. Alemdag, S., Gurocak, Z., Cevik, A., Cabalar, A.F. and Gokceoglu, C. (2016), "Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming", Eng. Geol., 203, 70-82. https://doi.org/10.1016/j.enggeo.2015.12.002
  3. Baykasoglu, A., Gullu, A., Canakci, H. and Ozbakir, L. (2008), "Prediction of compressive and tensile strength of limestone via genetic programming", Expert Syst. Appl., 35(1-2),111-123. https://doi.org/10.1016/j.eswa.2007.06.006
  4. Begonha, A. and Sequeira Braga, M.A. (2002), "Weathering of the Oporto granite: geotechnical and physical properties", Catena, 49(1-2), 57-76. https://doi.org/10.1016/S0341-8162(02)00016-4
  5. Ben-Hur, A., Weston, J. (2010), A User's Guide to Support Vector Machines, in Data Mining Techniques for the Life Sciences, Humana Press, New York, U.S.A., 223-239.
  6. Canakci, H. and Pala, M. (2007), "Tensile strength of basalt from a neural network", Eng. Geol., 94(1-2), 10-18. https://doi.org/10.1016/j.enggeo.2007.06.009
  7. Canakci, H., Baykasoglu, A. and Gullu, H. (2009), "Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming", Neural Comput. Appl., 18(8), 1031-1041. https://doi.org/10.1007/s00521-008-0208-0
  8. Ceryan, N., Okkan, U., Samui, P. and Ceryan, S. (2013), "Modeling of tensile strength of rocks materials based on support vector machines approaches", J. Numer. Anal. Meth. Geomech., 37(16), 2655-2670.
  9. Cherkassy, V. and Ma, Y. (2004), "Practical selection of SVM parameters and noise estimation for SVM regression", Neural Networks, 17(1), 113-126. https://doi.org/10.1016/S0893-6080(03)00169-2
  10. Chiaia, B., Van Mier, J.G.M. and Vervuurt, A. (1998), "Crack growth mechanisms in four different concretes: Microscopic observations and fractal analysis", Cement Concrete Res., 28(1), 103-114. https://doi.org/10.1016/S0008-8846(97)00221-4
  11. Christaras, B., Auger, F. and Mosse, E. (1994), "Determination of the moduli of elasticity of rocks. Comparison of the ultrasonic velocity and mechanical resonance frequency methods with direct static methods", Mater. Struct., 27(4), 222-228. https://doi.org/10.1007/BF02473036
  12. Cortes, C. and Vapnik, V. (1995), "Support vector networks", Mach. Learn., 20(3), 273-297. https://doi.org/10.1007/BF00994018
  13. Cortez, P. (2010), "Data mining with neural networks and support nector machines using the R/rminer tool", Proceedings of the 10th Industrial Conference on Data Mining, Berlin, Germany, July.
  14. Cristianini, N. and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machine, Cambridge University Press, Cambridge, U.K.
  15. Dagdelenler, G., Sezer, E.A. and Gokceoglu, C. (2011), "Some non-linear models to predict the weathering degrees of a granitic rock from physical and mechanical parameters", Expert Syst. Appl., 38(6), 7476-7485. https://doi.org/10.1016/j.eswa.2010.12.076
  16. Dibike, Y.B., Velickov, S., Solomatine, D.P. and Abbott, M.B. (2001), "Model introduction with support vector machines; Introduction and applications", J. Comput. Civ. Eng., 15(3), 208-216. https://doi.org/10.1061/(ASCE)0887-3801(2001)15:3(208)
  17. Downing, K.L. (2015), Intelligence Emerging: Adaptative and Search in Evolving Neural System, MIT Press, U.S.A.
  18. Gokceoglu, C., Zorlu, K., Ceryan, S. and Nefeslioglu, H. A. (2009), "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., 60(11), 1317-1327. https://doi.org/10.1016/j.matchar.2009.06.006
  19. GSL (Geological Society of London) (1995), "The description and classification of weathered rocks for engineering purposes, geological society engineering group working party report", Quart. J. Eng. Geol., 28, 207-242. https://doi.org/10.1144/GSL.QJEGH.1995.028.P3.02
  20. Gurocak, Z., Solanki, P., Alemdag, S. and Zaman, M.M. (2012), "New considerations for empirical estimation of tensile strength of rocks", Eng. Geol., 145, 1-8.
  21. Hastie, T., Tibshirani, R. and Friedman, J. (2001), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, USA.
  22. Haykin, S. (1999), Neural Networks-A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, New Jersey, U.S.A.
  23. Ilonen, J., Kamarainen, J.K. and Lampinen, J. (2003), "Differential evolution training algorithm for feed-forward neural network", Neural Process. Lett., 17(1), 93-105. https://doi.org/10.1023/A:1022995128597
  24. Irfan, T.Y. and Dearman, W.R. (1978), "The engineering petrography of weathered granite in Cornwall, England", Quart. J. Eng. Geol., 11(3), 233-244. https://doi.org/10.1144/GSL.QJEG.1978.011.03.03
  25. Johnson, R. (1984), Elementary Statistics, Duxbury Press, Boston, U.S.A., 86-106.
  26. Karakus, M. (2001), "Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP)", Comput. Geosci., 37(9), 1318-1323. https://doi.org/10.1016/j.cageo.2010.09.002
  27. Kilic, A. and Teymen, A. (2008), "Determination of mechanical properties of rocks using simple methods", Bull. Eng. Geol. Environ., 67(2), 237-244. https://doi.org/10.1007/s10064-008-0128-3
  28. Liang, Y., Xu, Q.S., Li, H.D. and Cao, D.S. (2011), Support Vector Machines and their Application in Chemistry and Biotechnology, CRC Press, Boca Raton, Florida, U.S.A.
  29. Liao, S.H., Chu, P.H. and Hsiao, P.Y. (2012), "Data mining techniques and applications-A decade review from 2000 to 2011", Expert Syst. Appl., 39(12), 11303-11311. https://doi.org/10.1016/j.eswa.2012.02.063
  30. Marques, M.L, Chastre, C.M. and Vasconcelos, G. (2012), "Modelling the compressive mechanical behavior of building stones", Construct. Build. Mater., 28(1), 372-381. https://doi.org/10.1016/j.conbuildmat.2011.08.083
  31. Martins, F.F., Begonha, A. and Sequeira Braga, M.A. (2012), "Prediction of the mechanical behavior of the Oporto granite using data mining techniques", Expert Syst. Appl., 39(10), 8778-8783. https://doi.org/10.1016/j.eswa.2012.02.003
  32. Martins, F.F. and Miranda, T.F.S. (2012), "Estimation of the rock deformation modulus and RMR based on data mining techniques", Geotech. Geol. Eng., 30(4), 787-801. https://doi.org/10.1007/s10706-012-9498-1
  33. Meulenkamp, F. and Grima, M.A. (1999), "Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness", J. Rock Mech. Min. Sci. Geomech., 36(1), 29-39. https://doi.org/10.1016/S0148-9062(98)00173-9
  34. Mishra, D.A. and Basu, A. (2013), "Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system", Eng. Geol., 160, 54-68. https://doi.org/10.1016/j.enggeo.2013.04.004
  35. Prado, E.P. and Van Mier, J.G.M. (2003), "Effect of particle structure on mode I fracture process of concrete", Eng. Fract. Mech., 70(14), 1793-1807. https://doi.org/10.1016/S0013-7944(03)00125-5
  36. Sharma, P.K. and Singh, T.N. (2007), "A correlation between Pwave velocity, impact strength index, slake durability index and uniaxial compressive strength", Bull. Eng. Geol. Environ., 67(1), 17-22. https://doi.org/10.1007/s10064-007-0109-y
  37. Singh, R., Umrao, R.K., Ahmad, M., Ansari, M.K., Sharma, L.K. and Singh, T.N. (2017), "Prediction of geomechanical parameters using soft computing and multiple regression approach", Measurement, 99, 108-119. https://doi.org/10.1016/j.measurement.2016.12.023
  38. Smola, A.J. and Scholkopf, B. (2004), "A tutorial on support vector regression", Stat. Comput., 14(3), 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
  39. Souza, A.M.F. and Soares, F.M. (2016), Neural Network Programming with Java, Packt Publishing Ltd, Birmingham, U.K.
  40. Tang, C.A., Tham, L.G., Wang, S.H., Liu, H. and Li, W.H. (2007), "A numerical study of the influence of heterogeneity on the strength characterization of rock under uniaxial tension", Mech. Mater., 39(4), 326-339. https://doi.org/10.1016/j.mechmat.2006.05.006
  41. Vapnik, V.N. (1998), Statistical Learning Theory, Wiley, New York, U.S.A.
  42. Vasconcelos, G., Lourenco, P.B., Alves, C.A.S. and Pamplona, J. (2008a), "Experimental characterization of the tensile behaviour of granites", J. Rock Mech. Min. Sci., 45(2), 268-277. https://doi.org/10.1016/j.ijrmms.2007.04.011
  43. Vasconcelos, G., Lourenco, P.B., Alves, C.A. and Pamplona, J. (2008b), "Ultrasonic evaluation of the physical and mechanical properties of granites", Ultrasonics, 48(5), 453-466. https://doi.org/10.1016/j.ultras.2008.03.008
  44. Yesiloglu-Gultekin, N., Gokceoglu, C. and Sezer, E.A. (2013), "Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances", J. Rock Mech. Min. Sci., 62, 113-122.