Browse > Article
http://dx.doi.org/10.12989/gae.2018.15.1.631

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)
Publication Information
Geomechanics and Engineering / v.15, no.1, 2018 , pp. 631-643 More about this Journal
Abstract
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.
Keywords
granite; tensile behavior; mechanical properties; physical properties; data mining techniques;
Citations & Related Records
연도 인용수 순위
  • Reference
1 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.   DOI
2 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.   DOI
3 Smola, A.J. and Scholkopf, B. (2004), "A tutorial on support vector regression", Stat. Comput., 14(3), 199-222.   DOI
4 Souza, A.M.F. and Soares, F.M. (2016), Neural Network Programming with Java, Packt Publishing Ltd, Birmingham, U.K.
5 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.   DOI
6 Vapnik, V.N. (1998), Statistical Learning Theory, Wiley, New York, U.S.A.
7 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.   DOI
8 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.   DOI
9 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.
10 Aleksander, I. and Morton, H. (1990), An Introduction to Neural Computing, Chapman & Hall, London, U.K.
11 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.   DOI
12 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.   DOI
13 Begonha, A. and Sequeira Braga, M.A. (2002), "Weathering of the Oporto granite: geotechnical and physical properties", Catena, 49(1-2), 57-76.   DOI
14 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.
15 Cherkassy, V. and Ma, Y. (2004), "Practical selection of SVM parameters and noise estimation for SVM regression", Neural Networks, 17(1), 113-126.   DOI
16 Canakci, H. and Pala, M. (2007), "Tensile strength of basalt from a neural network", Eng. Geol., 94(1-2), 10-18.   DOI
17 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.   DOI
18 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.
19 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.   DOI
20 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.   DOI
21 Cortes, C. and Vapnik, V. (1995), "Support vector networks", Mach. Learn., 20(3), 273-297.   DOI
22 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.
23 Cristianini, N. and Shawe-Taylor, J. (2000), An Introduction to Support Vector Machine, Cambridge University Press, Cambridge, U.K.
24 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.   DOI
25 Hastie, T., Tibshirani, R. and Friedman, J. (2001), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, USA.
26 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.   DOI
27 Downing, K.L. (2015), Intelligence Emerging: Adaptative and Search in Evolving Neural System, MIT Press, U.S.A.
28 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.   DOI
29 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.   DOI
30 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.
31 Haykin, S. (1999), Neural Networks-A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, New Jersey, U.S.A.
32 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.   DOI
33 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.   DOI
34 Johnson, R. (1984), Elementary Statistics, Duxbury Press, Boston, U.S.A., 86-106.
35 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.   DOI
36 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.   DOI
37 Kilic, A. and Teymen, A. (2008), "Determination of mechanical properties of rocks using simple methods", Bull. Eng. Geol. Environ., 67(2), 237-244.   DOI
38 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.
39 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.   DOI
40 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.   DOI
41 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.   DOI
42 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.   DOI
43 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.   DOI
44 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.   DOI