Applicability of Data Mining Techniques for Predicting Electrical Resistivity of Soils Based on Thermal Resistivity
Publication: International Journal of Geomechanics
Volume 13, Issue 5
Abstract
This article adopts two data mining techniques, support vector machine (SVM) and least-squares support vector machine (LSSVM), for prediction of soil electrical resistivity based on soil properties and thermal resistivity. Two models (Model I and Model II) are developed. Model I uses the percentage sum of the gravel-size and sand-size fractions (%) and thermal resistivity () as input parameters. Model II uses gravel-size and sand-size fractions (%), degree of saturation (%), and thermal resistivity () as input parameters. Equations have been also developed for the determination of the soil electrical resistivity () of soils. The results are compared with an artificial neural network (ANN) model. This study proves the capability of SVM and LSSVM for prediction of the of soils.
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References
Alipour, A., Jafari, A., and Hossaini, S. M. F. (2012). “Application of ANNs and MVLRA for estimation of specific charge in small tunnel.” Int. J. Geomech., 12(2), 189–192.
Borin, A., Ferrao, M. F., Mello, C., Maretto, D. A., and Poppi, R. J. (2006). “Least-squares support vector machines and near infrared spectroscopy for quantification of common adulterants in powdered milk.” Anal. Chim. Acta, 579(1), 25–32.
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). “A training algorithm for optimal margin classifier.” Proc., 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA.
Butterfield, R., and Johnston, I. W. (1980). “The influence of electro-osmosis on metallic piles in clay.” Geotech., 30(1), 17–38.
Cortes, C., and Vapnik, V. (1995). “Support vector networks.” Mach. Learn., 20, 273–297.
Drucker, H., Burges, C., Kaufman, L., Smola, A., and Vapnik, V. (1997). “Support vector regression machines.” Advances in neural information processing systems M. Mozer, M. Jordan, and T. Petsche, eds., Vol. 9, MIT Press, Cambridge, MA, 155–161.
Erchul, R. A., and Gularte, R. C. (1982). “Electrical resistivity used to measure liquefaction of sand.” J. Geotech. Eng., 108(5), 778–783.
Erzin, Y., Rao, B. H., Patel, A., Gumaste, S. D., Gupta, K., and Singh, D. N. (2010). “Artificial neural network models for predicting of electrical resistivity of soils from their thermal resistivity.” Int. J. Therm. Sci., 49(1), 118–130.
Gualtieri, J. A., Chettri, S. R., Cromp, R. F., and Johnson, L. F. (1999). “Support vector machine classifiers as applied to AVIRIS data.” Summaries, 8th JPL Airborne Earth Science Workshop.
Gunnink, B., and El-Jayyousi, J. (1993). “Soil-fabric measurement using conduction phase porosimetry.” J. Geotech. Eng., 119(6), 1019–1035.
Kalinski, R. J., and Kelly, W. E. (1993). “Estimating water content of soils from electrical resistivity.” Geotech. Testing J., 16(3), 323–329.
Kecman, V. (2001). Learning and soft computing: Support vector machines, neural networks and fuzzy logic models, MIT Press, Cambridge, MA.
MATLAB 5.3 [Computer software]. Natick, MA, MathWorks.
Mazac, O., Cislerove, M., Kelly, W. E., Landa, I., and Venhodova, D. (1990). “Determination of hydraulic conductivities by surface geoelectrical methods.” Geotechnical and environmental geophysics, Vol. II, S. Ward, ed., Society of Exploration Geophysics, Tulsa, OK 125–131.
McCarter, W. (1984). “The electrical resistivity characteristics of compacted clays.” Geotechnique, 34(2), 263–267.
McCollum, B., and Logan, K. H. (1930). “Electrolytic corrosion of iron in soils.” Bureau of Standards, Tech., 24.
Miranda, T., Correia, A. G., Santos, M., Sousa, L. R., and Cortez, P. (2011). “New models for strength and deformability parameter calculation in rock masses using data-mining techniques.” Int. J. Geomech., 11(1), 44–58.
Muller, K.-R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J., and Vapnik, V. (1997). “Predicting time series with support vector machines.” Artificial Neural Networks—ICANN ’97, W. Gerstner, A. Germond, M. Hasler, and J.-D. Nicoud, eds., Springer, 999–1004.
Park, D., and Rilett, L. R. (1999). “Forecasting freeway link travel times with a multilayer feedforward neural network.” Computer-Aided Civil Infrastruct. Eng., 14(5), 357–367.
Schultz, D. W., Duff, B. M., and Peters, W. R. (1984). “Performance of an electrical resistivity technique for detecting and locating geomembrane failures.” Int. Conf. on Geomembranes, Denver, 445–449.
Shea, P. F., and Luthin, J. N. (1961). “An investigation of the use of the four electrode probe for measuring soil salinity in situ.” Soil Sci., 92(5), 331–339.
Shi, D. F., and Gindy, N. N. (2007). “Tool wear predictive model based on least squares support vector machines.” Mech. Syst. Signal Process., 21(4), 1799–1814.
Singh, D. N., Kuriyan, S. J., and Manthena, K. C. (2001). “A generalised relationship between soil electrical and thermal resistivities.” Exp. Therm. Fluid Sci., 25(3–4), 175–181.
Sreedeep, S., Reshma, A. C., and Singh, D. N. (2005). “Generalized relationship for determining soil electrical resistivity from its thermal resistivity.” Exp. Therm. Fluid Sci., 29(2), 217–226.
Suykens, J. A. K., and Vandewalle, J. (1999). “Least squares support vector machine classifiers.” Neural Process. Lett., 9(3), 293–300.
Tagg, G. F. (1964). Earth resistances, MIT Press, Newnes, London.
Tsujinishi, D., and Abe, S. (2003). “Fuzzy least squares support vector machines for multi-class problems.” Neural Networks Field, 16, 785–792.
Van Gestel, T., et al. (2004). “Benchmarking least squares support vector machine classifiers.” Machine Learning, 54(1), 5–32.
Vapnik, V. N. (1998). Statistical learning theory, Wiley, New York.
Yazdi, J. S., Kalantary, F., and Yazdi, H. S. (2012). “An investigation on the effect of data imbalance on prediction of liquefaction.” Int. J. Geomech., 13(4), 463–466.
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Published In
International Journal of Geomechanics
Volume 13 • Issue 5 • October 2013
Pages: 692 - 697
Copyright
© 2013 American Society of Civil Engineers.
History
Received: May 11, 2012
Accepted: Sep 10, 2012
Published online: Sep 12, 2012
Published in print: Oct 1, 2013
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