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Machine learning techniques applied to prediction of residual strength of clay

Sarat Das / Pijush Samui / Shakilu Khan / Nagarathnam Sivakugan
Published Online: 2011-12-14 | DOI: https://doi.org/10.2478/s13533-011-0043-1


Stability with first time or reactivated landslides depends upon the residual shear strength of soil. This paper describes prediction of the residual strength of soil based on index properties using two machine learning techniques. Different Artificial Neural Network (ANN) models and Support Vector Machine (SVM) techniques have been used. SVM aims at minimizing a bound on the generalization error of a model rather than at minimizing the error on the training data only. The ANN models along with their generalizations capabilities are presented here for comparisons. This study also highlights the capability of SVM model over ANN models for the prediction of the residual strength of soil. Based on different statistical parameters, the SVM model is found to be better than the developed ANN models. A model equation has been developed for prediction of the residual strength based on the SVM for practicing geotechnical engineers. Sensitivity analyses have been also performed to investigate the effects of different index properties on the residual strength of soil.

Keywords: landslides; residual strength; index properties; prediction model; artificial neural network; support vector machine

  • [1] Skempton A.W., The long term stability of clay slopes. Geotechnique, 1964, 14, 77–101 http://dx.doi.org/10.1680/geot.1964.14.2.77CrossrefGoogle Scholar

  • [2] Mesri G., Shahien M., Residual Shear Strength Mobilized in First-Time Slope Failures. J. Geotech. Geoenviron. Eng., 2003, 129, 12–31 http://dx.doi.org/10.1061/(ASCE)1090-0241(2003)129:1(12)CrossrefGoogle Scholar

  • [3] Bowles J.E., Foundation analysis and design. McGraw-Hill International Edition, Singapore, 1988 Google Scholar

  • [4] Mesri G., Cepeda-Diaz A.F., Residual strength of clays and shales. Geotechnique, 1986, 36, 269–274 http://dx.doi.org/10.1680/geot.1986.36.2.269CrossrefGoogle Scholar

  • [5] Colotta T., Cantoni R., Pavesi U., Robert E., Moretti P.C., A correlation between residual friction angle, gradation and index properties of cohesive soil. Geotechnique, 1989, 39, 343–346 http://dx.doi.org/10.1680/geot.1989.39.2.343CrossrefGoogle Scholar

  • [6] Stark T.D., Eid H.T., Drained residual strength of cohesive soils. J. Geotech. Geoenviron. Eng., 1994, 120, 856–871 Google Scholar

  • [7] Wesley L.D., Residual strength of clays and correlations using Atterberg limit. Geotechnique, 2003, 53, 669–672 http://dx.doi.org/10.1680/geot.2003.53.7.669CrossrefGoogle Scholar

  • [8] Sridharan A., Rao P.R., Discussion: Residual strength of clays and correlation using Atterberg limits. Geotechnique, 2004, 54, 503–504 http://dx.doi.org/10.1680/geot.2004.54.7.503CrossrefGoogle Scholar

  • [9] Tiwari B., Marui H., A new method for the correlation of residual shear strength of the soil with mineralogical composition. J. Geotech. Geoenviron. Eng., 2005, 131, 1139–1150 http://dx.doi.org/10.1061/(ASCE)1090-0241(2005)131:9(1139)CrossrefGoogle Scholar

  • [10] Kaya A., Kwong J.K.P., Evaluation of common practice empirical procedures for residual friction angle of soils: Hawaiian amorphous material rich colluvial soil case study. Eng. Geol., 2007, 92, 49–58 http://dx.doi.org/10.1016/j.enggeo.2007.03.002Web of ScienceCrossrefGoogle Scholar

  • [11] Das S.K., Basudhar P.K., Prediction of residual friction angle of clays using artificial neural network. Eng. Geol., 2008, 100, 142–145 http://dx.doi.org/10.1016/j.enggeo.2008.03.001Web of ScienceCrossrefGoogle Scholar

  • [12] Demuth H., Beale M., Neural Network Toolbox. The Math Works Inc., USA, 2000. Google Scholar

  • [13] MathWork Inc., Matlab User’s Manual. Version 6.5. Natick MA, 2001 Google Scholar

  • [14] Ilonen J., Kamarainen J.K., Lampinen J., Differential Evolution training algorithm for feed-forward neural network. Neural Processing Letters, 2003, 17, 93–105 http://dx.doi.org/10.1023/A:1022995128597CrossrefGoogle Scholar

  • [15] Juang C.H., Elton D.J., Prediction of collapse potential of soil with neural networks. Trans. Res. Record, 1997, 1582, 22–28 http://dx.doi.org/10.3141/1582-04CrossrefGoogle Scholar

  • [16] Das S.K., Basudhar P.K., Undrained lateral load capacity of piles in clay using artificial neural network. Comput. and Geotech., 2006, 33, 454–459 http://dx.doi.org/10.1016/j.compgeo.2006.08.006CrossrefGoogle Scholar

  • [17] Boser B.E., Guyon I.M., Vapnik V.N., A training algorithm for optimal margin classifiers. In: Haussler D (Ed.) 5th Annual ACM workshop on COLT. ACM, Pittsburgh, 1992, 144–152 Google Scholar

  • [18] Cristianini N., Shawe-Taylor J., An introduction to support vector machine. University Press, London, Cambridge, 2000 Google Scholar

  • [19] Cortes C., Vapnik V.N., Support vector networks. Mach. Learn., 1995, 20, 273–297 Google Scholar

  • [20] Gualtieri J.A., Chettri S.R., Cromp R.F., Johnson L.F., Support vector machine classifiers as applied to AVIRIS data. In: The summaries of the 8th JPL airborne earth science workshop, 1999 Google Scholar

  • [21] Vapnik V.N., Statistical learning theory. Wiley, New York, 1998 Google Scholar

  • [22] Bennett K.P., Mangasarian O.L., Robust linear programming discrimination of two linearly inseparable sets. Optimization Methods and Software, 1992, 1, 23–34 http://dx.doi.org/10.1080/10556789208805504CrossrefGoogle Scholar

  • [23] Smola A.J., Scholkopf B., A tutorial on support vector regression. Stat. Comput., 2004, 14, 199–222 http://dx.doi.org/10.1023/B:STCO.0000035301.49549.88CrossrefGoogle Scholar

  • [24] Das S.K., Samui P., Sabat A.K., Sitharam T.G., Prediction of swelling pressure of soil using artificial intelligence techniques. Environmental Earth Science, 2010, 61, 393–403 http://dx.doi.org/10.1007/s12665-009-0352-6CrossrefGoogle Scholar

  • [25] Olden J.D., Joy M.K., Death R.G., An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Eco. Model, 2004, 178, 389–397 http://dx.doi.org/10.1016/j.ecolmodel.2004.03.013CrossrefGoogle Scholar

About the article

Published Online: 2011-12-14

Published in Print: 2011-12-01

Citation Information: Open Geosciences, Volume 3, Issue 4, Pages 449–461, ISSN (Online) 2391-5447, DOI: https://doi.org/10.2478/s13533-011-0043-1.

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© 2011 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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