Use of the Relevance Vector Machine for Prediction of an Overconsolidation Ratio
Publication: International Journal of Geomechanics
Volume 13, Issue 1
Abstract
This article uses the relevance vector machine (RVM) for the prediction of the overconsolidation ratio (OCR) of fine-grained soils based on piezocone penetration test data. RVM provides an empirical Bayes method of function approximation by kernel basis expansion. It uses the corrected cone resistance (), vertical total stress (), hydrostatic pore pressure (), pore pressure at the cone tip (), and the pore pressure just above the cone base () as input parameters. An equation has also been developed for the determination of OCR. The developed RVM model gives the variance of the predicted data. Sensitivity analysis has been conducted for determining the influence of each input parameter. The results are also compared with some of the existing interpretation methods. Comparisons indicate that the developed RVM model performs better than the existing interpretation methods for predicting OCR.
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References
Akin, S., and Karpuz, C. (2008). “Estimating drilling parameters for diamond bit drilling operations using artificial neural networks.” Int. J. Geomech., 8(1), 68–73.
Baligh, M., Vivatrat, V., and Ladd, C. C. (1980). “Cone penetration in soil profiling.” J. Geotech. Eng. Div., 106(4), 447–461.
Berger, J. O. (1985). Statistical decision theory and Bayesian analysis, 2nd Ed., Springer, NY.
Campanella, R. G., and Robertson, P. K. (1981). “Applied cone research.” Cone penetration testing and experience, R. M. Norris and R. D. Holtz, eds., ASCE, New York, 343–362.
Chen, B.S-Y., and Mayne, P. W. (1994). “Profiling the OCR of clays by piezocone tests.” Rep. No. CEEGEO-94-1, Georgia Institute of Technology, Atlanta.
Goh, A. T. C., and Goh, S. H. (2007). “Support vector machines: Their use in geotechnical engineering as illustrated using seismic liquefaction data.” Comput. Geotech., 34(5), 410–421.
Kecman, V. (2001). Learning and soft computing: Support vector machines, neural networks, and fuzzy logic models, MIT Press, Cambridge, MA.
Konrad, J. M., and Law, K. T. (1987). “Preconsolidation pressure from piezocone tests in marine clay.” Geotechnique, 37(2), 177–190.
Kurup, P. U. (1993). “Calibration chamber studies of miniature piezocone penetration tests in cohesive soil specimens.” Ph.D. dissertation, Louisiana State Univ., Baton Rouge, LA.
Kurup, P. U., and Dudani, N. K. (2002). “Neural networks for profiling stress history of clays from PCPT data.” J. Geotech. Geoenviron. Eng., 128(7), 569–579.
Liong, S. Y., Lim, W. H., and Paudyal, G. N. (2000). “River stage forecasting in Bangladesh: Neural network approach.” J. Comput. Civ. Eng., 14(1), 1–8.
MacKay, D. J. (1992). “Bayesian methods for adaptive models.” Ph.D. thesis, Department of Computer and Neural Systems, California Institute of Technology, Pasadena, CA.
Mayne, P. W. (1987). “Determining preconsolidation pressures from DMT contact pressures.” Geotech. Test. J., 10(3), 146–150.
Mayne, P. W. (1991). “Determination of OCR in clays by PCPT using cavity expansion and critical state concepts.” Soil Found., 31(2), 65–76.
Mayne, P. W., and Bachus, R. C. (1988). “Profiling OCR in clays by piezocone soundings.” Proc., 1st Int. Symposium on Penetration Testing, Orlando, FL, 857–864.
Mayne, P. W., and Holtz, R. D. (1988). “Profiling stress history from piezocone soundings.” Soil Found., 28(1), 16–28.
Mayoraz, F., and Vulliet, L. (2002). “Neural networks for slope movement prediction.” Int. J. Geomech., 2(2), 153–173.
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.
Pal, M. (2006). “Support vector machines-based modelling of seismic liquefaction potential.” Int. J. Numer. Anal. Methods Geomech., 30(10), 983–996.
Pal, M., and Deswal, S. (2010). “Modelling pile capacity using Gaussian process regression.” Comput. Geotech., 37(78), 942–947.
Park, D., and Rilett, L. R. (1999). “Forecasting freeway link travel times with a multi-layer feed forward neural network.” Comput.-Aided Civ. Infrastruct. Eng., 14, 358–367.
Samui, P. (2008). “Support vector machine applied to settlement of shallow foundations on cohesionless soils.” Comput. Geotech., 35(3), 419–427.
Samui, P., and Sitharam, T. G. (2010). “Site characterization model using artificial neural network and kriging.” Int. J. Geomech., 10(5), 171–180.
Samui, P., Sitharam, T. G., and Kurup, P. U. (2008). “OCR prediction using support vector machine based on piezocone data.” J. Geotech. Geoenviron. Eng., 134(6), 894–898.
Sandven, R., Senneset, K., and Janbu, N. (1988). “Interpretation of piezocone tests in cohesive soils.” Proc., ISOPTI, Vol. 2, Orlando, FL, 939–953.
Scholkopf, B., and Smola, A. J. (2002). Learning with kernels: Support vector machines, regularization, optimization, and beyond, MIT Press, Cambridge, MA.
Senneset, K., Janbu, N., and Svano, G. (1982). “Strength and deformation parameters for CPT.” Proc., 2nd European Symposium on Penetration Testing, Vol. 2, Amsterdam, Netherlands, 863–870.
Smits, F. P. (1982). “Penetration pore pressure measured with piezometer cones.” Proc., 2nd European Symposium on Penetration Testing, Vol. 2, Amsterdam, Netherlands, 871–876.
Sully, J. P., Campanella, R. G., and Robertson, P. K. (1988). “Overconsolidation ratio of clays from penetration pore pressures.” J. Geotech. Eng. Div., 114(2), 209–216.
Tipping, M. E. (2000). “The relevance vector machine.” Adv. Neural Inf. Process. Syst., 12, 625–658.
Tipping, M. E. (2001). “Sparse Bayesian learning and the relevance vector machine.” J. Mach. Learn., 1, 211–244.
Tumay, M. T., Boggess, R. L., and Acar, Y. (1981). “Subsurface investigation with piezo-cone penetrometer.” Cone penetration testing and experience, ASCE, New York, 325–342.
Tumay, M. T., Kurup, P. U., and Voyiadjis, G. Z. (1995). “Profiling OCR and Ko from piezocone penetration tests.” Proc., Int. Symposium on Cone Penetration Testing, Swedish Geotechnical Society, Linkoping, Sweden, 337–342.
Wahba, G. (1985). “A comparison of GCV and GML for choosing the smoothing parameters in the generalized spline-smoothing problem.” Ann. Stat., (13)4, 1378–1402.
Zaman, M., Solanki, P., Ebrahimi, A., and White, L. (2010). “Neural network modeling of resilient modulus using routine subgrade soil properties.” Int. J. Geomech., 10(1), 1–12.
Zhang, G., Xiang, X., and Tang, H. (2011). “Time series prediction of chimney foundation settlement by neural networks.” Int. J. Geomech., 11(3), 154–158.
Information & Authors
Information
Published In
International Journal of Geomechanics
Volume 13 • Issue 1 • February 2013
Pages: 26 - 32
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Aug 13, 2010
Accepted: Aug 15, 2011
Published online: Aug 17, 2011
Published in print: Feb 1, 2013
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