Investigation on the Effect of Data Imbalance on Prediction of Liquefaction
Publication: International Journal of Geomechanics
Volume 13, Issue 4
Abstract
Data imbalance causes learning bias in class identification techniques. A major cause for limited success in the prediction of liquefaction potential by various pattern recognition techniques is because of a liquefaction to nonliquefaction data class imbalance. It is suggested to use a support vector data description (SVDD) strategy to compensate the minority data. SVDD is used to generate virtual data points for the minority class bearing the same characteristics as the nonvirtual samples. Then an adaptive neuro-fuzzy inference system (ANFIS) classifier is employed to determine the liquefaction threshold. The ANFIS predictions are then examined by evaluating the coefficient of determination (COD) and comparing it with the Bayesian updating method. It is shown that for the liquefied data the approach is as efficient as the Bayesian method, but great improvement in the recognition rates of the nonliquefied data have been achieved.
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References
Cetin, K. O., Kiureghian, A. D., and Seed, R. B. (2002). “Probabilistic models for the initiation of seismic soil liquefaction.” Struct. Saf., 24(1), 67–82.
Jafarian, Y., Abdollahi, A. S., Vakili, R., and Baziar, M. H. (2010). “Probabilistic correlation between laboratory and field liquefaction potentials using relative state parameter index (ξR).” Soil. Dyn. Earthquake Eng., 30(10), 1061–1072.
Juang, C. H., Ynan, H., Lee, D. H., and Lin, P. S. (2003). “Simplified cone penetration test-based method for evaluating liquefaction resistance of soils.” J. Geotech. Geoenviron. Eng., 129(1), 66–80.
MATLAB 7.12.0.635 (R2011a) [Computer software]. Adelaide, South Australia, Australia, MathWorks, Inc.
Moss, R., Seed, R. B., Kayen, R. E., Stewart, J. P., Kiureghian, A. D., and Cetin, K. O. (2006). “CPT-based probabilistic and deterministic assessment of in situ seismic soil liquefaction potential.” J. Geotech. Geoenviron. Eng., 132(8), 1032–1051.
Mróz, Z., Boukpeti, N., and Drescher, A. (2003). “Constitutive model for static liquefaction.” Int. J. Geomech., 3(2), 133–144.
Oommen, T., Baise, L. G., and Vogel, R. (2010). “Validation and application of empirical liquefaction model.” J. Geotech. Geoenviron. Eng., 136(12), 1618–1633.
Rezania, M., Faramarzi, A., and Javadi, A. A. (2011). “An evolutionary based approach for assessment of earthquake-induced soil liquefaction and lateral displacement.” Eng. Appl. Artif. Intell., 24(1), 142–153.
Rezania, M., Javadi, A. A., and Giustolisi, O. (2010). “Evaluation of liquefaction potential based on CPT results using evolutionary polynomial regression.” Comput. Geotech., 37(1–2), 82–92.
Tax, D. M. J., and Duin, R. P. W. (1999). “Support vector domain description.” Pattern Recogni- t. Lett., 20, 1191–1199.
Tax, D. M. J., and Duin, R. P. W. (2004). “Support vector data description.” Mach. Learn., 54(1), 45–66.
Weiher, B., and Davis, R. (2004). “Correlation of elastic constants with penetration resistance in sandy soils.” Int. J. Geomech., 4(4), 319–329.
Youd, T. L., and Idriss, I. M. (2001). “Liquefaction resistance of soils: Summary report from the 1996 NCEER and 1998 NCEER/NSF workshops on evaluation of liquefaction resistance of soils.” J. Geotech. Geoenviron. Eng., 127(10), 817–833.
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© 2013 American Society of Civil Engineers.
History
Received: May 17, 2011
Accepted: Mar 7, 2012
Published online: Mar 12, 2012
Published in print: Aug 1, 2013
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