Site Characterization Model Using Support Vector Machine and Ordinary Kriging

Abstract

In the present study, ordinary kriging and support vector machine (SVM) have been used to develop three dimensional site characterization model of an alluvial site based on standard penetration test (SPT) results. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function has been adopted. The knowledge of the semivariogram of the SPT values (N) is used in the ordinary kriging method to predict the N values at any point in the subsurface of the site where field measurements are not available. The comparison between the SVM and ordinary kriging model demonstrates that the SVM model is superior to ordinary kriging model in predicting N values in the site.