The main objective of site characterization is the prediction of in situ soil properties at any half-space point at a site based on limited tests. In this study, the Support Vector Machine (SVM) has been used to develop a three dimensional site characterization model for Bangalore, India based on large amount of Standard Penetration Test. SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing ε-insensitive loss function. The database consists of 766 boreholes, with more than 2700 field SPT values (??) spread over 220?sq?km area of Bangalore. The model is applied for corrected ?? (????) values. The three input variables (??, ??, and ??, where ??, ??, and ?? are the coordinates of the Bangalore) were used for the SVM model. The output of SVM was the ???? data. The results presented in this paper clearly highlight that the SVM is a robust tool for site characterization. In this study, a sensitivity analysis of SVM parameters (σ, ??, and ε) has been also presented.
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