Data-Driven Site Characterization for Benchmark Examples Using Sparse Bayesian Learning

In this paper, a data-driven site characterization method called the sparse Bayesian learning (SBL) method previously proposed by the author is benchmarked by a set of virtual ground examples and a real ground example of cone penetration test (CPT) data. The SBL method assumes a zero-mean prior Gaussian random field model for the spatial trend modeled by sparse basis functions. The accuracy of the SBL method in predicting the cone tip resistance (qt) of CPT is quantified by the root-mean-square prediction error (RMSE), whereas the accuracy in identifying soil layers is quantified by the identification rate (IR). The performance of SBL is compared with that of the GLasso method. It is found that SBL does not always outperform GLasso, and GLasso does not always outperform SBL, either. Nonetheless, SBL requires less computational cost than GLasso.