Data-Drive Site Characterization for Benchmark Examples: Sparse Bayesian Learning versus Gaussian Process Regression

In this paper, two data-drive site characterization methods, the sparse Bayesian learning (SBL) method and the Gaussian process regression (GPR) method, are benchmarked by a set of virtual ground examples and a real ground example of cone penetration test (CPT) data. The two methods both assume a zero-mean prior Gaussian random field model for the spatial trend, but the strategies of maintaining model simplicity are different. The SBL method produces a simple trend model by adopting sparse basis functions, whereas the GPR method produces a simple trend model by adopting a kernel function governed by few hyperparameters. The accuracy of the two methods in predicting the cone tip resistance ($q_t$) of CPT was quantified by the root-mean square prediction error (RMSE), whereas the accuracy in identifying soil layers was quantified by the identification rate (IR). It was found that the GPR method in general outperforms the SBL method. Further accuracy improvement for the GPR method can be obtained if a clustering analysis based on the Robertson’s soil behavior index ($I_c$) is conducted.