Data-Driven Site Characterization for Benchmark Examples Using Sparse Bayesian Learning
Part Of
Geotechnical Special Publication
Journal Volume
2023-July
Journal Volume
2023-July
Journal Issue
GSP 345
Journal Issue
GSP 345
Start Page
438
End Page
445
ISSN
08950563
Date Issued
2023
Author(s)
Abstract
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.
Event(s)
Geo-Risk Conference 2023: Innovation in Data and Analysis Methods, 23 July 2023 - 26 July 2023, Arlington
Publisher
American Society of Civil Engineers (ASCE)
Type
conference paper