Efficient simulation of 3D conditional random field using kriging with Gaussian-process trend
Journal
Computers and Geotechnics
Journal Volume
177
Start Page
106862
ISSN
0266-352X
Date Issued
2025-01
Author(s)
Ikumasa Yoshida
DOI
10.1016/j.compgeo.2024.106862
Abstract
Previous investigations have shown that for the modeling the soil spatial variability, the Gaussian process regression (GPR) provides a more plausible trend model than the linear combination of basis functions. However, the effectiveness of the conditional random (CRF) simulation based on the GPR trend model (denoted by the t-GPR kriging) has not been investigated. This study first addresses the high computational cost issue of the t-GPR kriging for realisic 3D problems by deriving the Kronecker-product algorithms. Then, this study further investigates the effectiveness of the t-GPR kriging in CRF simulation using real case studies. It is shown that with the Kronecker-product derivations, the computational time can be dramatically reduced such that the t-GPR kriging can conduct CRF simulation for full-scale 3D problems.
Subjects
Gaussian process regression
Probabilistic site characterization
Random field
Spatial variability
Publisher
Elsevier BV
Type
journal article