Efficient simulation of 3D conditional random field using kriging with Gaussian-process trend
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 tr...
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| Published in | Computers and geotechnics Vol. 177; p. 106862 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.01.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0266-352X |
| DOI | 10.1016/j.compgeo.2024.106862 |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 106862 |
| Author | Ching, Jianye Yoshida, Ikumasa |
| Author_xml | – sequence: 1 givenname: Jianye surname: Ching fullname: Ching, Jianye email: jyching@gmail.com organization: Dept of Civil Engineering, National Taiwan University, Taipei, Taiwan, ROC – sequence: 2 givenname: Ikumasa surname: Yoshida fullname: Yoshida, Ikumasa organization: Department of Urban and Civil Engineering, Tokyo City University, Tokyo, Japan |
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| Cites_doi | 10.1061/(ASCE)GT.1943-5606.0001720 10.1061/(ASCE)GT.1943-5606.0001554 10.2113/gsecongeo.58.8.1246 10.1680/jgeot.16.P.143 10.1016/j.compgeo.2016.05.027 10.1139/t99-038 10.1016/j.strusafe.2019.101870 10.1061/(ASCE)EM.1943-7889.0001560 10.1016/j.compgeo.2021.104179 10.1061/(ASCE)EM.1943-7889.0001907 10.1016/j.gr.2022.07.011 10.1109/TSP.2007.914345 10.1061/(ASCE)EM.1943-7889.0001859 10.1139/cgj-2017-0254 10.1061/(ASCE)0733-9399(2007)133:7(816) 10.1093/biomet/93.4.989 10.1139/cgj-2016-0189 10.1061/(ASCE)EM.1943-7889.0001240 10.1016/j.sandf.2012.12.001 |
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