Abstract
Gaussian process regression is an effective method for the stochastic interpolation of geotechnical site data. However, a significant drawback of this method is its stationarity assumption, which is unrealistic given the existence of different soil layers. This assumption results in higher uncertainty in interpolation and poorer performance. To address this limitation, a mixture of Gaussian processes model is investigated for simultaneous layer identification and spatial interpolation in 2D. The model is based on the probabilistic assessment of layer boundaries and Gaussian processes for defining the statistical properties of the layers as well as spatial interpolation. The accuracy of the model is tested with real CPT profiles. The performance of the model is evaluated based on interpolation accuracy.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 9th International Symposium for Geotechnical Safety and Risk (ISGSR) |
| Editors | Zhongqiang Liu, Jian Dai, Kate Robinson |
| Place of Publication | Singapore |
| Publisher | Research Publishing |
| Pages | 136-139 |
| Number of pages | 4 |
| ISBN (Electronic) | 978-981-94-4075-7 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 9th International Symposium on Geotechnical Safety and Risk - Oslo, Norway Duration: 25 Aug 2025 → 28 Aug 2025 https://www.isgsr2025.com/ |
Conference
| Conference | 9th International Symposium on Geotechnical Safety and Risk |
|---|---|
| Abbreviated title | ISGSR 2025 |
| Country/Territory | Norway |
| City | Oslo |
| Period | 25/08/25 → 28/08/25 |
| Internet address |
Keywords
- CPT
- Gaussian process
- mixture of Gaussian processes
- site characterization
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