Abstract
This study explores the integration of advanced data analysis and geostatistical modeling to enhance geotechnical resilience in urban environments prone to cascading geo-hazards. Focusing on a 4 × 4 km plain area in Seoul, South Korea, standard penetration test data were meticulously pre-processed to remove outliers and missing values, ensuring a robust input for spatial analysis. Two predictive methods, 3D ordinary kriging and mosaic-based modeling, were applied to interpolate soil strength parameters essential for assessing seismic slope stability and liquefaction risk. The mosaic-based model demonstrated superior reliability, achieving a 43% improvement in prediction accuracy through leave-one-out cross-validation compared to ordinary kriging. The results highlight the importance of conservative modeling approaches for capturing the spatial variability of subsurface conditions in densely populated, high-risk areas. This research contributes to the development of data-driven strategies for mitigating compound and cascading geo-hazards, offering valuable insights for geotechnical earthquake engineering and urban risk management.
| Original language | English |
|---|---|
| Pages (from-to) | 194-201 |
| Number of pages | 8 |
| Journal | Geotechnical Special Publication |
| Volume | 2025-November |
| Issue number | GSP 369 |
| DOIs | |
| State | Published - 2025 |
| Event | Geo-Extreme 2025: Remote Sensing, Instrumentation, Big Data, and Decision Making - Long Beach, United States Duration: 2 Nov 2025 → 5 Nov 2025 |
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