TY - JOUR
T1 - Geospatial Mosaic-Based 3D Ground Model of SPT-Derived Subsurface Stiffness in Seoul, South Korea
AU - Chung, Taek Kyu
AU - Kim, Han Saem
AU - Sun, Chang Guk
AU - Chung, Choong Ki
N1 - Publisher Copyright:
© ASCE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105023839065
U2 - 10.1061/9780784486498.020
DO - 10.1061/9780784486498.020
M3 - Conference article
AN - SCOPUS:105023839065
SN - 0895-0563
VL - 2025-November
SP - 194
EP - 201
JO - Geotechnical Special Publication
JF - Geotechnical Special Publication
IS - GSP 369
T2 - Geo-Extreme 2025: Remote Sensing, Instrumentation, Big Data, and Decision Making
Y2 - 2 November 2025 through 5 November 2025
ER -