The 2017 Pohang earthquake (moment magnitude scale: 5.4) was South Korea’s second strongest earthquake in decades, and caused the maximum amount of damage in terms of infrastructure and human injuries. As the epicenters were located in regions with Quaternary sediments, which involve distributions of thick fill and alluvial geo-layers, the induced damages were more severe owing to seismic amplification and liquefaction. Thus, to identify the influence of site-specific seismic effects, a post-earthquake survey framework for rapid earthquake damage estimation, correlated with seismic site effects, was proposed and applied in the region of the Pohang earthquake epicenter. Seismic zones were determined on the basis of ground motion by classifying sites using the multivariate site classification system. Low-rise structures with slight and moderate earthquake damage were noted to be concentrated in softer sites owing to the low focal depth of the site, topographical effects, and high frequency range of the mainshocks.

A large amount of geotechnical investigation data is essential for the highly reliable design of geotechnical structure at a construction site. The number of geotechnical investigations, however, has been generally insufficient and spatially biased owing to economic and spatial-temporal limitations. In this study, a geotechnical three-dimensional spatial modeling was implemented using an optimized geostatistical interpolation approach at a bridge construction site in the south-central part of the Korean peninsula. The geotechnical investigation data were collected and standardized for the construction of a geo-database. For the site-specific stratification, a kriging-based integration of the geo-layers and the seismic velocity from a seismic refraction survey were applied. The value from a standard penetration test (SPT)-N of an uninvestigated location was predicted using parametric and nonparametric geostatistical methods. We accomplished three-dimensional spatial interpolations using ordinary kriging, a sequential Gaussian simulation with a normal score transformed dataset, and a sequential indicator simulation using the geodatabase. A leave-one-out cross validation was carried out to quantitatively evaluate the reliability of the three-dimensional modeling. Finally, a three-dimensional geotechnical spatial model assigned with subsurface stratification and SPT-N values was constructed using the sequential Gaussian simulation.