Multivariate geotechnical zonation of seismic site effects with clustering-blended model for a city area, South Korea

Abstract

The site classification system in the seismic design code and its dependent zonation should be guaranteed to represent the local spatial uncertainty of subsurface features, but have been uniformly used based on the site response parameters. Spatial interpolation-based zonation is only practically feasible if there are clear-cut stochastic/spatial correlations in geotechnical/geophysical measurements. The geology and terrain features can be substituted as an influential proxy for site amplification. To develop cluster-oriented zonation considering the spatial heterogeneity of the different site response parameters focusing on an uninvestigated area, this study proposes a new approach for multivariate site classification blended with geographic information system (GIS)-based spatial clustering and machine learning (ML)-based clustering ensemble technologies. GIS-based clustering characterizes a hot spot cluster with statistical and spatial correlation values of the site response parameters and defines the relative weight using the Gi∗Z-score as the index of spatial heterogeneity. ML-based clustering ensembles aim to combine the clustering model in terms of consistency and performance, and are designed for optimization through a consensus function by comparing the fitness with the site classification system to obtain better results than individual clustering algorithms. The novelty of the proposed workflow is the stepwise improvement of the proposed models compared with the zonation phases and practical methods.