Clustering analysis of probabilistic seismic hazard for the selection of ground motion time histories in vast areas

Abstract

We present a methodology for the selection of accelerometric time histories as input for dynamic response analyses over vast areas. The method is primarily intended for seismic microzonation studies and regional probabilistic seismic hazard assessments that account for site effects. It is also suitable for structural response analyses if one would like to use a fixed set of ground motion records for analyzing multiple structures with different (or unknown) periods. The proposed procedure takes advantage of unsupervised machine learning techniques to identify zones (i.e., groups of sites) with homogeneous seismic hazard, for which the same set of earthquake recordings can be reasonably used in the numerical simulations. The procedure consists of three steps: (1) data-driven cluster analysis to identify groups of sites with comparable seismic hazard levels for a specified mean return period (MRP); (2) for each zone, definition of a single, reference uniform hazard spectrum (UHS) corresponding to the MRP of interest; (3) selection of a set of accelerometric recordings that are consistent with the magnitude-distance scenarios contributing to the hazard of each zone, and meet the spectrum-compatibility requirement with respect to the reference UHS. An application of the procedure in the Po Plain (Northern Italy) is described in detail.