The impact of a PEER funded research project, “Ground Motions and Selection Tools for PEER Research Program,” is highlighted below. The project Principal Investigator (PI) is Jack W. Baker, Stanford University. The research team includes Cynthia Lee, Graduate Student Researcher, Stanford University.
Selection of ground motions is a topic of great interest as dynamic structural analysis, which requires ground motions as inputs, grows more prevalent. This selection typically involves searching a ground motion database to find time series produced under appropriate seismological conditions (e.g., earthquake magnitude and source-to-site distance), and that have appropriate response spectral values. In some cases, ground motions are selected based on their individual match to a target spectrum; that is, an optimal set of ground motions would have spectra that all perfectly match the target spectrum. In other cases, however, it is important that the ground motions have variability in response spectra that accurately represents target distributions from predictive models. As such, a number of algorithms have been proposed to select ground motions with some form of specified response spectral variability.
This project developed an efficient algorithm for selecting ground motions from a database that match a target response spectrum distribution (e.g., a Conditional Spectrum). The motivation for this work is that when the target spectrum has a distribution, rather than a single value, it is not possible to evaluate individual ground motions for selection without considering them as part of a suite of ground motions that collectively represent the distribution. But evaluating all possible suites of ground motions is impossible when considering large ground motion databases typical in practice today. This algorithm utilizes several practical strategies to quickly identify ground motion sets with a close match to the target spectrum.