An Efficient Computational Platform for Selecting and Scaling Ground Motion Records while Considering Multiple Target Spectra

Merdad Shokrabadi, Y. Bozorgnia, H.V. Burton, and M. Askari
March 2022

Latest Release Date: March 2022
Sponsored by the United States Geological Survey (USGS)


Mehrdad Shokrabadi, Ph.D., Yousef Bozorgnia, Ph.D., Henry V. Burton, Ph.D., and Mohammad Askari, M.Sc. Department of Civil and Environmental Engineering University of California, Los Angeles
Jack W. Baker, Ph.D. Department of Civil and Environmental Engineering Stanford University

Abstract

We developed an efficient computational platform to select and consistently scale recorded earthquake ground motions. Given a set of target response spectra for horizontal and vertical ground motion components, considering period-dependent record-to-record variabilities, a set of ground motions is selected and scaled such that: (1) the mean spectra of the selected and scaled horizontal motions would follow the target horizontal spectra; (2) the mean spectra of the selected and scaled vertical motions would follow the target vertical spectra; (3) the selected set of horizontal spectra would preserve the prescribed period-dependent record-to-record variability for the horizontal component; (4) the selected set of vertical spectra would match the prescribed period-dependent record-to-record variability for the vertical component; and (5) for each set of horizontal and vertical components, a single scaling factor is used; thus, preserving the relative amplitude and phasing of the original recorded horizontal and vertical components. Additionally, significant improvement in computational efficiency is achieved by employing a modified version of a greedy record selection algorithm. More specifically, the run time of the modified algorithm is significantly reduced by utilizing the parallelization capabilities that are present in most modern desktop and laptop computers. The process is demonstrated for sites where the hazard is dominated by shallow crustal earthquakes.

How to Cite this Tool:

Shokrabadi, Mehrdad; Bozorgnia, Yousef; Burton, Henry V.; Baker, Jack W.; Askari, Mohammad (2022): An Efficient Computational Platform for Selecting and Scaling Ground Motion Records while Considering Multiple Target Spectra. https://doi.org/10.34948/N3K01N

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ACKNOWLEDGMENTS

This research study is partially supported by Berkshire Hathaway Specialty Insurance and the National Science Foundation Award No. 1554714. Their support is gratefully acknowledged. The opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the study sponsors, the B. John Garrick Risk Institute, or the Regents of the University of California.