Abstract
This paper presents a novel method of data-based probabilistic seismic hazard analysis (PSHA) and ground motion simulation, verified using previously recorded strong-motion data and machine-learning techniques. The procedure consists of three parts: (1) selection of an orthonormal set of basis vectors called eigenquakes to represent characteristic earthquake records; (2) estimation of response spectra for the anticipated level of shaking for a scenario earthquake at a site using Gaussian process regression; and (3) optimal combination of the eigenquakes to generate time series of ground acceleration consistent with the response spectral ordinates obtained in the second part. The paper discusses the benefits of applying such machine-learning methods to strong-motion databases for PSHA and ground motion simulation, particularly in large urban areas where dense instrumentation is available or expected. The effectiveness of the proposed methodology is exhibited using four scenario examples for downtown Los Angeles. Advantages, disadvantages, and future research needs for this machine-learning approach to PSHA are discussed.
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Acknowledgments
The Earthquake Engineering Research Institute (EERI)/FEMA National Earthquake Hazards Reduction Program (NEHRP) Professional Fellowship and a California Institute of Technology (Caltech) Visiting Associateship awarded to the first author provided financial, logistical, and technical support for this project. Their support is greatly appreciated by both authors. Caltech’s Center for Advanced Computing Research (CACR) was instrumental in providing needed computational resources for the parallel processing tasks involved in this research, and their assistance is greatly appreciated. The work in this paper used recorded data that were provided by the PEER-NGA program through Dr. Yousef Bozorgnia of the University of California at Berkeley and by Professor Masumi Yamada of Kyoto University. Professor Sami Masri of the University of Southern California provided great advice during this study for which both authors are thankful.
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Journal of Engineering Mechanics
Volume 141 • Issue 4 • April 2015
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© 2014 American Society of Civil Engineers.
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Received: Sep 26, 2013
Accepted: Aug 18, 2014
Published online: Sep 17, 2014
Published in print: Apr 1, 2015
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