Computationally Efficient Vector-Valued Seismic Risk Analysis of Engineering Structures
Publication: Journal of Structural Engineering
Volume 142, Issue 9
Abstract
Probabilistic seismic risk analysis (SRA) is often used for accurately quantifying seismic risk of critical engineering structures. It combines the probabilistic model of the behavior of structural response given a ground motion parameter (GMP) (e.g., seismic fragility model) and the probabilistic seismic hazard analysis (PSHA) for the GMP in a mathematically rigorous manner. In current engineering practice, SRA is performed based on a single GMP. For structures whose responses can be better predicted using multiple GMPs, a vector-valued SRA (VSRA) gives more accurate estimates of risk. However, due to the extensive computational efforts required for the multiple-dimensional cases, VSRA has not been widely applied in engineering practice. This paper presents a simplified approach to VSRA, which can substantially improve computational efficiency without losing accuracy. A new seismic hazard deaggregation procedure is proposed to determine a set of controlling earthquakes in terms of magnitude, source-site distance, and occurrence rate for the site of interest. VSRA is then performed based on the controlling earthquakes rather than all possible earthquake occurrences. Two numerical examples are presented to validate the effectiveness and accuracy of the simplified approach. Factors affecting the approximations in the simplified approach are discussed.
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Acknowledgments
The research for this paper was supported, in part, by the Natural Sciences and Engineering Research Council of Canada (NSERC) and University Network of Excellence in Nuclear Engineering (UNENE).
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Information & Authors
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Published In
Journal of Structural Engineering
Volume 142 • Issue 9 • September 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Mar 13, 2015
Accepted: Jan 4, 2016
Published online: Apr 1, 2016
Published in print: Sep 1, 2016
Discussion open until: Sep 1, 2016
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