Markov-chain Monte Carlo ground-motion selection algorithms for conditional intensity measure targets

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Title: Markov-chain Monte Carlo ground-motion selection algorithms for conditional intensity measure targets
Authors: Shi, Y
Stafford, PJ
Item Type: Journal Article
Abstract: Two new algorithms are presented for efficiently selecting suites of ground motions that match a target multivariate distribution - or conditional intensity measure target. The first algorithm is a Markov-chain Monte Carlo (MCMC) approach in which records are sequentially added to a selected set such that the joint probability density function (PDF) of the target distribution is progressively approximated by the discrete distribution of the selected records. The second algorithm derives from the concept of the acceptance ratio within MCMC but does not involve any sampling. The first method takes advantage of MCMC's ability to efficiently explore a sampling distribution through the implementation of a traditional MCMC algorithm. This method is shown to enable very good matches to multivariate targets to be obtained when the numbers of records to be selected is relatively large. A weaker performance for fewer records can be circumvented by the second method which uses greedy optimization to impose additional constraints upon properties of the target distribution. A preselection approach based upon values of the multivariate PDF is proposed that enables near-optimal record sets to be identified with a very close match to the target. Both methods are applied for a number response analyses associated with different sizes of record sets and rupture scenarios. Comparisons are made throughout with the Generalized Conditional Intensity Measure (GCIM) approach. The first method provides similar results to GCIM, but with slightly worse performance for small record sets, while the second method outperforms method one and GCIM for all considered cases.
Issue Date: 10-Oct-2018
Date of Acceptance: 7-Jun-2018
ISSN: 0098-8847
Publisher: Wiley
Start Page: 1468
End Page: 1489
Journal / Book Title: Earthquake Engineering and Structural Dynamics
Volume: 47
Issue: 12
Copyright Statement: © 2018 John Wiley & Sons, Ltd. This is the pre-peer reviewed version of the following article, which has been published in final form at
Keywords: Science & Technology
Engineering, Civil
Engineering, Geological
conditional spectrum
intensity measures
ground-motion selection
Markov chain Monte Carlo
record selection
ground-motion selection
markov chain monte carlo
conditional spectrum
record selection
intensity measure
0905 Civil Engineering
Strategic, Defence & Security Studies
Publication Status: Published
Online Publication Date: 2018-07-11
Appears in Collections:Faculty of Engineering
Civil and Environmental Engineering

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