Journal Article10.1785/GSSRL.68.1.128
Equations for Estimating Horizontal Response Spectra and Peak Acceleration from Western North American Earthquakes: A Summary of Recent Work
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TL;DR: In this article, the authors provide tables for estimating random horizontal component peak acceleration and 5 percent damped pseudo-acceleration response spectra in terms of the natural, rather than common, logarithm of the ground-motion parameter.
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Abstract: In this paper we summarize our recently-published work on estimating horizontal response spectra and peak acceleration for shallow earthquakes in western North America. Although none of the sets of coefficients given here for the equations are new, for the convenience of the reader and in keeping with the style of this special issue, we provide tables for estimating random horizontal-component peak acceleration and 5 percent damped pseudo-acceleration response spectra in terms of the natural, rather than common, logarithm of the ground-motion parameter. The equations give ground motion in terms of moment magnitude, distance, and site conditions for strike-slip, reverse-slip, or unspecified faulting mechanisms. Site conditions are represented by the shear velocity averaged over the upper 30 m, and recommended values of average shear velocity are given for typical rock and soil sites and for site categories used in the National Earthquake Hazards Reduction Program's recommended seismic code provisions. In addition, we stipulate more restrictive ranges of magnitude and distance for the use of our equations than in our previous publications. Finally, we provide tables of input parameters that include a few corrections to site classifications and earthquake magnitude (the corrections made a small enough difference in the ground-motion predictions that we chose not to change the coefficients of the prediction equations).
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Citations
Bayesian Learning for Earthquake EngineeringApplications and Structural Health Monitoring
Chang Kook Oh
- 17 Sep 2007
TL;DR: In this paper, the authors applied the Bayesian methodology to inverse problems in earthquake engineering and especially to structural health monitoring, using either a Bayesian classification or regression approach, which is known to provide an efficient way to alleviate this illconditioning by incorporating the prior term for regularization of the inverse problem and to provide probabilistic results which are meaningful for decision making.
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Bayesian Learning for Earthquake Engineering Applications and Structural Health Monitoring
Chang Kook Oh
- 01 Jan 2008
TL;DR: This work focuses on the advantages of making probabilistic predictions obtained by Bayesian methods to deal with all uncertainties and the good characteristics of the proposed method in terms of computationally efficient training, and, especially, vi prediction that make it suitable for real-time operation.
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Application of Stochastic Simulation Methods to System Identification
Matthew M. Muto
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TL;DR: Stochastic simulation methods are applied to identify a deteriorating hysteretic model with sparse real data from a structure damaged in an earthquake show that the high-plausibility models demonstrate behavior consistent with the observed damage, indicating that there is promise in applying these methods to ill-conditioned problems in the real world.
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Seismic-Hazard Disaggregation: A Fully Probabilistic Methodology
Marco Pagani,Alberto Marcellini +1 more
TL;DR: In this paper, the authors presented a seismic-hazard disaggregation methodology based on the probabilistic approach introduced by Mortgat and Shah (1979), which computes the probability of exceedence of a disaggregated ground motion, gm d, characterized by a probability of at least one exceedence, P ( GM > gm D ), in a time interval, t, for given values of magnitude, m, distance, r, a measure of the deviation of the ground motion from its predicted value, ϵ, and the number of occurrences, n.
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