Robust attenuation relations for peak time-domain parameters of strong ground motions
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Mohammadnejad:2012:EES,
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author = "Ali Kafaei Mohammadnejad and
Seyyed Mohammad Mousavi and Mohammad Torabi and Mehdi Mousavi and
Amir Hossein Alavi",
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title = "Robust attenuation relations for peak time-domain
parameters of strong ground motions",
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journal = "Environmental Earth Sciences",
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year = "2012",
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volume = "67",
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number = "1",
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pages = "53--70",
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month = sep,
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keywords = "genetic algorithms, genetic programming, Time-domain
ground-motion parameters, Attenuation relationship,
Simulated annealing, Nonlinear modelling",
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publisher = "Springer",
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language = "English",
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ISSN = "1866-6280",
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URL = "http://link.springer.com/article/10.1007%2Fs12665-011-1479-9",
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DOI = "doi:10.1007/s12665-011-1479-9",
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size = "18 pages",
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abstract = "This study presents new attenuation models for the
estimation of peak ground acceleration (PGA), peak
ground velocity (PGV), and peak ground displacement
(PGD) using a hybrid method coupling genetic
programming and simulated annealing, called GP/SA. The
PGA, PGV, and PGD were formulated in terms of
earthquake magnitude, earthquake source to site
distance, average shear-wave velocity, and faulting
mechanisms. A worldwide database of strong ground
motions released by Pacific Earthquake Engineering
Research Center (PEER) was employed to establish the
models. A traditional genetic programming analysis was
performed to benchmark the proposed models. For more
validity verification, the GP/SA models were employed
to predict the ground-motion parameters of the Iranian
plateau earthquakes. Sensitivity and parametric
analyses were carried out and discussed. The results
show that the GP/SA attenuation models can offer
precise and efficient solutions for the prediction of
estimates of the peak time-domain characteristics of
strong ground motions. The performance of the proposed
models is better than or comparable with the
attenuation relationships found in the literature.",
- }
Genetic Programming entries for
Ali Kafaei Mohammadnejad
Seyyed Mohammad Mousavi
Mohammad Torabi
Mehdi Mousavi
A H Alavi
Citations