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An Attempt to Model the Relationship Between Mmi Attenuation and Engineering Ground-motion Parameters Using Artificial Neural Networks and Genetic Algorithms : Volume 10, Issue 12 (07/12/2010)

By Tselentis, G-a.

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Book Id: WPLBN0003989737
Format Type: PDF Article :
File Size: Pages 11
Reproduction Date: 2015

Title: An Attempt to Model the Relationship Between Mmi Attenuation and Engineering Ground-motion Parameters Using Artificial Neural Networks and Genetic Algorithms : Volume 10, Issue 12 (07/12/2010)  
Author: Tselentis, G-a.
Volume: Vol. 10, Issue 12
Language: English
Subject: Science, Natural, Hazards
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2010
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Tselentis, G., & Vladutu, L. (2010). An Attempt to Model the Relationship Between Mmi Attenuation and Engineering Ground-motion Parameters Using Artificial Neural Networks and Genetic Algorithms : Volume 10, Issue 12 (07/12/2010). Retrieved from http://worldpubliclibrary.org/


Description
Description: Seismological Laboratory, University of Patras, RIO 265 04, Patras, Greece. Complex application domains involve difficult pattern classification problems. This paper introduces a model of MMI attenuation and its dependence on engineering ground motion parameters based on artificial neural networks (ANNs) and genetic algorithms (GAs). The ultimate goal of this investigation is to evaluate the target-region applicability of ground-motion attenuation relations developed for a host region based on training an ANN using the seismic patterns of the host region. This ANN learning is based on supervised learning using existing data from past earthquakes. The combination of these two learning procedures (that is, GA and ANN) allows us to introduce a new method for pattern recognition in the context of seismological applications. The performance of this new GA-ANN regression method has been evaluated using a Greek seismological database with satisfactory results.

Summary
An attempt to model the relationship between MMI attenuation and engineering ground-motion parameters using artificial neural networks and genetic algorithms

Excerpt
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