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Neural Computing and Applications
Published in: Neural Computing and Applications 4/2010

01-06-2010 | Original Article

Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake

Authors: Tienfuan Kerh, David Gunaratnam, Yaling Chan

Published in: Neural Computing and Applications | Issue 4/2010

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Abstract

In this study, neural network models improved by genetic algorithm were employed to estimate peak ground acceleration (PGA) at seven metropolitan areas in the island of Taiwan, which is frequently subject to earthquakes. By considering a series of historical seismic records, and using the seismic design value in the current building code as the evaluation criteria, two metropolitan areas, Taichung and Chiayi, were identified by computational results as having higher estimated horizontal PGAs than the recommended design values. The approach implemented in this study provides a new and good basis for solving this type of seismic problems in the region studied.
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Metadata
Title
Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake
Authors
Tienfuan Kerh
David Gunaratnam
Yaling Chan
Publication date
01-06-2010
Publisher
Springer-Verlag
Published in
Neural Computing and Applications / Issue 4/2010
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-009-0301-z

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