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

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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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Acknowledgments

The financial support from National Science Council under Project Number NSC97-2221-E-020-022 is greatly appreciated. The historical seismic record provided by the Central Weather Bureau Seismological Center of Taiwan is also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Civil Engineering, National Pingtung University of Science and Technology, Pingtung, 91207, Taiwan

    Tienfuan Kerh & Yaling Chan

  2. Key Centre of Design Computing and Cognition, Faculty of Architecture, Design and Planning, University of Sydney, Sydney, NSW, 2006, Australia

    David Gunaratnam

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  1. Tienfuan Kerh
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  2. David Gunaratnam
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  3. Yaling Chan
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Correspondence to Tienfuan Kerh.

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Kerh, T., Gunaratnam, D. & Chan, Y. Neural computing with genetic algorithm in evaluating potentially hazardous metropolitan areas result from earthquake. Neural Comput & Applic 19, 521–529 (2010). https://doi.org/10.1007/s00521-009-0301-z

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