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Neural Computing and Applications

, Volume 19, Issue 4, pp 521–529 | Cite as

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

  • Tienfuan Kerh
  • David Gunaratnam
  • Yaling Chan
Original Article

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.

Keywords

Neural network Genetic algorithm Potentially hazardous metropolitan area Seismic record Peak ground acceleration 

Notes

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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Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  1. 1.Department of Civil EngineeringNational Pingtung University of Science and TechnologyPingtungTaiwan
  2. 2.Key Centre of Design Computing and Cognition, Faculty of Architecture, Design and PlanningUniversity of SydneySydneyAustralia

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