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Peak Ground Velocity Evaluation by Artificial Neural Network for West America Region

  • Ben-yu Liu
  • Liao-yuan Ye
  • Mei-ling Xiao
  • Sheng Miao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

With the Peak Ground Velocity 283 records in three dimensions, the velocity attenuation relationship with distance was discussed by neural network in this paper. The earthquake magnitude, epicenter distance, site intensity and site condition were considered as basic input element for the network. By using Bayesian Regularization Back Propagation Neural Networks (BRBPNN), the over-fitting phenomenon was reduced to some extent. The horizontal velocity was discussed. The PGV predicted by neural networks can simulate the detail difference with distance, while the PGV given by other traditional attenuation relationship only give a reduction relation with distance. The importance of each input factor was compared by the square weight of the input layer of the network. The order may be earthquake magnitude, epicenter distance and soil condition.

Keywords

Neural Network Neural Network Model Peak Ground Acceleration Epicenter Distance Earthquake Magnitude 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ben-yu Liu
    • 1
  • Liao-yuan Ye
    • 1
  • Mei-ling Xiao
    • 1
  • Sheng Miao
    • 1
  1. 1.Institute of Public Safety and Disaster PreventionYunnan UniversityKunmingChina

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