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Generation of Near‐Field Artificial Ground Motions Compatible with Median‐Predicted Spectra Using PSO‐Based Neural Network and Wavelet Analysis
Author(s) -
Amiri G. Ghodrati,
Abdolahi Rad A.,
Aghajari S.,
Khanmohamadi Hazaveh N.
Publication year - 2012
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2012.00783.x
Subject(s) - particle swarm optimization , artificial neural network , principal component analysis , wavelet , moment magnitude scale , attenuation , algorithm , mathematics , computer science , pattern recognition (psychology) , artificial intelligence , physics , geometry , optics , scaling
The principal purpose of this article is to present a novel method based on particle swarm optimization (PSO) and wavelet packet transform (WPT) techniques and multilayer feed‐forward (MLFF) neural networks, in order to generate spectrum‐compatible near‐field artificial earthquake accelerograms. PSO is employed to optimize the weight values of the networks. Moreover, to improve the training efficiency principal component analysis (PCA) is used, as a reduction technique. The proposed PSO‐based MLFF (PSOBMLFF) neural network develops an inverse mapping from compacted and reduced spectrum coefficients into metamorphosed accelerogram wavelet packet coefficients. In this research, to produce compatible synthetic long‐period near‐field ground motions with median predicted spectra, the attenuation of peak ground velocity (PGV) with the close horizontal distance (R), moment magnitude (M), and time‐average shear‐wave velocity from the surface to 30 m (Vs30) has been developed using nonlinear regression analysis.