Abstract
Abstract: 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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computer-Aided Civil and Infrastructure Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
