Neural network-based generation of artificial spatially variable earthquakes ground motions

Hossein Ghaffarzadeh, Mohammad Mahdi Izadi, Nima Talebian

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

In this paper, learning capabilities of two types of Arterial Neural Networks, namely hierarchical neural networks and Generalized Regression Neural Network were used in a two-stage approach to develop a method for generating spatial varying accelerograms from acceleration response spectra and a distance parameter in which generated accelerogram is desired. Data collected from closely spaced arrays of seismographs in SMART-1 array were used to train neural networks. The generated accelerograms from the proposed method can be used for multiple support excitations analysis of structures that their supports undergo different motions during an earthquake.
Original languageEnglish
Pages (from-to)509-525
JournalEarthquake and Structures
Volume4
Issue number5
DOIs
Publication statusPublished - 23 May 2013
Externally publishedYes

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