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Prediction of Strong Ground Motion Using Fuzzy Inference Systems Based on Adaptive Networks

Mostafa Allameh Zadeh, Gholam Javan Doloiee and Ali Nasrollahnejad
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Mostafa Allameh Zadeh: Assistance Professor, International institute of seismology and earthquake engineering, Iran
Gholam Javan Doloiee: Assistance Professor, International institute of seismology and earthquake engineering, Iran
Ali Nasrollahnejad: Phd student, International institute of seismology and earthquake engineering, Iran

Biostatistics and Biometrics Open Access Journal, 2018, vol. 6, issue 1, 17-31

Abstract: Peak ground acceleration (PGA) estimates have been calculated in order to predict the devastation potential resulting from earthquakes in reconstruction sites. In this research, a training algorithm based on gradient descent were developed and employed by using strong ground motion records. The Artificial Neural Networks (ANN) algorithm indicated that the fitting between the predicted strong ground motion by the networks and the observed PGA values were able to yield high correlation coefficients of 0.78 for PGA.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:6:y:2018:i:1:p:17-31

DOI: 10.19080/BBOAJ.2018.06.555680

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