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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Mostafa Allameh Zadeh & Gholam Javan Doloiee & Ali Nasrollahnejad, 2018. "Prediction of Strong Ground Motion Using Fuzzy Inference Systems Based on Adaptive Networks," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 6(1), pages 17-31, April.
    • Handle: RePEc:adp:jbboaj:v:6:y:2018:i:1:p:17-31
    DOI: 10.19080/BBOAJ.2018.06.555680
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