APPLICATION OF NEURAL NETWORK TO PREDICT STRONG GROUND MOTION FOR HIMALAYAN REGION.

  • Department of Civil Engineering, Arni University, Kathgarh, (H.P), India.
  • # 207, Back suit, Global Change Center National Taiwan University.
  • Department of Earthquake Engineering, Indian Institute of Technology Roorkee-247667.
  • Department of Geophysics, Banaras Hindu University Varanasi.
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To design engineering structures at a particular region, it requires the information about the characteristics of strong ground motion. Peak Ground Acceleration (PGA) is most frequently used parameter to characterize such ground motions. Ground motion predictions using regression analysis are commonly used for estimating these loading conditions by using strong ground motion data from previous recorded earthquakes. Artificial Neural Networks (ANNs) are efficient computing models which have shown their strengths in solving many complex problems in numerous fields. A data set of 398 strong ground motion records from 69 earthquakes (3.0≤M≤6.8) occurred in Himalayan region is used in this study. Multi-layer perceptron architecture with the error back-propagation learning algorithm has been adopted to estimate peak ground accelerations for the Himalayan earthquakes. The PGAs predicted by the ANN have been compared with PGAs obtained by regression analysis. From these observations it has been concluded that the perceptron model is quite promising for the estimation of peak ground acceleration. Results of the predicted PGA have indicated that ANN is a promising tool for the estimation of peak ground acceleration at a site.

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[Arjun Kumar, Himanshu Mittal, Rajiv Sachdeva and Rohtash Kumar. (2017); APPLICATION OF NEURAL NETWORK TO PREDICT STRONG GROUND MOTION FOR HIMALAYAN REGION. Int. J. of Adv. Res. 5 (Jun). 1059-1066] (ISSN 2320-5407). www.journalijar.com

Dr. Arjun Kumar
Department of Civil Engineering

DOI:

Article DOI: 10.21474/IJAR01/4508      
DOI URL: https://dx.doi.org/10.21474/IJAR01/4508

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