Estimation of strong ground motion prameters using artificial neural networks

H. Bakhshi, G. Ghodrati Amiri, M. A. Barkhordari, H. R. Ronagh

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

Abstract

![CDATA[This paper aims to predict ground acceleration, speed, and maximum displacement using Artificial Neural Network (ANN). Database of Next Generation Attenuation (NGA) project are employed for training and testing of the neural network. In the selected learning algorithm, the average speed of the shear wave in the top 30 metres, focal depth, magnitude and distance to source are the input variables, and Peak Ground Acceleration, Velocity and Displacement (PGA, PGV and PGD) values are used as output. Close match between the predicted values of the deployed method with the observed values and its ability to reduce or even eliminate the uncertainties in the attenuation relationships show that this method can be used as a reliable method for predicting the main parameters of strong ground motions. The results indicate successful performance for the artificial neural network algorithm in predicting the expected results. They also show that the faults with reverse-oblique mechanism in constant earthquake magnitude and assuming equal average speed of the shear wave and focal depth generate higher values for PGA, PGV and PGD Parameters.]]
Original languageEnglish
Title of host publicationProceedings of the First International Postgraduate Conference on Engineering, Designing and Developing the Built Environment for Sustainable Wellbeing, 27-29 April 2011, Brisbane, Queensland
PublisherQueensland University of Technology
Pages189-193
Number of pages5
ISBN (Print)9780980582741
Publication statusPublished - 2011
EventInternational Postgraduate Conference on Engineering_Designing and Developing the Built Environment for Sustainable Wellbeing -
Duration: 27 Apr 2016 → …

Conference

ConferenceInternational Postgraduate Conference on Engineering_Designing and Developing the Built Environment for Sustainable Wellbeing
Period27/04/16 → …

Keywords

  • earthquakes
  • parameter estimation
  • artificial neural network
  • shear waves

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