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 language | English |
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Title of host publication | Proceedings of the First International Postgraduate Conference on Engineering, Designing and Developing the Built Environment for Sustainable Wellbeing, 27-29 April 2011, Brisbane, Queensland |
Publisher | Queensland University of Technology |
Pages | 189-193 |
Number of pages | 5 |
ISBN (Print) | 9780980582741 |
Publication status | Published - 2011 |
Event | International Postgraduate Conference on Engineering_Designing and Developing the Built Environment for Sustainable Wellbeing - Duration: 27 Apr 2016 → … |
Conference
Conference | International Postgraduate Conference on Engineering_Designing and Developing the Built Environment for Sustainable Wellbeing |
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Period | 27/04/16 → … |
Keywords
- earthquakes
- parameter estimation
- artificial neural network
- shear waves