Production of Synthetic Seismic Records Using Fuzzy Neural Network

Subject Areas : Analysis of Structure and Earthquake

Peyman Shadman ¹ , Mehdi Amri ² , Mohammad Khorasani ³

1 - باشگاه پژوهشگران جوان
2 -
3 - دانشگاه علم و صنعت ایران

Received: 2015-11-02 Accepted : 2016-01-31 Published : 2016-02-24

Keywords: Artificial Accelerograms, Wavelet Packet Analysis, Wavelet Coefficients, FuzzyNeural Network,

Abstract :

There is a growing need for dynamic time history analysis and the absence of proper records in different areas has necessitatedthe production of artificiallaccelerograms compatible with the whole plan. This study presents a new approachbased on wavelet packet transform and artificial intelligence techniques to produce artificial earthquake accelerograms compatible with the whole plan. This approachtakes into account the magnitude and the distance from the fault. The study of neural networks and fuzzy wavelet packet analysis has been used to achieve the desired goal. To do so, first earthquake accelerograms have been collected according to specific site conditions, earthquake magnitude and distance from origin.Then all records have been gatehered for training with fuzzy neural network. Attenuation spectra have been developed on the basis of information in the area using nonlinear regression. Then using fuzzy neural networks, the relationship between earthquake records and the devloped spectra from each record is calculated. In this satge, using wavelet packet analysis, mapping acceleration are analyzed and converted intoaccelerograms (wavelet coefficients)