New method for the estimation of strong ground motions based on the colonial competitive algorithm

This paper is aimed at presenting a new method on the basis of the colonial competitive algorithm to predict horizontal peak ground acceleration and spectral acceleration. The colonial competitive algorithm was developed over the last few years in an attempt to overcome inherent limitations of traditional optimize method. The proposed method employs the optimization capabilities of the colonial competitive algorithm to determine the coefficients of attenuation relationships of peak ground acceleration and spectral acceleration. This method was applied to an ensemble of earthquake records of two seismic zones, namely Zagros and Alborz-Central Iran. The obtained results clearly reveal that the colonial competitive algorithm can be viewed as a powerful and reliable tool for solving complex optimization problems such as attenuation relationship.

[1]  R. Mirza Hessabi,et al.  Ground-Motion Prediction Equations of Spectral Ordinates and Arias Intensity for Iran , 2009 .

[2]  Caro Lucas,et al.  Colonial Competitive Algorithm as a Tool for Nash Equilibrium Point Achievement , 2008, ICCSA.

[3]  F. Manouchehri Dana,et al.  Attenuation Relationships for Iran , 2007 .

[4]  M. Takin Iranian Geology and Continental Drift in the Middle East , 1972, Nature.

[5]  H. Razeghi,et al.  Detection and Estimation of Damage in Structures Using Imperialist Competitive Algorithm , 2012 .

[6]  Caro Lucas,et al.  Imperialist competitive algorithm for minimum bit error rate beamforming , 2009, Int. J. Bio Inspired Comput..

[7]  Abdollah Bagheri,et al.  Application of neural networks and an adapted wavelet packet for generating artificial ground motion , 2011 .

[8]  Abdollah Bagheri,et al.  Optimal control of structures under earthquake excitation based on the colonial competitive algorithm , 2014 .

[9]  A. Nowroozi,et al.  Seismotectonic provinces of Iran , 1976 .

[10]  W. B. Joyner,et al.  Methods for regression analysis of strong-motion data , 1993, Bulletin of the Seismological Society of America.

[11]  Jamshid Ghaboussi,et al.  New method of generating spectrum compatible accelerograms using neural networks , 1998 .

[12]  J. Bommer,et al.  PREDICTION OF HORIZONTAL RESPONSE SPECTRA IN EUROPE , 1996 .

[13]  Abdollah Bagheri,et al.  Estimation of spectral acceleration based on neural networks , 2014 .

[14]  I. Ahmad,et al.  Neural Network Based Attenuation of Strong Motion Peaks in Europe , 2008 .

[15]  Abdollah Bagheri,et al.  Application of wavelet multiresolution analysis and artificial intelligence for generation of artificial earthquake accelerograms , 2008 .

[16]  G. Ghodrati Amiri,et al.  Generation of Multiple Earthquake Accelerograms Compatible with Spectrum Via the Wavelet Packet Transform and Stochastic Neural Networks , 2009 .

[17]  K. Campbell,et al.  NGA Ground Motion Model for the Geometric Mean Horizontal Component of PGA, PGV, PGD and 5% Damped Linear Elastic Response Spectra for Periods Ranging from 0.01 to 10 s , 2008 .

[18]  J. Stocklin Structural History and Tectonics of Iran: A Review , 1968 .

[19]  Caro Lucas,et al.  Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition , 2007, 2007 IEEE Congress on Evolutionary Computation.

[20]  G. Atkinson,et al.  Ground-Motion Prediction Equations for the Average Horizontal Component of PGA, PGV, and 5%-Damped PSA at Spectral Periods between 0.01 s and 10.0 s , 2008 .

[21]  Miguel P. Romo,et al.  Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks , 2007 .

[22]  W. J. Hall,et al.  An Overview of Selected Seismic Hazard Analysis Methodologies , 1994 .

[23]  Jamshid Ghaboussi,et al.  Generating multiple spectrum compatible accelerograms using stochastic neural networks , 2001 .

[24]  N. Ambraseys,et al.  A history of Persian earthquakes , 1982 .

[25]  Maurice S. Power,et al.  An Overview of the NGA Project , 2008 .