ARMA Modeling of Artificial Accelerograms for Algeria

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The main aim of this study is to examine on the real and simulated earthquakes effects. This paper deals with the use of ARMA models in earthquake engineering. The time-varying auto regressive moving average (ARMA) process is used as a simple yet efficient method for simulating earthquake ground motions. This model is capable of reproducing the nonstationary amplitude as well as the frequency content of the earthquake ground accelerations. The moving time-window technique is applied to synthesize the near field earthquakes, Chlef-1, Chlef-2, Chlef-3 and Attaf 1980 recorded on dense soils in Algeria. This model, is based on a low-order, time-invariant ARMA process excited by Gaussian white noise and amplitude modulated using a simple envelope function to account for the non-stationary characteristics. This simple model gives a reasonable fit to the observed ground motion. It is shown that the selected ARMA (2,1) model and the algorithm used for generating the accelerograms are able to preserve the features of the real earthquake records with different frequency content. In this evaluation, the linear and non linear responses of a given soil layer have been adopted. This study suggests the ability to characterize the earthquake by a minimum number of parameters.

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Periodical:

Edited by:

Paul P. Lin and Chunliang Zhang

Pages:

348-355

DOI:

10.4028/www.scientific.net/AMM.105-107.348

Citation:

A. Menasri et al., "ARMA Modeling of Artificial Accelerograms for Algeria", Applied Mechanics and Materials, Vols. 105-107, pp. 348-355, 2012

Online since:

September 2011

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$38.00

[1] Lam N, Wilson J and Hutchiston G (2000), Generation of synthetic earthquake accelerograms using seismological modeling, A Review Journal of earthquake engineering, 4(3), 321-354.

DOI: 10.1080/13632460009350374

[2] Fu Huimin and Wang Zhihua(2004), Method for determining parametric functions of generalized time-varying ARMA mode, , Journal of Mechanical Strength, 26(6): 636-641(in Chinese).

[3] Turkstra, C. J., Tallin, A. G., Brahimi, M., and Kim, H. -J., Applications of ARMA Models for Seismic Damage Prediction, Probabilistic Methods in Civil Engineering, Proceedings 5th ASCE Specialty Conference, P. D. Spanos, ed., 1988, pp.277-280.

[4] Brahimi M and Berri S (2006).

[5] Findell K L, Koyluglu H U and Cakamak A S (1993), Modeling and simulating earthquake accelerograms using strong motion data from the Istanbul, Turkey region, Soil Dynamic and Earthquake Engineering, 12, 51-59.

DOI: 10.1016/0267-7261(93)90056-w

[6] Chang M K, Kwiatokowski J W, Nau R F, Oliver R M and Pister K K (1982), ARMA model for earthquake ground motions, Earthquake Engineering and Structural Dynamics, 10, 651-662.

DOI: 10.1002/eqe.4290100503

[7] Sabetta F and Dugliese A(1996) , Estimation of Response Spectra and simulation of nonstationary earthquake ground motion, BSSA, 86(2), 337-352.

[8] Pradlwarter H. J and Schuëller G.I. (1997) , On Advanced MCS Procedures in Stochastic Structural Dynamics, Journal of Nonlinear Mechanics, Vol. 32, No. 4, p.735 – 744 7.

[9] Hamilton DC and Watts D (1978), Interpreting partial autocorrelation functions of seasonal time series models, , Biometrika , 65(1): 135-140 doi: 10. 1093/biomet/65. 1. 135.

DOI: 10.1093/biomet/65.1.135

[10] Akaike H (1974), IEEE Transactions on Automatic Control, Vol. 19, (1974), p.716.

[11] Wang Zhihua, Fu Huimi(2006), A method for time-varying series analysis, Journal of Mechanical Strength, 28(3): 353-357(in Chinese).

[12] Newmark NM and Rosenblueth E (1971), Fundamentals of Earthquake Engineering, Prentice Hall, in Englewood Cliffs, N. J.

DOI: 10.1002/eqe.4290040108

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