ARTICLE
Effects of advanced seismic analysis with ARMA models: Assessing damage impact and hysteresis in earthquake time series
 
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1
Civil Engineering Department, Technology Faculty, M’sila University, Algeria
 
2
Laghouat Normal School (ENSL), Algeria
 
3
Research Laboratory in Civil Engineering, Biskra University, Algeria
 
 
Submission date: 2024-01-22
 
 
Final revision date: 2024-12-06
 
 
Acceptance date: 2024-12-28
 
 
Online publication date: 2025-04-24
 
 
Corresponding author
Abderrazek Menasri   

Civil Engineering Department, Technology Faculty, M’sila University, Algeria
 
 
 
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ABSTRACT
Actual records of strong motion acceleration cannot meet engineering requirements. The simulated seismic waves become an equivalent source of seismic wave input. The time-varying autoregressive moving average (ARMA) process is a straightforward and effective method for simulating seismic ground motions. This model can accurately replicate the non-stationary amplitude and frequency characteristics of seismic ground accelerations, making it useful for assessing damage indicators, which are then compared to spectra derived from real seismic records. It is demonstrated that the chosen ARMA(2,1) model and the applied algorithm effectively capture the characteristics of real seismic records, even with the varying frequency content.
REFERENCES (16)
1.
Ay, A.M. & Wang, Y. (2014). Structural damage identification based on self-fitting ARMAX model and multi-sensor data fusion. Structural Health Monitoring, 13 (4), 445–460. https://doi.org/10.1177/147592....
 
2.
Bodeux, J.B. & Golinval, J.C. (2001). Application of ARMAV models to the identification and damage detection of mechanical and civil engineering structures. Smart Materials and Structures, 10 (3), 479–489. https:/doi.org/10.1088/0964-1726/10/3/309.
 
3.
Box, G.E., Jenkins, G.M., Reinsel, G.C., & Ljung, G.M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
 
4.
Brahimi, M. (1989). The use of ARMA models for earthquake response spectra. Polytechnic Institute of Brooklyn.
 
5.
Brahimi, T. & Smain, T. (2021). A nonstationary mathematical model for acceleration time series. Mathematical Modelling of Engineering Problems, 8 (2), 246–252. https://doi.org/10.18280/mmep.....
 
6.
Carden, E.P. & Brownjohn, J.M. (2008). ARMA modelled time-series classification for structural health monitoring of civil infrastructure. Mechanical Systems and Signal Processing, 22 (2), 295–314. https://doi.org/10.1016/j.ymss....
 
7.
El-Choum, M.K. (2014). Utilization of ARMA models to measure damage potential in seismic records. In K. Chantawarangul (Ed.). Sustainable Solutions in Structural Engineering and Construction (pp. 138-143). Fargo, USA: ISEC Press. http://dx.doi.org/10.14455/ISE....
 
8.
Johnson, E.A., Lam, H.F., Katafygiotis, L.S., & Beck, J.L. (2004). Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data. Journal of Engineering Mechanics, 130 (1), 3–15. https://doi.org/10.1061/(ASCE)....
 
9.
Luo, W.F. & Yu, L. (2017). New damage-sensitive feature for structures with bolted joints. Journal of Physics: Conference Series, 842 (1), Article 012083. IOP Publishing. https://doi.org/10.1088/1742-6....
 
10.
Menasri, A. (2012). Simulation of strong motion with the ARMA technique (in French). [Doctoral dissertation, Ecole Nationale Polytechnique].
 
11.
Menasri, A., Brahimi, M., Frank, R., & Bali, A. (2012). ARMA modeling of artificial accelerograms for Algeria. Applied Mechanics and Materials, 105–107, 348–355. https://doi.org/10.4028/www.sc....
 
12.
Nair, K.K., Kiremidjian, A.S., & Law, K.H. (2006). Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. Journal of Sound and Vibration, 291 (1–2), 349–368. https://doi.org/10.1016/j.jsv.....
 
13.
Ouzandja, D., Messaad, M., Berrabah, A.T., & Belhrizi, M. (2023). Impact of material nonlinearity of dam-foundation rock system on seismic performance of concrete gravity dams. Journal of Theoretical and Applied Mechanics, 61 (1), 49–63. https://doi.org/10.15632/jtam-....
 
14.
Sakellariou, J.S. & Fassois, S.D. (2006). Stochastic output error vibration-based damage detection and assessment in structures under earthquake excitation. Journal of Sound and Vibration, 297 (3–5), 1048–1067. https://doi.org/10.1016/j.jsv.....
 
15.
Zhang, Q.W. (2007). Statistical damage identification for bridges using ambient vibration data. Computers & Structures, 85 (7–8), 476–485. https://doi.org/10.1016/j.comp....
 
16.
Zheng, H. & Mita, A. (2007). Two-stage damage diagnosis based on the distance between ARMA models and pre-whitening filters. Smart Materials and Structures, 16 (5), Article 1829. https://doi.org/10.1088/0964-1....
 
eISSN:2543-6309
ISSN:1429-2955
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