Time Series Analysis Methodology for Damage Detection in Civil Structures

Öz

Structural health monitoring methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an autoregressive (AR) model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Anahtar Kelimeler

• Structural health monitoring
• auto-regressive models
• singular value decomposition
• damage localization
• residual analysis
• impact testing

Time Series Analysis Methodology for Damage Detection in Civil Structures

Öz

Structural health monitoring (SHM) methodologies employing data-driven techniques are becoming increasingly popular for detection of structural damage at the earliest stage possible. With measured vibration signals from the structure, time series modeling methods provide quantitative means for extracting such features that can be utilized for damage diagnosis. In this study, one-step prediction error of an autoregressive (AR) model over a data set is used as damage indicator. In particular, the difference between the prediction of the AR model that is fit to the measured acceleration signal obtained from the intact structure and actual measured signals collected for different damage states of the structure are interrogated for diagnosis purposes. More specifically, the standard deviation of the residual error is employed to locate the damaged region. Singular-value decomposition (SVD) is employed to find the optimal order for an AR model created using the impulse responses of the system. Numerical simulations are carried out using the impulse responses acquired from a four-story frame structure contaminated with additive noise including single and multiple damaged elements. The results of the simulations demonstrate that the method can be effectively employed to detect and locate damage. The performance of the proposed procedure are further demonstrated using the impact data acquired from a reinforced concrete frame for real applications.

Anahtar Kelimeler

• Structural health monitoring
• auto-regressive models
• singular value decomposition
• damage localization
• residual analysis
• impact testing

Kaynakça

1. [1] C. R. Farrar, S.W. Doebling, and D. A. Nix, "Vibration–based structural damage identification," Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 359.1778 (2001): 131-149.
2. [2] S. W. Doebling, C. R Farrar, M. B. Prime, and D. W. Shevitz, “Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review,” United States: N. p., 1996. Web. doi:10.2172/249299.
3. [3] S. W. Doebling, C. R. Farrar, and M. B. Prime, "A summary review of vibration-based damage identification methods," Shock and vibration digest 30.2 (1998): 91-105.W.-K. Chen, Linear Networks and Systems. Belmont, CA, USA: Wadsworth, 1993, pp. 123–135.
4. [4] D. Montalvao, N. M. M. Maia, and A. M. R. Ribeiro, "A review of vibration-based structural health monitoring with special emphasis on composite materials," Shock and vibration digest 38, no. 4 (2006): 295-324.
5. [5] H. Sohn, J. A. Czarnecki, and C. R. Farrar, "Structural health monitoring using statistical process control," Journal of structural engineering 126, no. 11, 2000, 1356-1363.
6. [6] H. Sohn, C. Farrar, N. Hunter, and K. Worden, Applying the LANL statistical pattern recognition paradigm for structural health monitoring to data from a surface-effect fast patrol boat. No. LA-13761-MS. Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2001.
7. [7] M. Gul, F. N. Catbas, and M. Georgiopoulos, "Application of pattern recognition techniques to identify structural change in a laboratory specimen," In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 6529, 2007, pp. 556-565. SPIE.
8. [8] P. Omenzetter, and J. M. W. Brownjohn, "Application of time series analysis for bridge monitoring." Smart Materials and Structures 15, no. 1, 2006, 129-138,

9. [9] K. K. Nair, A. S. Kiremidjian, and K. H. Law, "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure," Journal of Sound and Vibration 291, no. 1-2, 2006, 349-368.
10. [10] K. K. Nair, and A. S. Kiremidjian, "Time Series Based Structural Damage Detection Algorithm Using Gaussian Mixtures Modeling," ASME. J. Dyn. Sys., Meas., Control, May 2007; 129(3), 285–293. https://doi.org/10.1115/1.2718241
11. [11] A. Entezami, H. Shariatmadar, and A. Karamodin, "Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods," Structural Health Monitoring 18, no. 5-6, 2019, 1416-1443.
12. [12] F. P. Kopsaftopoulos, and S. D. Fassois, "Vibration based health monitoring for a lightweight truss structure: experimental assessment of several statistical time series methods," Mechanical Systems and Signal Processing 24, no. 7, 2010, 1977-1997.
13. [13] E. Carden, J. Brownjohn, “ARMA modelled time-series classification for structural health monitoring of civil infrastructure,” Mechanical Systems and Signal Processing 22 (2), 2008, 295–314.
14. [14] P. J. Brockwell and R. A. Davis, “Time series: Theory and methods," Springer, New York, 1991.
15. [15] M. B. Priestley, “Spectral Analysis and Time Series,” New York: Academic Press Limited, 1981, pp. 501–612.
16. [16] D. S. Broomhead and G. P. King, “Extracting qualitative dynamics from experimental data,” Physica D, 20, 1986, 217–236.
17. [17] F. Takens, “Detecting strange attractors in turbulence,” Lecture Notes in Mathematics, 898, 1981, 365–381.
18. [18] Q. He, X. Wang, Q. Zhou, “Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis,” Sensors, 2014, 14, 382–402.
19. [19] Y. Geng, and X. Zhao, “Optimization of Morlet wavelet scale based on energy spectrum of singular values,” J. Vib. Shock, 2015, 34, 133–139.
20. [20] B. Gunes and O. Gunes, Gunes, "Vibration-based damage evaluation of a reinforced concrete frame subjected to cyclic pushover testing," Shock and Vibration, 2021, 1-16.