Volume 4, Issue 1 (9-2019)                   NMCE 2019, 4(1): 49-61 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Ahmadian V, Beheshti Aval S B, Darvishan E. Real-time damage detection of bridges using adaptive time-frequency analysis and ANN. NMCE 2019; 4 (1) :49-61
URL: http://nmce.kntu.ac.ir/article-1-235-en.html
1- MSc., Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran.
2- Associate Professor, Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran. , beheshti@kntu.ac.ir
3- Assistant Professor, Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran.
Abstract:   (666 Views)
Although traditional signal-based structural health monitoring algorithms have been successfully employed for small structures, their application for large and complex bridges has been challenging due to non-stationary signal characteristics with a high level of noise. In this paper, a promising damage detection algorithm is proposed by incorporation of adaptive signal processing and Artificial Neural Network (ANN). First, three adaptive signal processing techniques including Empirical Mode Decomposition (EMD), Local Mean Decomposition (LMD) and Hilbert Vibration Decomposition (HVD) are compared. The efficacy of these methods is examined for several numerically simulated signals to find a reliable signal processing tool. Then, three signal features are compared to find the most sensitive feature to damage. In the next step, an ANN ensemble is utilized as a classifier. Traditional statistical features and energy indices are used as the network input and output to make real-time detection of damage possible. The strength of this approach lies with training the network only based on healthy state of the structure. Having a trained ANN, online processing can be made to find a possible damage. Results show that the proposed algorithm has a good capacity as an online output-only damage detection method.
Full-Text [PDF 1348 kb]   (431 Downloads)    
Type of Study: Research | Subject: General
Received: 2019/05/15 | Revised: 2019/07/1 | Accepted: 2019/08/1 | ePublished ahead of print: 2019/08/15

References
1. [1] Chen Z., Zhou X, Wang X, Dong L, Qian Y. Deployment of a smart structural health monitoring system for long-span arch bridges: A review and a case study. Sensors, 2017, 17(9): 2151. [DOI:10.3390/s17092151]
2. [2] Hou Z, Hera, A, Noori M. Wavelet-Based Techniques for Structural Health Monitoring. In: Health Assessment of Engineered Structures, World Scientific Publishing, 2013: 179-201. [DOI:10.1142/9789814439022_0007]
3. [3] Du R Engineering monitoring and diagnosis using wavelet transforms. In Computer-Aided Design, Engineering, and Manufacturing, CRC Press, 2019: 312-341.
4. [4] Yan R, Chen X, Mukhopadhyay S C. Structural Health Monitoring: An Advanced Signal Processing Perspective, Springer, 2017.
5. [5] Zhang R R, King R, Olson L, and Xu Y L. Dynamic response of the Trinity River Relief Bridge to controlled pile damage: modeling and experimental data analysis comparing Fourier and Hilbert-Huang techniques. Journal of Sound and Vibration, 2005, 285(4): 1049-1070.‌ [DOI:10.1016/j.jsv.2004.09.032]
6. [6] Huang N E, Shen Z, Long S R, Wu M C, Shih H H, Zheng Q, Liu, H H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995.‌ [DOI:10.1098/rspa.1998.0193]
7. [7] Liu J, Wang X, Yuan S, Li G. On Hilbert-Huang transform approach for structural health monitoring. Journal of Intelligent Material Systems and Structures, 2006, 17(8-9):721-728.‌ [DOI:10.1177/1045389X06055766]
8. [8] Li H, Deng X, Dai H. Structural damage detection using the combination method of EMD and wavelet analysis. Mechanical Systems and Signal Processing, 2007, 21(1):298-306.‌ [DOI:10.1016/j.ymssp.2006.05.001]
9. [9] Dong Y, Li Y, Lai M. Structural damage detection using empirical-mode decomposition and vector autoregressive moving average model. Soil Dynamics and Earthquake Engineering, 2010, 30(3): 133-145.‌ [DOI:10.1016/j.soildyn.2009.10.002]
10. [10] Liu T Y, Chiang W L, Chen C W, Hsu W K, Lu L C, Chu T J. Identification and monitoring of bridge health from ambient vibration data. Journal of Vibration and Control, 2011, 17(4): 589-603.‌ [DOI:10.1177/1077546309360049]
11. [11] Roveri N, Carcaterra A. Damage detection in structures under traveling loads by Hilbert-Huang transform, Mechanical Systems and Signal Processing, 2012, 28:128-144.‌ [DOI:10.1016/j.ymssp.2011.06.018]
12. [12] Kunwar A, Jha R, Whelan M, Janoyan K. Damage detection in an experimental bridge model using Hilbert-Huang transform of transient vibrations. Structural Control and Health Monitoring, 2013, 20(1):1-15.‌ [DOI:10.1002/stc.466]
13. [13] Hsu W K, Chiou D J, Chen C W, Liu M Y, Chiang W L, Huang P C. A case study of damage detection in four-bays steel structures using the HHT approach. Smart Structures and Systems, 2014, 14(4):595-615 [DOI:10.12989/sss.2014.14.4.595]
14. [14] Ramezani S, Bahar O. EMD-based output-only identification of mode shapes of linear structures. Smart Structures and Systems, 2015, 16(5):919-35. [DOI:10.12989/sss.2015.16.5.919]
15. [15] Wang L, Chan T H. Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring. QUT Conference Proceedings, 2009 ‌
16. [16] Chen B, Zhao S L, Li P Y. Application of Hilbert-Huang transform in structural health monitoring: a state-of-the-art review. Mathematical Problems in Engineering, 2014. [DOI:10.1155/2014/317954]
17. [17] Goyal D, Pabla B S. The vibration monitoring methods and signal processing techniques for structural health monitoring: A review. Archives of Computational Methods in Engineering, 2016, 23(4), 585-594. [DOI:10.1007/s11831-015-9145-0]
18. [18] Smith J S. The local mean decomposition and its application to EEG perception data. Journal of the Royal Society Interface, 2005, 2(5): 443-454 [DOI:10.1098/rsif.2005.0058]
19. [19] Feldman M. Time-varying vibration decomposition and analysis based on the Hilbert transform. Journal of Sound and Vibration, 2006, 295(3): 518-530 [DOI:10.1016/j.jsv.2005.12.058]
20. [20] Stepien, P. Sliding window empirical mode decomposition-its performance and quality. EPJ Nonlinear Biomedical Physics, 2014, 2(1): 14. [DOI:10.1140/epjnbp/s40366-014-0014-9]
21. [21] Cheng J S, Zhang K, Yang Y, Yu D J. Comparison between the methods of local mean decomposition and empirical mode decomposition. Journal of vibration and shock, 2009, 28(5): 13-16.
22. [22] Huang Y, Yan C J, Xu Q. On the difference between empirical mode decomposition and Hilbert vibration decomposition for earthquake motion records. 15th World Conference on Earthquake Engineering, Lisboa, 2012.
23. [23] Henrici P. Applied and Computational Complex Analysis, Vol. 1: Power Series, Integration, Conformal Mapping, Location of Zeros. New York: Wiley, 1988.
24. [24] Li S, Li H, Liu Y, Lan C, Zhou W, Ou J. SMC structural health monitoring benchmark problem using monitored data from an actual cable‐stayed bridge. Structural Control and Health Monitoring, 2014, 21(2), 156-172.‌ [DOI:10.1002/stc.1559]
25. [25] Kaloop M. Structural health monitoring through dynamic and geometric characteristics of bridges extracted from GPS measurements. Doctoral dissertation. HIT. Harbin. China, 2010
26. [26] Asmussen, J C. Modal analysis based on the random decrement technique: application to civil engineering structures. Doctoral dissertation, unknown, 1997.
27. [27] Rilling G, Flandrin P. Sampling effects on the empirical mode decomposition. Advances in Adaptive Data Analysis, 2009, 1(01): 43-59.‌ [DOI:10.1142/S1793536909000023]
28. [28] Wenzel H, Pichler D. Ambient vibration monitoring. John Wiley & Sons, 2005 [DOI:10.1002/0470024577]
29. [29] Li H, Li S, Ou J, Li H. Modal identification of bridges under varying environmental conditions: temperature and wind effects. Structural Control and Health Monitoring, 2010, 17(5): 495-512.‌ [DOI:10.1002/stc.319]
30. [30] Broch J T. Mechanical Vibration and Shock Measurements. Bruel & Kjaer: Naerum, Denmark, 2000.
31. [31] Li J, Dackermann U, Xu Y L, Samali B. Damage identification in civil engineering structures utilizing PCA‐compressed residual frequency response functions and neural network ensembles. Structural Control and Health Monitoring, 2011, 18(2):207-226.‌ [DOI:10.1002/stc.369]
32. [32] Skiadas C H. Recent advances in stochastic modeling and data analysis. World Scientific Publishing Company; 2007.
33. [33] Hamdia K M, Silani M, Zhuang X, He P, Rabczuk T. Stochastic analysis of the fracture toughness of polymeric nanoparticle composites using polynomial chaos expansions. International Journal of Fracture, 2017, 206(2): 215-227. [DOI:10.1007/s10704-017-0210-6]
34. [34] Hamdia K M, Lahmer T, Nguyen-Thoi T, Rabczuk T. Predicting the fracture toughness of PNCs: A stochastic approach based on ANN and ANFIS. Computational Materials Science, 2015, 102: 304-313. [DOI:10.1016/j.commatsci.2015.02.045]
35. [35] Sprinthall RC, Fisk ST. Basic statistical analysis. Englewood Cliffs, NJ: Prentice Hall; 1990.
36. [36] Hamdia K M, Zhuang, X, He P, Rabczuk T. Fracture toughness of polymeric particle nanocomposites: evaluation of models performance using Bayesian method. Composites Science and Technology, 2016, 126: 122-129. [DOI:10.1016/j.compscitech.2016.02.012]
37. [37] Mitzenmacher M, Upfal E. Probability and computing: Randomization and probabilistic techniques in algorithms and data analysis. Cambridge university press, 2017.
38. [38] Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabczuk T. A software framework for probabilistic sensitivity analysis for computationally expensive models. Advances in Engineering Software, 2016, 100: 19-31. [DOI:10.1016/j.advengsoft.2016.06.005]
39. [39] Kirsch A. An introduction to the mathematical theory of inverse problems. Springer Science & Business Media, 2011. [DOI:10.1007/978-1-4419-8474-6]
40. [40] Vu-Bac N, Duong T X, Lahmer T, Zhuang X, Sauer R A, Park H S, Rabczuk T. (2018). A NURBS-based inverse analysis for reconstruction of nonlinear deformations of thin shell structures. Computer Methods in Applied Mechanics and Engineering, 331, 427-455. [DOI:10.1016/j.cma.2017.09.034]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author