Online nonlinear structural damage detection using Hilbert Huang transform and artificial neural networks

Document Type : Research Note

Authors

1 Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.

2 International Institute of Earthquake Engineering and Seismology, Tehran, Iran.

Abstract

Structural Health Monitoring (SHM) as a process in order to implement a damage detection strategy and assess the condition of structure plays a key role in structural reliability. In this paper, we aim to present a methodology for online detection of damages which may occur during a strong ground excitation. In this regard, Empirical Mode Decomposition (EMD) is superseded by Ensemble Empirical Mode Decomposition (EEMD) in the Hilbert Huang Transformation (HHT). Albeit analogous, EEMD brings about more appropriate Intrinsic Mode Functions (IMFs) than EMD. IMFs are employed to assess the first mode frequency and mode shape. Afterward, Artificial Neural Network (ANN) is applied to predict story acceleration based on previously measured values. Because ANN functions precisely, any congruency between predicted and measured acceleration indicates onset of damage. Then, another ANN method is applied to estimate the stiffness matrix. Though the first mode shape and frequency are calculated in advance, the process essentially requires an inverse problem to be solved in order to find stiffness matrix, which is done by ANN. This algorithm is implemented on moment-resisting steel frames, and the results show that the proposed methodology is reliable for online prediction of structural damage.

Keywords

Main Subjects

References

References:
1. Adeli, H. "Neural networks in civil engineering: 1989- 2000", Comput. Civ. Infrastruct. Eng., 16(2), pp. 126- 142 (2001).
2. Chang, P.C. and Liu, S.C. "Recent research in nondestructive evaluation of civil infrastructures", J. Mater. Civ. Eng., 15(3), pp. 298-304 (2003).
3. Fan, W. and Qiao, P. "Vibration-based damage identification methods: a review and comparative study", Struct. Heal. Monit., 10(1), pp. 83-111 (2011).
4. Lynch, J.P. and Loh, K.J. "A summary review of wireless sensors and sensor networks for structural health monitoring", Shock Vib. Dig., 38(2), pp. 91-130 (2006).
5. Doebling, S.W., Farrar, C.R., and Prime, M.B. "A summary review of vibration-based damage identification methods", Shock Vib. Dig., 30(2), pp. 91-105 (1998).
6. Yan, Y.J., Cheng, L.,Wu, Z.Y., and Yam, L.H. "Development in vibration-based structural damage detection technique", Mech. Syst. Signal Process., 21(5), pp. 2198-2211 (2007).
7. Montalvao, D., Maia, N.M.M., and Ribeiro, A.M.R. "A review of vibration-based structural health monitoring with special emphasis on composite materials", Shock Vib. Dig., 38(4), pp. 295-326 (2006).
8. Goyal, D. and Pabla, B.S. "The vibration monitoring methods and signal processing techniques for structural health monitoring: A review", Arch. Comput. Methods Eng., 23(4), pp. 585-594 (2016).https://link.springer.com/article/10.1007/s11831-015-9145-0.
9. Chang, P.C., Flatau, A., and Liu, S.C. "Review paper: Health monitoring of civil infrastructure", Struct. Heal. Monit., 2(3), pp. 257-267 (2003).
10. Qarib, H. and Adeli, H. "Recent advances in health monitoring of civil structures", Sci. Iran., 21(6), pp. 1733-1742 (2014).
11. Loh, C.-H., Wu, T.-C., and Huang, N.E. "Application of the empirical mode decomposition-Hilbert spectrum method to identify near-fault ground-motion characteristics and structural responses", Bull. Seismol. Soc. Am., 91(5), pp. 1339-1357 (2001).
12. Yang, J., Lei, Y., Lin, S., and Huang, N. "Hilbert- Huang based approach for structural damage detection", J. Eng. Mech., 130, pp. 85-95 (January 2004).
13. Zhang, R.R., Ma, S., Safak, E., and Hartzell, S. "Hilbert-Huang transform analysis of dynamic and earthquake motion recordings", J. Eng. Mech., 129(8), pp. 861-875 (2003).
14. Hou, Z., Noori, M., and Amand, R. St. "Wavelet-based approach for structural damage detection", J. Eng. Mech., 126(7), pp. 677-683 (2000).
15. Li, H., Zhang, Y., and Zheng, H. "Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings", J. Mech. Sci. Technol., 23(2), pp. 291-301 (2009).
16. Amini Tehrani, H., Bakhshi, A., and Akhavat, M. "An effective approach for structural damage localization in flexural members based on generalized S-transform", Sci. Iran. (In Press). http://scientiairanica.sharif.edu/article 20019.html.
17. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., and Liu, H.H. "The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis", Proc. R. Soc. London A Math. Phys. Eng. Sci., 454(1971), pp. 903-995 (1998).
18. Huang, N., Long, S., and Shen, Z. "The mechanism for frequency downshift in nonlinear wave evolution", Adv. Appl. Mech., 32, pp. 59-117 (1996).https://www.sciencedirect.com/science/article/pii/S0065215608700760.
19. Chen, B., Zhao, S., and Li, P. "Application of Hilbert-Huang transform in structural health monitoring: A state-of-the-art review", Math. Probl. Eng., 2014(7), pp. 1-22 (2014).
20. Huang, N.E., Shen, Z., and Long, S.R. "A new view of nonlinear water waves: The hilbert spectrum1", Annu. Rev. Fluid Mech., 31(1), pp. 417-457 (1999).
21. Vincent, H.T., Hu, S.L.J., and Hou, Z. "Damage detection using empirical mode decomposition method and a comparison with wavelet analysis", in 2nd International Workshop on Structural Health Monitoring, Stanford University, pp. 891-900 (1999).
22. Wu, Z. and Huang, N.E. "A study of the characteristics of white noise", R. Soc., 460, pp. 1597-1611 (2004).
23. Wu, Z. and Huang, N.E., Ensemble Empirical Mode Decomposition, World Sci. Publ., 1(1), pp. 1-41 (2009).
24. Aied, H., Gonzalez, A., and Cantero, D. "Identification of sudden stiffness changes in the acceleration response of a bridge to moving loads using ensemble empirical mode decomposition", Mech. Syst. Signal Process., 66- 67, pp. 314-338 (2016).
25. Moser, P. and Moaveni, B. "Environmental effects on the identified natural frequencies of the Dowling Hall Footbridge", Mech. Syst. Signal Process., 25(7), pp. 2336-2357 (2011).
26. Nagarajaiah, S. and Basu, B. "Output only modal identification and structural damage detection using time frequency & wavelet techniques", Earthq. Eng. Eng. Vib., 8(4), pp. 583-605 (2009).
27. Bishop, C.M., Neural Networks for Pattern Recognition, Oxford University Press, Inc. (1995).
28. Suresh, S., Omkar, S.N., Ganguli, R., and Mani, V. "Identification of crack location and depth in a cantilever beam using a modular neural network approach", Smart Mater. Struct., 13(4), pp. 907-915 (2004).
29. Fang, X., Luo, H., and Tang, J. "Structural damage detection using neural network with learning rate improvement", Comput. Struct., 83(25-26), pp. 2150- 2161 (2005).
30. Xu, B., Wu, Z., Chen, G., and Yokoyama, K. "Direct identification of structural parameters from dynamic responses with neural networks", Eng. Appl. Artif. Intell., 17, pp. 931-943 (2004).
31. Saadat, S., Buckner, G.D., Furukawa, T., and Noori, M.N. "An intelligent parameter varying (IPV) approach for non-linear system identification of base excited structures", Int. J. Non. Linear. Mech., 39(6), pp. 993-1004 (2004).
32. Bandara, R.P., Chan, T.H.T., and Thambiratnam, D.P. "Structural damage detection method using frequency response functions", Struct. Heal. Monit., 13(4), pp. 418-429 (Feb. 2014).
33. Rafiei, M.H. and Adeli, H. "A novel unsupervised deep learning model for global and local health condition assessment of structures", Eng. Struct., 156, pp. 598- 607 (2018).
34. Entezami, A., Shariatmadar, H., and Karamodin, A. "An improvement on feature extraction via time series modeling for structural health monitoring based on unsupervised learning methods", Sci. Iran. (In Press).http://scientiairanica.sharif.edu/article 20641 0.html.
35. Amezquita-Sanchez, J.P. and Adeli, H. "Feature extraction and classification techniques for health monitoring of structures", Sci. Iran. Trans. A, Civ. Eng., 22(6), p. 1931 (2015).
36. Huang, N.E. and Shen, S.S., Hilbert-Huang Transform and Its Applications, (World Scientific, 5 (2005).
37. Huang, N.E., Computing Instantaneous Frequency by Normalizing Hilbert Transform, Patent 6901353, U.S. Patent and Trademark Off., Washington, D.C. (2005).
38. Bahar, O. and Ramezani, S. "Enhanced Hilbert-Huang transform and its application to modal identification", Struct. Des. Tall Spec. Build., 23(4), pp. 239-253 (2014).
39. Wang, T., Zhang, M., Yu, Q., and Zhang, H. "Comparing the applications of EMD and EEMD on timefrequency analysis of seismic signal", J. Appl. Geophys., 83, pp. 29-34 (2012).
40. Jiang, S., Wu, S., and Dong, L. "A time-domain structural damage detection method based on improved multiparticle swarm coevolution optimization algorithm", Math. Probl. Eng., 2014, p. 11 (2014).
41. Friswell, M.I., Penny, J.E.T., and Garvey, S.D. "A combined genetic and eigensensitivity algorithm for the location of damage in structures", Comput. Struct., 69(5), pp. 547-556 (1998).
Scientia Iranica
Volume 26, Issue 3
Transactions on Civil Engineering (A)
May and June 2019
Pages 1266-1279

Files

History

  • Receive Date: 03 April 2018
  • Revise Date: 12 December 2018
  • Accept Date: 03 March 2019

Share

How to cite

Statistics