Damage detection of structures using signal processing and artificial neural networks

Document Type : Research Paper

Authors

Abstract

Over the last two decades, extensive research has been conducted on structural health monitoring and damage detection in order to reduce the life-cycle cost of structures and improve their reliability and safety. These methods are divided into modal-based and signal-based approaches. Recent advances in the field of sensor technologies have facilitated the use of signal-based methods as practical solution to detect damages in structures. After a sever earthquake, usually there is no possibility for visiting the individual structures. Therefore, application of methods that can detect damage of structures only by using signals recorded at the time of the earthquake is noteworthy. Many existing methods, especially methods based on signal processing are not able to determine the damage severity. This article presents a signal-based seismic structural health monitoring technique for damage detection and evaluating damage severity of a multi-story frame subjected to an earthquake event. As a case study, this article is focused on IASC–ASCE benchmark problem to provide possibility for side-by-side comparison. First three signal processing techniques including EMD, HVD and LMD, which are categorized as instantaneous time-frequency methods, have been compared to find a method with the best resolution in extracting frequency responses. Based on the results EMD has proved to outperform than the others. Second, EMD is used to extract the acceleration response of the sensors. Results show that by taking advantage of signal processing and artificial intelligence techniques in this research, damage detection of structures was carried out for three levels including damage occurrence, damage severity and location of the damage.

Keywords

References

[1] A. Rytter, “Vibrational based inspection of civil engineering structures”, unknown, 1993.
[2] Y. Xu, S. Chen, and R. Zhang, “Modal identification of Di Wang Building under typhoon York using the Hilbert–Huang transform method”, The Structural Design of Tall and Special Buildings, Vol. 12, No. 1, 2003, pp. 21-47.
[3] J.N. Yang, et al., “Hilbert-Huang based approach for structural damage detection”, Journal of engineering mechanics, Vol. 130, No. 1, 2004, pp. 85-95.
[4] Y. Xu, and J. Chen, “Structural damage detection using empirical mode decomposition: experimental investigation”, Journal of engineering mechanics, Vol. 130, No. 11, 2004, pp. 1279-1288.
[5] J. Liu, et al., “On Hilbert-Huang transform approach for structural health monitoring”, Journal of intelligent material systems and structures, Vol. 17, No. 8-9, 2006, pp. 721-728.
[6] H. Chen, Y. Yan, and J. Jiang, “Vibration-based damage detection in composite wingbox structures by HHT”, Mechanical systems and signal processing, Vol. 21, No. 1, 2007, pp. 307-321.
[7] H. Li, X. Deng, and H. Dai, “Structural damage detection using the combination method of EMD and wavelet analysis”, Mechanical Systems and Signal Processing, Vol. 21, No. 1, 2007, pp. 298-306.
[8] N. Roveri, & A. Carcaterra, “Damage detection in structures under traveling loads by Hilbert–Huang transform”, Mechanical Systems and Signal Processing, Vol. 28, 2012, pp. 128-144.
[9] A. Kunwar, R. Jha, M. Whelan, & K. Janoyan, “Damage detection in an experimental bridge model using Hilbert–Huang transform of transient vibrations” Structural Control and Health Monitoring, Vol. 20, No. 1, 2013, pp. 1-15.
[10] Y. Huang, C. J. Yan, & Q. Xu, “On the difference between empirical mode decomposition and Hilbert vibration decomposition for earthquake motion records” In 15th World Conference on Earthquake Engineering, 2012.
[11] L. Wang, T. H. Chan, “Review of vibration-based damage detection and condition assessment of bridge structures using structural health monitoring” QUT Conference Proceedings, 2009.
[12] B. Chen, S. L. Zhao, & P. Y. Li, “Application of Hilbert-Huang transform in structural health monitoring: a state-of-the-art review” Mathematical Problems in Engineering, 2014.
[ 13 [ ص. امید، "آشنایی مقدماتی با ویولت"، دانشگاه صنعتی شریف، 1387 .
[14] G. Yan, L.L. Zhou, and F. Yuan, “Wavelet-based built-in damage detection and identification for composites. in Smart Structures and Materials”, International Society for Optics and Photonics, 2005.
[15] E. Johnson, et al., “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data”, Journal of Engineering Mechanics, Vol. 130, No. 1, 2004, pp. 3-15.
[16] N.E. Huang, et al., “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, The Royal Society, 1998.
[17] J.S. Smith, “The local mean decomposition and its application to EEG perception data”, Journal of the Royal Society Interface, Vol. 2, No. 5, 2005, pp. 443-454.
[18] M. Feldman, “Time-varying vibration decomposition and analysis based on the Hilbert transform”, Journal of Sound and Vibration, Vol. 295, No. 3, 2006, pp. 518-530.
[19] J. Chen, and G. Zhao, “Numerical and Experimental Investigation on Parameter Identification of Time-Varying Dynamical System Using Hilbert Transform and Empirical Mode Decomposition”, Mathematical Problems in Engineering, 2014.
Journal of Modeling in Engineering
Volume 16, Issue 52
March 2018
Pages 253-265

Files

History

  • Receive Date: 08 November 2015
  • Revise Date: 08 October 2016
  • Accept Date: 23 November 2016

Share

How to cite

Statistics