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Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis

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Abstract

This paper describes an application of discrete wavelet analysis for damage detection of a framed structure subjected to strong earthquake excitation. The response simulation data on each floor were obtained by non-linear dynamic analysis. Damage to the frame was introduced due to the non-linear behaviour of the columns and beams. In order for the structural members to reach the yield point or go slightly beyond yielding, the earthquake excitation was scaled up with the appropriate factor. Since the dynamic behaviour of an inelastic structure during an earthquake is a non-stationary process, discrete wavelet analysis was performed in order to analyze the simulation response data for each floor. It was shown that structural damage on a floor, and the time when this occurred can be clearly detected by spikes in the wavelet details of the response, acquired from the corresponding floor. Damage can be detected by observing the spikes directly from the wavelet details or following a statistical procedure. An automatic numerical procedure that can clearly distinguish between the spikes associated with damage and the spikes due to non-stationary excitation is proposed. The effects of noise were taken into account by adding a white Gaussian noise to the simulation response data. Damage to the element can also be detected again from the noised signal, if the level of details and the order of wavelets in the wavelet analysis of the response signal are increased. Numerical results show the effectiveness of the discrete wavelet approach to damage detection of framed structures.

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References

  • Alonso R, Noori M, Saadat S, Masuda A, Hou Z (2004) Effects of excitation frequency on detection accuracy of orthogonal wavelet decomposition for structural health monitoring. Earthq Eng Eng Vib 3(1):101–106

    Article  Google Scholar 

  • Bukkapatnam S, Nichols J, Seaver M, Trickey S, Hunter M (2005) A wavelet-based, distortion energy approach to structural health monitoring. Struct Health Monit 4:247–258

    Article  Google Scholar 

  • Casciati F, Casciati S (2006) Structural health monitoring by Lyapunov exponents of non-linear time series. Struct Control Health Monit 13(1):132–146

    Article  Google Scholar 

  • Chandrashekhar M, Ranjan G (2009) Structural damage detection using modal curvature and fuzzy logic. Struct Health Monit 8(2):267–282

    Article  Google Scholar 

  • Chatzi EN, Smyth AW, Masri SF (2010) Experimental application of on-line parametric identification for non-linear hysteretic systems with model uncertainty. J Struct Safety 32(5):326–337

    Article  Google Scholar 

  • Ciambella J, Vestroni F, Vidoli S (2011) Damage observability, localization and assessment based on eigenfrequencies and eigenvectors curvatures. Smart Struct Syst 8(2):191–204

    Article  Google Scholar 

  • Dertimanis V, Chatzi E, (2014), A hybrid evolution strategy approach to the structural identification problem via state–space models, Conferences Proceedings, Sixth World Conference on Structural Control and Monitoring (6WCSCM), 2468–2477, ISBN 978-84-942844-5-8, Barcelona, Spain

  • Fan W, Qiao P (2011) Vibration-based damage identification methods: a reviewand comparative study. Struct Health Monit 10(1):83–111

    Article  Google Scholar 

  • Friswell MI, Mottershead JE (1995) Finite element model updating in structural dynamics. Kluwer Academic Publishers Group, New York

    Book  Google Scholar 

  • Goggins J, Broderick B, Basu B, Elghazouli A (2007) Investigation of the seismic response of braced frames using wavelet analysis. J Struct Control Health Monit 14(4):627–648

    Article  Google Scholar 

  • Guido De Roeck, Reynders E. (2009), Exploring the limits and extending the borders of structural health monitoring, Proceedings Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, COMPDYN, ECCOMAS, Papadrakakis M, Lagaros ND, Fragiadakis M (eds.) Rhodes, Greece

  • Hera A, Hou Z (2004) Application of wavelet approach for asce structural health monitoring benchmark studies. J Eng Mech ASCE 130(1):96–104

    Article  Google Scholar 

  • Hou Z, Noori M, Arnand ST (2000) Wavelet-based approach for structural damage detection. J Eng Mech 126(7):677–683

    Article  Google Scholar 

  • Humar J, Bagchi A, Xu HP (2006) Performance of vibration-based techniques for the identification of structural damage. Struct Health Monit 5(3):215–241

    Article  Google Scholar 

  • Khatam H, Golafshani AA, Beheshti-Aval SB, Noori M (2007) Harmonic class loading for damage identification in beams using wavelet analysis. Struct Health Monit 6(1):67–80

    Article  Google Scholar 

  • Kim H, Melhem H (2004) Damage detection of structures by wavelet analysis. Eng struct 26:347–362

    Article  Google Scholar 

  • Kirmser PG. (1944), The effect of discontinuities of the natural frequency of beams. In: Proceedings American Society for Testing Materials, Philadelphia

  • Lima MM, Amiri GG, Bagheri A (2012) Wavelet-based method for damage detection of non-linear structures. J Civil Eng Urban 2(4):149–153

    Google Scholar 

  • Masri SF, Nakamura M, Chassiakos AG, Caughey TK (1996) Neural network approach to detection of changes in structural parameters. J Eng Mech 122(4):350–360

    Article  Google Scholar 

  • Nagarajaiah S, Basu B (2009) Output only modal identification and structural damage detection using time frequency and wavelet techniques. Earthq Eng Eng Vib 8(4):583–605

    Article  Google Scholar 

  • Newland DE (1993) An introduction to random vibrations, spectral and wavelet analysis. Longman Singapore publishers Pte Ltd, Singapore

    Google Scholar 

  • Noh HY, Nair K, Lignos DG, Kiremidjian AS (2011) Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data. J Struct Eng 137(10):1215–1228

    Article  Google Scholar 

  • Noh HY, Lignos DG, Nair K, Kiremidjian AS (2012) Development of fragility functions as a damage classification method for steel moment-resisting frames using a wavelet-based damage sensitive feature. Earthq Eng Struct Dyn 41(4):681–696

    Article  Google Scholar 

  • Papadimitriou C, Ntotsios E. (2009), Structural model updating using vibration measurements. In: Proceedings Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, ECCOMAS, COMPDYN, M. Papadrakakis, N.D. Lagaros, M. Fragiadakis (eds.), Rhodes, Greece

  • Rucka M, Wilde K (2010) Neuro-wavelet damage detection technique in beam, plate and shell structures with experimental validation. J Theor Appl Mech 48(3):579–604

    Google Scholar 

  • Sakellariou J, Fassois S (2006) Stochastic output error vibration-based damage detection and assessment in structures under earthquake excitation. J Sound Vib 127(3):1048–1067

    Article  Google Scholar 

  • Soyoz S, Feng M (2007) Instantaneous damage detection of bridge structures and experimental verification. Struct Control Health Monitor 15(7):958–973

    Article  Google Scholar 

  • Staszewski WJ (1998) Structural and mechanical damage detection using wavelets. Shock Vib Digest 30(6):457–472

    Article  Google Scholar 

  • Taha R, Noureldin M, Lucero A, Baca T (2006) Wavelet transform for structural health monitoring: a compendium of uses and features. Struct Health Monitor 5(3):267–295

    Article  Google Scholar 

  • Vanik, MW, Beck JL (1997) A Bayesian probabilistic approach to structural health monitoring. In: Proceedings, International Workshop on Structural Health Monitoring: Current Status and Perspectives, Stanford University, Stanford

  • Wen YK (1976) Method for random vibration of hysteretic systems. J Eng Mech 102:246–263

    Google Scholar 

  • Wu X, Ghabossi J, Garrett JH (1992) Use of neural networks in detection of structural damage. Comput Struct 42(4):649–659

    Article  Google Scholar 

  • Yun GJ, Kenneth AO, Dyke JS, Song W (2009) A two-stage damage detection approach based on subset selection and genetic algorithms. Smart Struct Syst 5(1):1–21

    Article  Google Scholar 

Download references

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Correspondence to Nikos G. Pnevmatikos.

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Pnevmatikos, N.G., Hatzigeorgiou, G.D. Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis. Bull Earthquake Eng 15, 227–248 (2017). https://doi.org/10.1007/s10518-016-9962-z

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  • DOI: https://doi.org/10.1007/s10518-016-9962-z

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