Structural Damage Detection and Localization with Unknown Postdamage Feature Distribution Using Sequential Change-Point Detection Method
Publication: Journal of Aerospace Engineering
Volume 32, Issue 2
Abstract
The high structural deficient rate poses serious risks to the operation of many bridges and buildings. To prevent critical damage and structural collapse, a quick structural health diagnosis tool is needed during normal operation or immediately after extreme events. In structural health monitoring (SHM), many existing methods will have limited usefulness in the quick damage identification process because (1) the damage event needs to be identified quickly, and (2) postdamage information is usually unavailable. To address these drawbacks, we propose a new damage detection and localization approach based on stochastic time series analysis. Specifically, damage sensitive features, which are extracted from vibration signals, follow different distributions before and after a damage event. Hence, we use optimal change-point detection theory to find the time of damage occurrence. Because existing change-point detectors require the postdamage feature distribution, which is unavailable in SHM, we propose a maximum likelihood method for learning the distribution parameters from the time-series data. The proposed damage detection using estimated parameters achieves optimal performance. Also, we utilize the detection results to find damage location without any further computation. Validation results show highly accurate damage identification in American Society of Civil Engineers benchmark structures and two shake table experiments.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
We would like to thank Shieh-Kung Huang from National Taiwan University (NTU) and the personnel of NCREE for their help and collaboration. This research is partially supported by National Science Foundation-Network for Earthquake Engineering Simulation Research (NSF-NEESR) Grant No. 1207911, and their support is gratefully acknowledged. The first author would like to thank the Charles H. Leavell Graduate Student Fellowship for its financial support.
References
ASCE. 2017. “2017 report card for America’s infrastructure.” Accessed September 20, 2017. https://www.infrastructurereportcard.org/.
Bornn, L., C. R. Farrar, D. Higdon, and K. P. Murphy. 2016. “Modeling and diagnosis of structural systems through sparse dynamic graphical models.” Mech. Syst. Signal Process. 74: 133–143. https://doi.org/10.1016/j.ymssp.2015.11.005.
Cover, T. M., and J. A. Thomas. 2012. Elements of information theory. New York: Wiley.
Farrar, C. R., T. A. Duffey, S. W. Doebling, and D. A. Nix. 1999. “A statistical pattern recognition paradigm for vibration-based structural health monitoring.” Struct. Health Monitor. 2000: 764–773.
Farrar, C. R., and H. Sohn. 2000. “Pattern recognition for structural health monitoring.” In Proc., Workshop on Mitigation of Earthquake Disaster by Advanced Technologies. Las Vegas.
Hastie, T., R. Tibshirani, J. Friedman, T. Hastie, J. Friedman, and R. Tibshirani. 2009. The elements of statistical learning. New York: Springer.
Huang, Y., J. L. Beck, and H. Li. 2017. “Hierarchical sparse Bayesian learning for structural damage detection: Theory, computation and application.” Struct. Saf. 64: 37–53. https://doi.org/10.1016/j.strusafe.2016.09.001.
Hui, Y., S. S. Law, and C. J. Ku. 2017. “Structural damage detection based on covariance of covariance matrix with general white noise excitation.” J. Sound Vibr. 389: 168–182. https://doi.org/10.1016/j.jsv.2016.11.014.
Johnson, E., H. Lam, L. Katafygiotis, and J. Beck. 2004. “Phase I IASC-ASCE structural health monitoring benchmark problem using simulated data.” J. Eng. Mech. 130 (1): 3–15. https://doi.org/10.1061/(ASCE)0733-9399(2004)130:1(3).
Lanata, F., and A. Del Grosso. 2006. “Damage detection and localization for continuous static monitoring of structures using a proper orthogonal decomposition of signals.” Smart Mater. Struct. 15 (6): 1811–1829. https://doi.org/10.1088/0964-1726/15/6/036.
Lei, Y., A. Kiremidjian, K. Nair, J. Lynch, K. Law, T. Kenny, E. Carryer, and A. Kottapalli. 2003. “Statistical damage detection using time series analysis on a structural health monitoring benchmark problem.” In Proc., 9th Int. Conf. on Applications of Statistics and Probability in Civil Engineering, 6–9.
Liao, Y., K. Balafas, A. Kiremidjian, R. Rajagopal, and C.-H. Loh. 2015. “Application of acceleration-based damage detection algorithms to experimental data from multi-story steel structures.” In Proc., Int. Workshop on Structural Health Monitoring. Stanford, CA: Stanford Univ.
Liao, Y., A. S. Kiremidjian, R. Rajagopal, and C.-H. Loh. 2016. “Angular velocity-based structural damage detection.” In Proc., Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems Conf. Las Vegas: International Society for Optics and Photonics.
Liao, Y., M. Mollineaux, R. Hsu, R. Bartlett, A. Singla, A. Raja, R. Bajwa, and R. Rajagopal. 2014. “Snowfort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring.” Sens. J. IEEE 14 (12): 4253–4263. https://doi.org/10.1109/JSEN.2014.2358253.
Nair, K. K., and A. S. Kiremidjian. 2007. “Time series based structural damage detection algorithm using gaussian mixtures modeling.” J. Dyn. Syst. Measur. Control 129 (3): 285–293. https://doi.org/10.1115/1.2718241.
Nair, K. K., and A. S. Kiremidjian. 2009. “Derivation of a damage sensitive feature using the Haar wavelet transform.” J. Appl. Mech. 76 (6): 061015. https://doi.org/10.1115/1.3130821.
Nair, K. K., A. S. Kiremidjian, and K. H. Law. 2006. “Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure.” J. Sound Vibr. 291 (1): 349–368. https://doi.org/10.1016/j.jsv.2005.06.016.
Noh, H., R. Rajagopal, and A. Kiremidjian. 2013. “Sequential structural damage diagnosis algorithm using a change point detection method.” J. Sound Vibr. 332 (24): 6419–6433. https://doi.org/10.1016/j.jsv.2013.07.005.
Pollak, M., and A. G. Tartakovsky. 2009. “Optimality properties of the Shiryaev-Roberts procedure.” Statistica Sin. 19 (4): 1729–1739.
Qiao, L., A. Esmaeily, and H. G. Melhem. 2012. “Signal pattern recognition for damage diagnosis in structures.” Comput. -Aided Civil Infrastruct. Eng. 27 (9): 699–710. https://doi.org/10.1111/j.1467-8667.2012.00766.x.
Tartakovsky, A. G., and V. V. Veeravalli. 2005. “General asymptotic Bayesian theory of quickest change detection.” Theory Probab. Appl. 49 (3): 458–497. https://doi.org/10.1137/S0040585X97981202.
Tartakovsky, A. G., and V. V. Veeravalli. 2008. “Asymptotically optimal quickest change detection in distributed sensor systems.” Sequential Anal. 27 (4): 441–475. https://doi.org/10.1080/07474940802446236.
Tian, S., Z. Yang, Z. He, and X. Chen. 2014. “Damage identification by the Kullback-Leibler divergence and hybrid damage index.” Shock Vibr. 2014: 1–22. https://doi.org/10.1155/2014/962056.
Yeung, W., and J. Smith. 2005. “Damage detection in bridges using neural networks for pattern recognition of vibration signatures.” Eng. Struct. 27 (5): 685–698. https://doi.org/10.1016/j.engstruct.2004.12.006.
Information & Authors
Information
Published In
Journal of Aerospace Engineering
Volume 32 • Issue 2 • March 2019
Copyright
©2018 American Society of Civil Engineers.
History
Received: Oct 2, 2017
Accepted: Aug 7, 2018
Published online: Dec 3, 2018
Published in print: Mar 1, 2019
Discussion open until: May 3, 2019
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.