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On the development of data-based damage diagnosis algorithms for structural health monitoring

  • Kiremidjian, Anne S. (Department of Civil and Environmental Engineering, Stanford University)
  • Received : 2022.06.12
  • Accepted : 2022.07.17
  • Published : 2022.09.25
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Abstract

In this paper we present an overview of damage diagnosis algorithms that have been developed over the past two decades using vibration signals obtained from structures. Then, the paper focuses primarily on algorithms that can be used following an extreme event such as a large earthquake to identify structural damage for responding in a timely manner. The algorithms presented in the paper use measurements obtained from accelerometers and gyroscope to identify the occurrence of damage and classify the damage. Example algorithms are presented include those based on autoregressive moving average (ARMA), wavelet energies from wavelet transform and rotation models. The algorithms are illustrated through application of data from test structures such as the ASCE Benchmark structure and laboratory tests of scaled bridge columns and steel frames. The paper concludes by identifying needs for research and development in order for such algorithms to become viable in practice.

Keywords

Acknowledgement

The research described in this paper was financially supported by the Natural Science Foundation, the John A Blume Earthquake Engineering Center, and the John Blume and James Gere Fellowships at Stanford University. The author gratefully acknowledges the contribution of the doctoral students involved in the research - Krishnan Nair, Pooya Sarabandi, Alan Cheung, Hae-Young Noh, Konstantinos Balafas, and Yezheng Liao. In addition, the help of colleagues who enabled us to participate in various laboratory tests or provided key data for testing the algorithms is greatly appreciated. They include Professor Emeritus Seiid Seiidi of the University of Nevada, Reno, the late Professor Steve Mahin of the University of California, Berkeley, and Professor Emeritus H.C. Loh of the National Taiwan University.

References

  1. Albishi, A. and Ramahi, O. (2017), "Microwaves-based high sensitivity sensors for crack detection in metallic materials", IEEE Transact. Microw. Theory Techniq., 65(5), 1864-1872. https://doi.org/10.1109/TMTT.2017.2673823
  2. Balafas, K. and Kiremidjian, A. (2015a), "Development and validation of a novel earthquake damage estimation scheme based on the continuous wavelet transform of input and output acceleration measurements", Earthq. Eng. Struct. Dyn., 44(4), 501-522. https://doi.org/10.1002/eqe.2529
  3. Balafas, K. and Kiremidjian, A. (2015b), "Reliability assessment of the rotation algorithm for earthquake damage estimation", Struct. Infrastr. Eng., 11(1), 51-62. https://doi.org/10.1080/15732479.2013.879318
  4. Cantero-Chinchilla, S., Beck, J., Chiachio, J., Chiachio, M., Chronopoulos, D. and Jones, A. (2020), "Optimal sensor and actuator placement for structural health monitoring via an efficient convex cost-benefit optimization", Mech. Syst. Signal Process., 144, 106901. https://doi.org/10.1016/j.ymssp.2020.106901
  5. Chakraborty, J., Katunin, A., Klikowicz, P. and Salamak, P. (2019), "Early crack detection of reinforced concrete structure using embedded sensors", Sensors, 19(18), 3879. https://doi.org/10.3390/s19183879
  6. Cheung, A. and Kiremidjian, A. (2014), "Development of a rotation algorithm for earthquake damage diagnosis", Earthq. Spectra, 30(4), 1381-1401. https://doi.org/10.1193/012212EQS016M
  7. FEMA P-58 (2018), "Seismic performance assessment of buildings methodology", Applied Technology Council; Redwood City, CA, USA. https://www.atcouncil.org/docman/fema/246-fema-p-58-1-seismic-performance-assessment-of-buildings-volume-1-methodology-second-edition/file
  8. Guo, H., Zhang, L., Zhang, L. and Zhou, J. (2004), "Optimal placement of sensors for structural health monitoring using improved genetic algorithms", Smart Mater. Struct., 13(3), 528-534. https://doi.org/10.1088/0964-1726/13/3/011
  9. Hong, J.C. and Kim, Y.Y. (2004), "The determination of the optimal Gabor wavelet shape for the best time-frequency localization using the entropy concept", Experim. Mech., 44, 387-395. https://doi.org/10.1007/BF02428092
  10. Johnson, E.A., Lam, H.F., Katafygiotis, L.S. and Beck, J.L. (2000), "A benchmark problem for structural health monitoring and damage detection", Proceedings of 14th Engineering Mechanics Conference, Austin, TX, USA, May. https://doi.org/10.1142/9789812811707_0028
  11. Kiremidjian, A.S., Sarabandi, P. and Kiremidjian, G. (2009), "A wireless structural monitoring system with embedded damage algorithms and decision support system", Struct. Infrastr. Eng., 7(12), 881-894. https://doi.org/10.1080/15732470903208773
  12. Lignos, D., Krawinkler, H. and Whittaker, A. (2011), "Prediction and validation of sidesway collapse of two scale models of a 4-story steel moment frame", Earthq. Eng. Struct. Dyn., 40(7), 807-825. https://doi.org/10.1002/eqe.1061
  13. Lynch, J.P., Sundararajan, A., Law, K.H., Kiremidjian, A.S. and Carryer, E. (2004), "Embedding damage detection algorithms in a wireless sensing unit for attainment of operational power efficiency", Smart Mater. Struct., 13(4), 800-810. https://doi.org/10.1088/0964-1726/13/4/018
  14. Liao, Y., Balafas, K., Kiremidjian, A., Rajagopal, R. and Loh, C. (2017), "Rapid damage detection from strong motion acceleration and rotation response measurements", Proceedings of the 16th World Conference on Earthquake Engineering, Santiago de Chile, Chile, January.
  15. Mallat, S. (1999), A Wavelet Tour of Signal Processing, Academic Press, San Diego, CA, USA.
  16. Nair, K.K. and Kiremidjian, A.S. (2007), "Time series based structural damage detection algorithm using Gaussian mixtures modeling", J. Dyn. Syst. Measure. Control, 129(3), 285-293. https://doi.org/10.1115/1.2718241
  17. Nair, K.K. and Kiremidjian, A.S. (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
  18. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time seriesbased damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291(2), 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  19. Noh, H.Y., Nair, K.K., Lignos, D.G. and Kiremidjian, A.S. (2011), "On the use of wavelet based damage sensitive features for structural damage diagnosis using strong motion data", J. Struct. Eng., 137(10), 1215-1228. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000385
  20. Noh, H., Lignos, D., Nair, K. and Kiremidjian, A. (2012), "Development of fragility functions as a damage classification/ prediction method for steel moment-resisting frames using a wavelet-based damage sensitive feature", Int. J. Earthq. Eng. Struct. Dyn., 41(4), 681-696. https://doi.org/10.1002/eqe.1151
  21. Roach, D. (2009), "Real time crack detection using mountable comparative vacuum monitoring sensors", Smart Struct. Syst., Int. J., 5(4), 317-328. https://doi.org/10.12989/sss.2009.5.4.317
  22. Snowfort (2017), Sensor Network: Open & Wireless for Data Analytics. https://web.stanford.edu/group/snowfort/
  23. Straser, E.G. (1998), "A modular, wireless damage monitoring system for structures", Report No. 128; John A Blume Earthquake Engineering Center, Department of Civil and Environmental Engineering, Stanford University: Stanford, CA, USA.