Proposal for a Time-Dependent Dynamic Identification Algorithm for Structural Health Monitoring

Abstract

This paper describes the design, test and validation processes of a dynamicidentification algorithm aimed at the time-dependent assessment of modern structures and heritage buildings for civil and seismic engineering purposes. Full validation of the algorithm is performed through analysis of numerically simulated data from an idealized masonry tower. Making use of output-only vibration measurements, the non-parametric algorithm can generate dynamic features results as time-dependent functions for the complete observation period. The algorithm can work in the presence of different dynamic loads and non-linear structural behaviours, close spectral frequency components and noisecontaminated data. Time-dependent structural dynamic parameters that can be computed are modal frequencies, modal displacements, modal curvatures, and higher derivatives of mode shapes. The proposed algorithm aims to be used as the core estimator of timedependent identification methods devoted to the health monitoring of structures and infrastructures, being suitable for a multitude of tasks ranging from the simple operational modal analysis (in pre and post-event condition) to the complex online assessment of the structural response during seismic events for rapid damage identification.

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References

[1] D. V. Oliveira, L. F. F. Ramos, P. B. B. Lourenço, and J. Roque, “Structural Monitoring of the Monastery of Jerónimos,” in 250th Anniversary of the 1755 Lisbon Earthquake International Conference, 2005.

[2] M. J. Morais et al., “A proactive approach to the conservation of historic and cultural Heritage: The HeritageCare methodology,” in IABSE Symposium, 2019, pp. 64–71.

[3] M. G. Masciotta, M. J. Morais, L. F. Ramos, D. V. Oliveira, L. J. Sánchez-Aparicio, and D. González-Aguilera, “A Digital-based Integrated Methodology for the Preventive Conservation of Cultural Heritage: The Experience of HeritageCare Project,” Int. J. Archit. Herit., 2019.

[4] R. Brincker, “Some Elements of Operational Modal Analysis,” Shock Vib., vol. 2014, p.11, 2014.

[5] N. M. M. Maia, “Extraction of Valid Modal Properties from Measured Data in Structural Vibrations,” University of London, 1988.

[6] C. Rainieri and G. Fabbrocino, Operational Modal Analysis of Civil Engineering Structures: An Introduction and Guide for Applications. 2014.

[7] R. Pintelon, P. Guillaume, and J. Schoukens, “Uncertainty calculation in (operational) modal analysis,” Mech. Syst. Signal Process., vol. 21, no. 6, pp. 2359–2373, 2007.

[8] X. X. Bao, C. L. Li, and C. B. Xiong, “Noise elimination algorithm for modal analysis,” Appl. Phys. Lett., vol. 107, no. 4, Jul. 2015.

[9] S. K. Au, J. M. W. Brownjohn, and J. E. Mottershead, “Quantifying and managing uncertainty in operational modal analysis,” Mech. Syst. Signal Process., vol. 102, pp. 139–157, 2018.

[10] R. Brincker, L. Zhang, and P. Andersen, “Modal identification of output-only systems using frequency domain decomposition.,” Smart Mater. Struct., vol. 10, no. 3, pp. 441–445, 2001.

[11] R. Brincker, C. E. . Ventura, and P. Andersen, “Damping Estimation by Frequency Domain Decomposition,” in Proceedings of IMAC XIX, 2001, pp. 698–703.

[12] J. Rodrigues, R. Brincker, and P. Andersen, “Improvement of frequency domain outputonly modal identification from the application of the random decrement technique,” in Proceedings of IMAC XXII, 2004, pp. 92–100.

[13] R. Brincker and P. Andersen, “Understanding Stochastic Subspace Identification,” in Proceedings of IMAC XXIV, 2006, pp. 461–466.

[14] P. Van Overschee and B. De Moor, Subspace Identification For Linear Systems. Kluwer Academic Publishers, 1996.

[15] B. Peeters and G. De Roeck, “Reference-based stochastic subspace identification for output-only modal analysis,” Mech. Syst. Signal Process., vol. 13, no. 6, pp. 855–878, 1999.

[16] F. Ubertini, C. Gentile, and A. L. Materazzi, “Automated modal identification in operational conditions and its application to bridges,” Eng. Struct., vol. 46, pp. 264–278, 2013.

[17] M. G. Masciotta, “Damage Identification of Structures based on Spectral Output Signals,” University of Minho, 2015.

[18] M. G. Masciotta, L. F. Ramos, P. B. Lourenço, and M. Vasta, “Spectral algorithm for non-destructive damage localisation: Application to an ancient masonry arch model,” Mech. Syst. Signal Process., vol. 84, pp. 286–307, 2017.

[19] M. G. Masciotta, L. F. Ramos, P. B. Lourenço, M. Vasta, and G. De Roeck, “A spectrumdriven damage identification technique: Application and validation through the numerical simulation of the Z24 Bridge,” Mech. Syst. Signal Process., vol. 70–71, pp. 578–600, 2016.

[20] I. Daubechies, Ten Lectures of Wavelets. Springer-Verlag, 1992.

[21] Y. Wang, Z. He, and Y. Zi, “A demodulation method based on improved local mean decomposition and its application in rub-impact fault diagnosis,” Meas. Sci. Technol.,vol. 20, no. 2, p. 025704, Feb. 2009.

[22] I. Provaznik and J. Kozumplik, “lnformatics Wavelet transform in electrocardiographydata,” Int. J. Med. Inform., vol. 45, no. 1–2, pp. 111–128, 1997.

[23] A. Grossmann and J. Morlet, “Decomposition of hardy functions into square integrable Wavelets of constant shape.,” SIAM J. Math. Anal., vol. 15, no. 4, pp. 723–736, 1984.

[24] B. Adhikari et al., “Application of wavelet for seismic wave analysis in Kathmandu Valley after the 2015 Gorkha earthquake, Nepal,” Geoenvironmental Disasters, vol. 7, no. 1, 2020.

[25] H. Nyquist, “Certain Topics in Telegraph Transmission Theory,” Trans. Am. Inst. Electr. Eng., vol. 47, no. 2, pp. 617–644, 1928.

[26] C. E. Shannon, “Communication in the Presence of Noise,” Proc. IRE, vol. 37, no. 1, pp. 10–21, 1949.

[27] A. T. Walden and A. Contreras Cristan, “The phase-corrected undecimated discrete wavelet packet transform and its application to interpreting the timing of events,” Proc. R. Soc. London A Math. Phys. Eng. Sci., vol. 454, no. 1976, pp. 2243–2266, 1998.

[28] M. Feldman, Hilbert Transform Applications in Mechanical Vibration, First Edit. Chichester: John Wiley & Sons, 2011.

[29] A. W. Bowman and A. Azzalini, Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-PLUS Illustrations, vol. 18. UK, 1997.

[30] E. Ramírez, P. B. Lourenço, and M. D’Amato, “Seismic Assessment of the Matera Cathedral,” in 11th International Conference on Structural Analysis of Historical Constructions (SAHC), 2019, vol. 18, pp. 1346–1354.