Seismic damage assessment of building with strong non-linearity based on particle filter
Takenori Hida, Ryoji Ishizakadownload PDF
Abstract. Various structural health monitoring (SHM) methods based on system identification using strong motion records of buildings were proposed in previous studies. Methodologies based on linear state-space models such as subspace state-space system identification (4SID)  were often used to assess the structural damage in past studies. However, it is inappropriate to apply methods based on linear state-space representations when evaluating damage to buildings that exhibit strong nonlinearity, such as wooden structures that were severely damaged due to an earthquake. To evaluate the seismic damage of a building, it is desirable to observe the strong motion on all floors of the building. However, such cases are rare in real buildings due to various restrictions such as cost and/or spatial limitation. Based on the background mentioned above, this paper investigates the applicability of the particle filter  to assess the structural integrity of highly nonlinear buildings using the strong motion records observed on a limited number of floors.
Structural Health Monitoring, Strong Motion Record, Data Assimilation, Particle Filter, Bouc-Wen Model
Published online 3/30/2023, 7 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Takenori Hida, Ryoji Ishizaka, Seismic damage assessment of building with strong non-linearity based on particle filter, Materials Research Proceedings, Vol. 27, pp 166-172, 2023
The article was published as article 21 of the book Structural Health Monitoring
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
 M. Verhaegen and P. Dewilde, Subspace model identification, Part 1: The output-error state-space model identification class of algorithms, International Journal of Control, Vol. 56, No. 5, 1992, pp. 1187-1210. https://doi.org/10.1080/00207179208934363
 P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems, Kluwer Academic Pub., Norwell, Massachusetts, 1996. https://doi.org/10.1007/978-1-4613-0465-4
 Kitagawa, G., Monte Carlo filter and smoother for non-Gaussian nonlinear state space models, Journal of Computational and Graphical Statistics, 5(1), 1996, pp. 1–25. https://doi.org/10.1080/10618600.1996.10474692
 Kitagawa, G., A self-organizing state space models, Journal of American Statistical Association, 93, 1998, pp. 1203-1205. https://doi.org/10.2307/2669862
 S. Nakano, G. Ueno, and T. Higuchi, Merging particle filter for sequential data assimilation, Nonlin. Processes Geophys., 14, 2007, pp. 395-408. https://doi.org/10.5194/npg-14-395-2007
 Bouc, R.: Forced vibration of mechanical systems with hysteresis. Proceedings of the Fourth Conference on Nonlinear Oscillation. Prague, Czechoslovakia, 1967, p. 315.
 Wen, Y. K., Method for random vibration of hysteretic systems. Journal of Engineering Mechanics. American Society of Civil Engineers. 102 (2), 1976, pp. 249–263. https://doi.org/10.1061/JMCEA3.0002106