Abstract
Frequent large-scale earthquakes, climate changes, manmade hazards, and the duration of the service are the possible origins of structural damage in Taiwan. To detect the changing features and damage states of the structures, the demand for understanding the unknown system models of the operating structures has risen. The accuracy of structural health monitoring has become a significant issue. Therefore, four kinds of refined data-driven stochastic subspace system identification (SSI-DATA) methods, namely the mode-by-mode methods, are proposed in this research. Because the mode-by-mode methods only extract a single mode per iteration, the “mode elimination” and “signal reconstruction” steps are added to the traditional SSI-DATA. The mode elimination is realized by removing the singular components that have been exploited in the identified mode. Meanwhile, the signal reconstruction employs a similar approach used in the singular spectrum analysis after the Hankel matrix is regenerated with the removal of identified modes. Moreover, the effective projection operations and modification of the singular value decomposition process are employed in the refined methods. A unified analysis procedure is also introduced to automatically extract all the concerned modes one by one using the methods, while the errors between the reference frequencies and identified frequencies and the calculated frequency resolutions are the criteria for selecting modes. To verify the proposed methods, cases of a simulated eight-story frame and the actual operating bridge structure are studied using the proposed system identification methods. Consequently, the identification results show that the refined methods can yield slightly more accurate modal parameters of the structures. Moreover, the computational time of the second, third, and fourth methods is much less than the traditional SSI-DATA.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Van Overschee, P., De Moor, B.: Subspace algorithm for the stochastic identification problem. In: 30th IEEE Conference on Decision and Control, pp.1321–1326 (1991)
Peeters, B., De Roeck, G.: Reference-based stochastic subspace identification for output-only modal analysis. Mech. Syst. Signal Process. 13(6), 855–878 (1999)
Liu, Y.C., Loh, C.H., Ni, Y.Q.: Stochastic subspace identification for output-only modal analysis: application to super high-rise tower under abnormal loading condition. Earthquake Eng. Struct. Dynam. 42(4), 477–498 (2013)
Shimpi, V., Sivasubramanian, M.V., Singh, S.B.: System identification of heritage structures through AVT and OMA: a review. Struct. Durabl. Health Monit. 13(1), 1–40 (2019)
Gentile, C., Gallino, N.: Condition assessment and dynamic system identification of a historic suspension footbridge. Struct. Control. Health Monit. 15(3), 369–388 (2008)
Zonno, G., Aguilar, R., Boroschek, R., Lourenço, P.B.: Automated long-term dynamic monitoring using hierarchical clustering and adaptive modal tracking: validation and applications. J. Civ. Struct. Heal. Monit. 8(5), 791–808 (2018). https://doi.org/10.1007/s13349-018-0306-3
Ercan, E.: Assessing the impact of retrofitting on structural safety in historical buildings via ambient vibration tests. Constr. Build. Mater. 164, 337–349 (2018)
Elyamani, A., Roca Fabregat, P.: A review on the study of historical structures using integrated investigation activities for seismic safety assessment Part I: dynamic investigation. Sci. Cult. 4(1), 1–27 (2018)
Peeters, B., De Roeck, G.: Stochastic system identification for operational modal analysis: a review. J. Dyn. Syst. Meas. Contr. 123(4), 659–667 (2001)
Brühwiler, E., Bastien Masse, M.: Strengthening the Chillon viaducts deck slabs with reinforced UHPFRC. In: IABSE Conference Geneva 2015 - Structural Engineering: Providing Solutions to Global Challenges, pp. 1171–1178, IABSE (2015)
Martín-Sanz, H., Tatsis, K., Dertimanis, V.K., Avendaño-Valencia, L.D., Brühwiler, E., Chatzi, E.: Monitoring of the UHPFRC strengthened Chillon viaduct under environmental and operational variability. Struct. Infrastruct. Eng. 16(1), 138–168 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chang, CM., Chuang, YJ. (2024). Development of Refined Data-Driven Stochastic Subspace System Identification for Buildings and Bridges. In: Endo, Y., Hanazato, T. (eds) Structural Analysis of Historical Constructions. SAHC 2023. RILEM Bookseries, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-031-39450-8_90
Download citation
DOI: https://doi.org/10.1007/978-3-031-39450-8_90
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-39449-2
Online ISBN: 978-3-031-39450-8
eBook Packages: EngineeringEngineering (R0)