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Application of Stochastic Subspace Identification in Bridge Health Monitoring, and Study on Effects of Temperature Fluctuations on Frequency Changes

Ambient vibration tests and real-time health monitoring have become more accepted in civil engineering. Time domain and frequency domain algorithms have been used in structural identification using the output-only measurements. The purpose of this paper was to investigate the effectiveness of the data-driven subspace identification algorithms in modal analysis of bridges from output-only measurements. The stochastic subspace identification (SSI) algorithm was examined through a numerical truss bridge and a real concrete highway bridge. In the modal identifications, the simulated dynamic responses of the truss bridge with abrupt damages during the excitation and the actual acceleration measurements from a real-time health monitoring system were used as the output data for the numerical truss bridge and the real highway bridge respectively. Stabilization diagrams with a range of model orders were used to determine the modal frequencies, damping ratios, and mode shapes. As one of the environmental conditions, temperature fluctuations can have a great effect on the dynamic characteristics of bridges. It is useful to learn the pattern of changes in frequencies due to temperature fluctuations. The variation of frequencies with respect to temperature was investigated using one-year ambient vibration data of a highway bridge. The modal frequencies and temperatures were correlated, which showed that such correlations for most modes can be represented by single or bilinear lines.

[1] B. Peeters, C.E. Ventura, Comparative study of modal analysis techniques for bridge dynamic characteristics, Mechanical Systems and Signal Processing, 17 (2003) 965-988. [2] R. Brincker, L. Zhang, P. Andersen, Modal identification from ambient responses using frequency domain decomposition, in: Proceedings of IMAC 18, the International Modal Analysis Conference, San Antonio, TX, 2000, pp. 625-630. [3] P. Van Overschee, B. De Moor, Subspace Algorithms for the Stochastic Identification Problem, Automatica, 29 (1993) 649-660. [4] P. Van Overschee, B. De Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Kluwer Academic Publishers, 1996. [5] M. Viberg, B. Wahlberg, B. Ottersten, Analysis of state space system identification methods based on instrumental variables and subspace fitting, Automatica, 33 (1997) 1603-1616. [6] B. Peeters, G. De Roeck, Reference-Based Stochastic Subspace Identification for Output-Only Modal Analysis, Mechanical Systems and Signal Processing, 13 (1999) 855-878. [7] B. Peeters, G. Lowet, H.V.D. Auweraer, J. Leuridan, A new procedure for modal parameter estimation, Sound and Vibration, 38 (2004) 24-29. [8] B. Peeters, G.D. Roeck, One-year monitoring of the Z24-Bridge: environmental effects versus damage events, Earthquake Engineering & Structural Dynamics, 30 (2001) 149-171. [9] J. Chen, J. Li, Simultaneous identification of structural parameters and input time history from output-only measurements, Comput Mech, 33 (2004) 365-374. [10] M.W. Halling, T. Petty, Strong motion instrumentation of I-15 bridge C-846, in, UDOT Report No. UT-01.12, 2001. [11] T.M. Dye, Forced and ambient vibration testing of a permanently instrumented full-scale bridge, in: Department of Civil and Envirionmental Engineering, Utah State University, Logan, 2002, pp. 116. [12] M.J.N. Priestley, F. Seible, G.M. Calvi., Seismic design and retrofit of bridges, John Wiley and Sons, Inc, 1996. [13] S. Alampalli, Influence of in-service environment on modal parameters in: 16th International Modal Analysis Conference, Santa Barbara, CA, USA, 1998, pp. 111-116.
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