UM
Recursive stochastic subspace identification for structural parameter estimation
Chang C.C.; Li Z.
2009-12-01
Source PublicationProceedings of SPIE - The International Society for Optical Engineering
Volume7292
IssuePART 1
AbstractIdentification of structural parameters under ambient condition is an important research topic for structural health monitoring and damage identification. This problem is especially challenging in practice as these structural parameters could vary with time under severe excitation. Among the techniques developed for this problem, the stochastic subspace identification (SSI) is a popular time-domain method. The SSI can perform parametric identification for systems with multiple outputs which cannot be easily done using other time-domain methods. The SSI uses the orthogonal-triangular decomposition (RQ) and the singular value decomposition (SVD) to process measured data, which makes the algorithm efficient and reliable. The SSI however processes data in one batch hence cannot be used in an on-line fashion. In this paper, a recursive SSI method is proposed for on-line tracking of time-varying modal parameters for a structure under ambient excitation. The Givens rotation technique, which can annihilate the designated matrix elements, is used to update the RQ decomposition. Instead of updating the SVD, the projection approximation subspace tracking technique which uses an unconstrained optimization technique to track the signal subspace is employed. The proposed technique is demonstrated on the Phase I ASCE benchmark structure. Results show that the technique can identify and track the time-varying modal properties of the building under ambient condition. © 2009 SPIE.
KeywordModal analysis Recursive Structural dynamics Time-varying systems
DOI10.1117/12.815422
URLView the original
Language英語
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Document TypeConference paper
CollectionUniversity of Macau
AffiliationHong Kong University of Science and Technology
Recommended Citation
GB/T 7714
Chang C.C.,Li Z.. Recursive stochastic subspace identification for structural parameter estimation[C],2009.
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