4.7 Article

An efficient algorithm for simultaneous identification of time-varying structural parameters and unknown excitations of a building structure

期刊

ENGINEERING STRUCTURES
卷 98, 期 -, 页码 29-37

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2015.04.019

关键词

Time-varying structural parameters; Unknown excitations; Simultaneous identification; Projection matrix; Time-varying correction factor; Least-squares estimation

资金

  1. Research Grants Council of the Hong Kong (PolyU) [5319/10E]
  2. National Natural Science Foundation of China (NSFC) [50830203]
  3. Natural Science Foundation of Guangdong Province, China [2014A030310193]

向作者/读者索取更多资源

When a structure is being damaged under unknown excitations, structural parameters of the damaged elements are actually varying with time. Hence, the simultaneous identification of time-varying structural parameters and unknown excitations is an important task in structural health monitoring. Although some analytical methods for such identifications are available in the literature, they are complicated, time-consuming, or restrained by special requirements. This paper presents an efficient algorithm for identifying time-varying structural parameters of a building structure under unknown excitations. By projecting on to the column space of influence matrix of unknown excitations, the observation equation of the structural system with unknown excitations is transformed from a multiple linear regression equation to a simple linear regression equation. By further introducing a time-varying correction factor matrix, an analytical recursive least-squares estimation algorithm is developed for identifying unknown excitations and time-varying structural parameters such as stiffness and damping. The feasibility and accuracy of the proposed algorithm are finally demonstrated through numerical examples and comparison with the existing methods. The results clearly exhibit that the proposed algorithm can simultaneously identify unknown excitations and time-varying structural parameters efficiently and accurately. (C) 2015 Elsevier Ltd. All rights reserved.

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