Seismic Damage Assessment of A Base-Isolated Bridge Using Recursive Subspace Identification Algorithm

被引:0
作者
Yu, I-No [1 ]
Loh, Chin-Hsiung [1 ]
Chen, Jun-Da [1 ]
Li, Chuan-Fu [1 ]
机构
[1] Natl Taiwan Univ, Dept Civil Engn, Taipei 10617, Taiwan
来源
SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2018 | 2018年 / 10598卷
关键词
Recursive subspace system identification; Damage detection; matrix inversion lemma algorithm; base-isolated bridge; model updating technique;
D O I
10.1117/12.2295874
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Structural damage assessment under external loading, such as earthquake excitation, is an important issue in structural safety evaluation. In this regard, appropriate data analysis and feature extraction techniques are required to interpret the measured data and to identify the state of the structure and, if possible, to detect the damage. Among these techniques tracking modal parameters and estimating the structural current state from its seismic response measurement can provide useful information for structural safety assessment, therefore, on-line or recursive identification technique needs to be developed for structural seismic response monitoring. In this paper, the recursive subspace identification algorithms based on matrix inversion lemma algorithm (RSI-Inversion) with oblique projection technique was developed. Forgetting factor with enlarge window is introduced in the RSI-Inversion to emphasize the latest state of the time-varying system in this method. In addition to identifying the instantaneous dynamic characteristics of the structural system using RSI, a two-stage damage detection algorithm incorporated with the identified results from RSI will also be applied to localize and quantify the structural damage. Seismic responses of a base-isolated bridge are used to verify the proposed identification and the damage assessment algorithms, i.e. specify its corresponding damage location, the time of occurrence during the excitation, and the percentage of stiffness reduction.
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收藏
页数:12
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