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Multivariate statistical analysis for early damage detection
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DOI：10.1016/j.engstruct.2013.05.022.png)
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摘要
En 中文
A large amount of researches and studies have been recently performed by applying statistical methods for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting in the combination of advanced multivariate statistical methods and quantities, such as principal components, symbolic data and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Structural Health Monitoring
Early-damage detection
Principal component analysis
Symbolic data
Symbolic dissimilarity measures
Cluster analysis
Numerical model
Damage simulations
期刊
IF:
6.4
论文数:
2.1W
被引数:
8.7W
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引用论文
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