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A quantitative comparison study for structural flexibility identification using Accelerometric and computer vision-based vibration data
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DOI：10.1016/j.jsv.2024.118288.png)
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Abstract
En 中文
For civil engineering structures, the dynamics-based structural health monitoring is commonly based on inverse analysis of structural acceleration response data to identify the structural parameters. As the measurement of structural displacement response becomes more feasible and cost-effective thanks to advances in computer vision techniques, a comparison of the displacement and acceleration response data for structural identification has become of considerable interest among researchers. This paper presents a comprehensive analysis and comparison of structural flexibility identification based on displacement versus acceleration response data through uncertainty quantification technique. Firstly, theoretical derivation reveals that the energy spectral density of acceleration response is equal to the fourth power of the frequency of the energy spectral density of displacement response, which affects the distribution of measurement noise across different frequency bands. Secondly, the uncertainty quantification method for modal flexibility from subspace-based system identification is utilized to compare the derivation process of covariance estimation on modal flexibility. Finally, numerical analysis and experimental example are carried out to compare the structural flexibility identification results when using the displacement versus acceleration. It shows that more precise identification results of lower-order modal parameters are obtained when using displacement, which further improves the precision of modal flexibility identification. The displacement is more suitable for identifying the modal flexibility when the measurement noises are at the same level.
Keywords:
Displacement
Acceleration
Vision -based displacement sensor
Modal flexibility
Uncertainty quantification
Modal parameters
Structural identification
Subspace identification
Journal
IF:
4.9
Papers:
1.7W
Citations:
4.8W
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