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  5. Numerical and experimental verifications on damping identification with model updating and vibration monitoring data

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Article
en
2017

Numerical and experimental verifications on damping identification with model updating and vibration monitoring data

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en
2017
Vol 20 (2)
Vol. 20
DOI: 10.12989/sss.2017.20.2.127ira.lib.polyu.edu.hk/handle/10397/74799

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Jun Li
Jun Li

Curtin University

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Jun Li
Hong Hao
Gao Fan
+5 more

Abstract

Identification of damping characteristics is of significant importance for dynamic response analysis and condition assessment of structural systems. Damping is associated with the behavior of the energy dissipation mechanism. Identification of damping ratios based on the sensitivity of dynamic responses and the model updating technique is investigated with numerical and experimental investigations. The effectiveness and performance of using the sensitivity-based model updating method and vibration monitoring data for damping ratios identification are investigated. Numerical studies on a three-dimensional truss bridge model are conducted to verify the effectiveness of the proposed approach. Measurement noise effect and the initial finite element modelling errors are considered. The results demonstrate that the damping ratio identification with the proposed approach is not sensitive to the noise effect but could be affected significantly by the modelling errors. Experimental studies on a steel planar frame structure are conducted. The robustness and performance of the proposed damping identification approach are investigated with real measured vibration data. The results demonstrate that the proposed approach has a decent and reliable performance to identify the damping ratios.

How to cite this publication

Jun Li, Hong Hao, Gao Fan, Pinghe Ni, Xiangyu Wang, Changzhi Wu, Jae‐Myung Lee, Kwang‐Hyo Jung (2017). Numerical and experimental verifications on damping identification with model updating and vibration monitoring data. , 20(2), DOI: https://doi.org/10.12989/sss.2017.20.2.127.

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Publication Details

Type

Article

Year

2017

Authors

8

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.12989/sss.2017.20.2.127

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