Analytical mode decomposition with Hilbert transform for modal parameter identification of buildings under ambient vibration

2014 ◽  
Vol 59 ◽  
pp. 173-184 ◽  
Author(s):  
Zuocai Wang ◽  
Genda Chen
Keyword(s):  
Parameter Identification ◽  
Hilbert Transform ◽  
Ambient Vibration ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Mode Decomposition

Modal Parameter Identification of Hagia Sophia Bell-Tower via Ambient Vibration Test

2009 ◽  
Vol 28 (1) ◽  
pp. 37-47 ◽  
Author(s):  
Alemdar Bayraktar ◽  
Temel Türker ◽  
Barış Sevım ◽  
Ahmet Can Altunişik ◽  
Faruk Yildirim
Keyword(s):  
Parameter Identification ◽  
Ambient Vibration ◽  
Vibration Test ◽  
Modal Parameter ◽  
Ambient Vibration Test ◽  
Modal Parameter Identification ◽  
Bell Tower ◽  
Hagia Sophia

Consistent Multilevel RDT-ERA for Output-Only Ambient Modal Identification of Structures

2017 ◽  
Vol 17 (09) ◽  
pp. 1750106 ◽  
Author(s):  
Zhouquan Feng ◽  
Wenai Shen ◽  
Zhengqing Chen
Keyword(s):  
Parameter Identification ◽  
Modal Identification ◽  
Ambient Vibration ◽  
Weighted Averaging ◽  
Modal Parameters ◽  
Modal Parameter ◽  
Consistency Analysis ◽  
Modal Parameter Identification ◽  
Random Decrement Technique ◽  
Random Decrement

This paper presents an improved method called the consistent multilevel random decrement technique in conjunction with eigensystem realization algorithm (RDT-ERA) for modal parameter identification of linear dynamic systems using the ambient vibration data. The conventional RDT-ERA is briefly revisited first and the problem of triggering level selection in the RDT is thoroughly studied. Due to the use of a single triggering level by the conventional RDT-ERA, an inappropriate triggering level may produce poor random decrement (RD) functions, thereby yielding a poor estimate of modal parameters. In the proposed consistent multilevel RDT-ERA, multiple triggering levels are used and a consistency analysis is proposed to sift out the RD functions that deviate largely from the majority of the RD functions. Then the ERA is applied to the retained RD functions for modal parameter identification. Subsequently, a similar consistency analysis is conducted on the identified modal parameters to sift out the outliers. Finally, the final estimates of the modal parameters are calculated using weighted averaging with the weights set proportional to the number of RD segments extracted from the corresponding triggering levels. The proposed method is featured by the fact that the information from the signal is fully utilized using multiple triggering levels and the outliers are sifted out using consistency analysis, thus making the identified result more accurate and reliable. The effectiveness and accuracy of the method have been demonstrated in the examples using the simulated data and experimental data.

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