Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges

Zhang, Mingjin; Huang, Xianfu; Li, Yongle; Sun, Hao; Zhang, Jingyu; Huang, Bin

doi:10.1155/2020/4360184

Cited by 11 publications

(6 citation statements)

References 24 publications

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“…The method has been extensively used for damage detection and modal identification. 39,40 A modified version of wavelet-based methods is synchrosqueezed wavelet transform (WSST), which was used by Matarazzo et al 34 yielding promising results.…”

Section: Methodsmentioning

confidence: 99%

Bridge modal property identification based on asynchronous mobile sensing data

Eshkevari¹,

Cronin

Matarazzo

et al. 2022

Structural Health Monitoring

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There is a growing attention in real-time bridge condition assessment using data from drive-by vehicles as a potentially scalable approach. Most system identification methods are based on synchronized vibration data collection for this purpose. This study presents an approach for bridge modal identification that estimates high-resolution absolute value of the operational mode shapes using asynchronous mobile data. With each trip of a vehicular sensor, the spatio-temporal response of the bridge is sampled, along with various sources of noise, e.g. vehicle dynamics, environmental effects, road profile, etc. The crowdsourced modal identification using continuous wavelet (CMICW) method is proposed that gradually magnifies the bridge dynamical signatures and mitigates noise over the spatio-temporal map. The performance of the CMICW method is validated in an experimental setting. The method successfully identifies natural frequencies and absolute value of the operational mode shapes of a bridge with high resolution and accuracy. Notably, by including data collected from various bridge lanes, the method can reconstruct 3D representation of the mode shapes. The influence of the speed of the mobile sensors on the accuracy of the estimated modal properties is investigated as well. Using a hybrid simulation framework, the effect of vehicle dynamic is included in the mobile sensing data. The study shows that the CMICW method is successful in discounting the effect of vehicle dynamics, thereby strengthening the bridge modal information. Finally, a blind source separation technique is implemented to separate the effects of road irregularities, which further improves the accuracy of modal property estimates. This study contributes to the growing body of knowledge on mobile crowdsensing for physical properties of transportation infrastructure.

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Section: Methodsmentioning

confidence: 99%

Bridge modal property identification based on asynchronous mobile sensing data

Eshkevari¹,

Cronin

Matarazzo

et al. 2022

Structural Health Monitoring

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show abstract

“…Therefore, it is necessary to adjust the wavelet basis function to increase its frequency-domain resolution, but increasing the frequency-domain resolution will lower the time-domain resolution due to the Heisenberg Uncertainty Principle. 10 As a result, it will introduce a significant error into the calculation of the damping ratio. Hence, the smallest frequency-domain resolution that maintains the frequency-domain resolution should be selected to achieve optimal analysis results.…”

Section: Determination Of Optimal Wavelet Parameters For Multi-mode V...mentioning

confidence: 99%

Continuous wavelet transform for modal parameter identification of flutter data

Guo

2023

Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XVII

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This paper is based on the continuous wavelet transform to achieve modal parameter identification of the response signal of a wing under environmental excitation. Suitable wavelet basis functions are selected for the characteristics of flutter data to achieve a good time-frequency analysis of flutter data. And the endpoint effect is effectively suppressed by the predictive extension method of support vector machine. The identification of wavelet ridges and the wavelet cross-section are obtained through a crazy creep algorithm to achieve the identification of modal damping in the chattering data. Finally, the method is applied to the wind tunnel test data of a 3d-printed wing to identify its modal parameters, and is compared with its numerical simulation data to verify the reliability of CWT applied to the identification of modal parameters of wing flutter data under environmental excitation.

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“…This paper uses the wind-vehicle-bridge coupled analysis system BANSYS (Bridge Analysis System) 19,20 to calculate the acceleration response of Oujiang Bridge under the combined action of trains and fluctuating wind loads. In the wind-vehicle-bridge coupled vibration analysis of Oujiang Bridge, the wind speed level is 30 m/s, and the speed is 72 km/h.…”

Section: Response Of the Bridge Under Train And Fluctuating Wind Loadmentioning

confidence: 99%

Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion

Zhou

Jiang

Zhang

et al. 2021

Measurement and Control

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In the wind tunnel test of a long-span bridge model, to ensure that the dynamic characteristics of the model can satisfy the test design requirements, it is particularly important to accurately identify the modal parameters of the model. First, the stochastic subspace identification algorithm was used to analyze the modal parameters of the model in the wind tunnel test; then, Grubbs criterion was introduced to effectively eliminate outliers in the damping ratio matrix. Stochastic subspace identification algorithm with Grubbs criterion improved the accuracy of the modal parameter identification and the ability to determine system matrix order and prevented the modal omissions caused by determining the stable condition of the damping ratio in the stability diagram. Finally, Oujiang Bridge was used as an example to verify the stochastic subspace identification algorithm with Grubbs criterion and compare with the results of the finite element method. The example shows that the improved method can be effectively applied to the modal parameter identification of bridges.

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Improved Continuous Wavelet Transform for Modal Parameter Identification of Long-Span Bridges

Cited by 11 publications

References 24 publications

Bridge modal property identification based on asynchronous mobile sensing data

Bridge modal property identification based on asynchronous mobile sensing data

Continuous wavelet transform for modal parameter identification of flutter data

Modal parameters identification of bridge by improved stochastic subspace identification method with Grubbs criterion

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