A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge

Wang, Yafei; Feng, Hui; Xu, Nan; Zhong, Jiwei; Wang, Zhengxing; Yao, Wenfan; Jiang, Yuyin; Laima, Shujin

doi:10.3390/app14062233

Cited by 3 publications

(1 citation statement)

References 34 publications

(44 reference statements)

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“…The Pearson correlation coefficient is a widely used metric for assessing relationships [24]. It finds extensive application in fields such as data analysis and fault diagnosis [25]. The definition of this coefficient is provided in Equation (10).…”

Section: Imf Component Screeningmentioning

confidence: 99%

Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction

Xu,

Ma,

2024

Applied Sciences

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Aiming at the problem that the existing harmonic detection methods are susceptible to noise interference in the actual working environment, which leads to the reduction of detection accuracy, this paper introduces a novel harmonic detection technique utilizing a RIME optimization algorithm (RIME) to enhance variational mode decomposition (VMD) combined with an improved wavelet threshold (IWT) approach. Initially, RIME optimization refines VMD for the decomposition of harmonic current signals, yielding several modal components. Subsequently, a correlation coefficient method distinguishes between effective and ineffective modal components, discarding the latter. The effective components undergo noise reduction through an enhanced wavelet thresholding technique, and these denoised components are then reconstructed to produce the final noise-reduced signal. Finally, the Hilbert transform is applied to the denoised signal to extract harmonic parameters. Verification through both simulated and actual signal measurements demonstrates that the proposed method not only surpasses other noise reduction algorithms in signal-to-noise ratio and root-mean-square error, but also shows superior accuracy and robustness compared to alternative detection techniques. This method effectively filters out signal noise under noise interference, minimizes detection errors, and achieves precise harmonic signal detection with improved accuracy.

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Section: Imf Component Screeningmentioning

confidence: 99%

Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction

Xu,

Ma,

2024

Applied Sciences

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Study on the Influence of Wind Fairing Parameters on the Aerodynamic Performance of Long-Span Double-Deck Steel Truss Suspension Bridge

Yang,

Li,

Yao

et al. 2024

Buildings

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A long-span double-deck steel truss suspension bridge is easy to produce vortex-induced vibration (VIV) at low air velocity, which affects bridge service life. Additional aerodynamic measures play a role in suppressing VIV by changing the aerodynamic shape, which is a common control method. As the main aerodynamic measure to suppress the VIV response, wind fairing is widely used in engineering practice. In order to obtain the optimal additional position and shape parameters of the fairing, Huangjuetuo Yangtze River Bridge is the research target. Through the combination of a wind tunnel test and numerical simulation, the VIV response of the original and fairing section is studied. Based on data analysis, it is revealed that these additional fairings to the upper chord can significantly reduce the VIV response. When the shape parameters of the fairing are h/D = 1/4 and l/D = 1, the VIV inhibition efficiency is the highest, which can reach 65.51%. By analyzing the flow distribution, it can be seen that VIV is caused mainly by vortex separation in the upper bridge board area. Although this wind fairing does not change the original vortex shedding forms, it changes the first separation point and movement direction of the airflow, making the vortex scale generated by the airflow smaller and the vorticity lower, thus effectively suppressing VIV.

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Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

Chen,

Wu,

Yang

et al. 2024

JMSE

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Vortex-induced vibration is a type of wind-induced vibration occurring frequently in large-span sea-crossing bridges under relatively low wind speeds, posing a threat to the structural fatigue performance and driving comfort. Identifying the instantaneous occurrence moments of vortex-induced vibration is a prerequisite for establishing a data-driven prediction model for vortex-induced vibration, and it is of great significance for the monitoring and early warning of vortex-induced vibration performance in bridges. To automatically detect the occurrence moments of vortex-induced vibration and establish a correlation model between vortex-induced vibration amplitude and environmental factors, this study proposes a fuzzy C-means clustering-based classification method. In order to detect the occurrence moments of vortex-induced vibration more finely, only short-term or even instantaneous structural vibration indicators were selected and transformed for distribution as clustering features. The entire detection process could be carried out unsupervised, reducing the manual cost of obtaining vortex-induced vibration information from massive monitoring data. Finally, actual vortex-induced vibration test data from a certain overseas bridge was utilized to verify the feasibility of this method. Based on the classification results, the correlation between vortex-induced vibration amplitude and environmental variables was determined, providing valuable guidance for predicting vortex-induced vibration amplitudes.

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A Data-Driven Model for Predictive Modeling of Vortex-Induced Vibrations of a Long-Span Bridge

Cited by 3 publications

References 34 publications

Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction

Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction

Study on the Influence of Wind Fairing Parameters on the Aerodynamic Performance of Long-Span Double-Deck Steel Truss Suspension Bridge

Vortex-Induced Vibration Performance Analysis of Long-Span Sea-Crossing Bridges Using Unsupervised Clustering

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