Automated Identification, Warning, and Visualization of Vortex-Induced Vibration
Abstract
1. Introduction
- (1)
- Introducing an automated signal processing method to extract the characteristics of the vibration signal.
- (2)
- Proposing a feature index to quantify the stability of the vibration signal in time domain for VIV identification.
- (3)
- Establishing a multi-level warning strategy based on acceleration for VIV warning.
- (4)
- Producing visualized VIV identification results for quick confirmation.
2. Description of VIV
3. Proposed Automatic Identification and Warning Method
3.1. Recurrence Plot
3.2. VIV Identification Method
3.2.1. Determination of Time Delay
3.2.2. Determination of Embedding Dimension
3.2.3. Feature Index for Characterization of VIV and Identification
3.2.4. Multi-Level VIV Warning
3.2.5. Automatic VIV Identification and Warning Process
4. Application for a Suspension Bridge
4.1. Description of the Bridge
4.2. Automated VIV Identification Result
4.3. Validation of Identification Results
4.4. Validation of Multi-Level VIV Warning
4.5. Parametric Analysis
5. Conclusions
- (1)
- The recurrence plot can visualize the difference in the signals corresponding to different vibration states, and it shows multiple parallel diagonal lines for VIV signals while it shows no specific pattern for ambient vibration. The recurrence plot has the advantage in graphing the characteristic of the signal and is very suitable for online identification.
- (2)
- The proposed RCS can estimate the stability of the diagonal lines (vibration signal), and therefore it is capable of identifying the vibration state. For VIV signal, the recurrence plot would show stable parallel diagonal lines and have small RCS, while the RCS would be big for ambient vibration.
- (3)
- The proposed method is capable of identifying the VIVs with small and big vibration amplitudes and different dominant modes. The recurrence plot shows the same characteristics for VIVs with large and small vibration amplitudes. Since the proposed RCS estimates the stability of the signal without any limitation on vibration amplitude, it can be used for identifying VIVs with any amplitude.
- (4)
- The proposed severity ratio can estimate the severity of the VIV through comparing the vibration amplitude with the allowable displacement amplitude. The multi-level warning can successfully warn the VIV with different severities and provide possible early warning for serious VIV.
- (5)
- The processes of determining the optimal parameters, building the recurrence plot, calculating the RCS, identifying the vibration state, and issuing the warning are fully automated. The proposed method can be used for automated VIV identification and continuous online VIV identification without any manual intervention.
- (6)
- One of the novelties of the proposed method is the visualization of the identification results. If a large analytical window is utilized, the visualization procedure would cost too much time, leading to an efficiency problem in real time identification. In real application, the analytical window is suggested to set to 1 min to guarantee a precise identification result and an efficient real time analysis.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Li, H.; Laima, S.; Ou, J.; Zhao, X.; Zhou, W.; Yu, Y.; Li, N.; Liu, Z. Investigation of vortex-induced vibration of a suspension bridge with two separated steel box girders based on field measurements. Eng. Struct. 2011, 33, 1894–1907. [Google Scholar] [CrossRef]
- Zhu, Z.W.; Chen, W.; Li, J.P.; Yang, Y. Field observation of vortex-induced vibration of stiffening cables in a multi-tower cable-stayed bridge with application of analytical mode decomposition. China J. Highw. Transp. 2019, 10, 247–256. [Google Scholar]
- Tang, Z.; Zou, G.; Li, L. The effect of damping on the vortex-induced vibration of a rectangular prism. Phys. Fluids 2024, 36, 023613. [Google Scholar] [CrossRef]
- Hwang, Y.C.; Kim, S.; Kim, H. Cause investigation of high-mode vortex-induced vibration in a long-span suspension bridge. Struct. Infrastruct. Eng. 2020, 16, 84–93. [Google Scholar] [CrossRef]
- Duan, Q.; Shang, J.; Ma, C.; Li, Z. Vortex-induced vibration characteristics of twin-box girder section in long-span bridge: A numerical evaluation method. Phys. Fluids 2025, 37, 14109. [Google Scholar] [CrossRef]
- Inoue, M.; Siringoringo, D.M.; Fujino, Y.; Koike, Y. Identification of Vortex-Induced Vibration on the Osman Gazi Suspension Bridge Tower and Mitigation by an Active Mass Damper. J. Bridge Eng. 2025, 30, 04024108. [Google Scholar] [CrossRef]
- Cao, S.; Zhang, Y.; Tian, H.; Ma, R.; Chang, W.; Chen, A. Drive comfort and safety evaluation for vortex-induced vibration of a suspension bridge based on monitoring data. J. Wind Eng. Ind. Aerodyn. 2020, 204, 104266. [Google Scholar] [CrossRef]
- Diana, G.; Resta, F.; Belloli, M.; Rocchi, D. On the vortex shedding forcing on suspension bridge deck. J. Wind Eng. Ind. Aerodyn. 2006, 94, 341–363. [Google Scholar] [CrossRef]
- Li, H.; Laima, S.; Jing, H. Reynolds number effects on aerodynamic characteristics and vortex-induced vibration of a twin-box girder. J. Fluids Struct. 2014, 50, 358–375. [Google Scholar] [CrossRef]
- Ma, C.M.; Wang, J.X.; Li, Q.S.; Qin, H.; Liao, H.L. Vortex-induced vibration performance and suppression mechanism for a long suspension bridge with wide twin-box girder. J. Struct. Eng. 2018, 144, 4018202. [Google Scholar] [CrossRef]
- Li, H.; Laima, S.; Zhang, Q.; Li, N.; Liu, Z. Field monitoring and validation of vortex-induced vibrations of a long-span suspension bridge. J. Wind Eng. Ind. Aerodyn. 2014, 124, 54–67. [Google Scholar] [CrossRef]
- Li, S.; Kaiser, E.; Laima, S.; Li, H.; Brunton, S.L.; Kutz, J.N. Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems. Phys. Rev. E 2019, 100, 22220. [Google Scholar] [CrossRef]
- Laima, S.; Li, H.; Chen, W.; Ou, J. Effects of attachments on aerodynamic characteristics and vortex-induced vibration of twin-box girder. J. Fluids Struct. 2018, 77, 115–133. [Google Scholar] [CrossRef]
- Fujino, Y.; Yoshida, Y. Wind-induced vibration and control of Trans-Tokyo Bay crossing bridge. J. Struct. Eng. 2002, 128, 1012–1025. [Google Scholar] [CrossRef]
- Ge, C.; Chen, A. Vibration characteristics identification of ultra-long cables of a cable-stayed bridge in normal operation based on half-year monitoring data. Struct. Infrastruct. Eng. 2019, 15, 1567–1582. [Google Scholar] [CrossRef]
- Chen, W.; Gao, D.; Laima, S.; Li, H. A field investigation on vortex-induced vibrations of stay cables in a cable-stayed bridge. Appl. Sci. 2019, 9, 4556. [Google Scholar] [CrossRef]
- Yang, Y.; Ma, T.; Ge, Y. Evaluation on bridge dynamic properties and VIV performance based on wind tunnel test and field measurement. Wind Struct. 2015, 20, 719–737. [Google Scholar] [CrossRef]
- Li, S.; Laima, S.; Li, H. Cluster analysis of winds and wind-induced vibrations on a long-span bridge based on long-term field monitoring data. Eng. Struct. 2017, 138, 245–259. [Google Scholar] [CrossRef]
- Xu, S.; Ma, R.; Wang, D.; Chen, A.; Tian, H. Prediction analysis of vortex-induced vibration of long-span suspension bridge based on monitoring data. J. Wind Eng. Ind. Aerodyn. 2019, 191, 312–324. [Google Scholar] [CrossRef]
- Li, S.; Laima, S.; Li, H. Data-driven modeling of vortex-induced vibration of a long-span suspension bridge using decision tree learning and support vector regression. J. Wind Eng. Ind. Aerodyn. 2018, 172, 196–211. [Google Scholar] [CrossRef]
- Huang, Z.; Li, Y.; Hua, X.; Chen, Z.; Wen, Q. Automatic identification of bridge vortex-induced vibration using random decrement method. Appl. Sci. 2019, 9, 2049. [Google Scholar] [CrossRef]
- Dan, D.; Li, H. Monitoring, intelligent perception, and early warning of vortex-induced vibration of suspension bridge. Struct. Control Health Moni. 2022, 29, e2928. [Google Scholar] [CrossRef]
- He, M.; Liang, P.; Wang, Y.; Xia, Z.; Wu, X. Online automatic monitoring of abnormal vibration of stay cables based on acceleration data from structural health monitoring. Measurement 2022, 195, 111102. [Google Scholar] [CrossRef]
- He, M.; Liang, P.; Zhang, Y.; Wang, Y.; Wang, K. Identification, tracking and warning of vortex induced vibration using k-means clustering method. Struct. Infrastruct. Eng. 2024, 20, 380–393. [Google Scholar] [CrossRef]
- Kim, S.; Kim, T. Machine-learning-based prediction of vortex-induced vibration in long-span bridges using limited information. Eng. Struct. 2022, 266, 114551. [Google Scholar] [CrossRef]
- Arul, M.; Kareem, A.; Kwon, D.K. Identification of vortex-induced vibration of tall building pinnacle using cluster analysis for fatigue evaluation: Application to Burj Khalifa. J. Struct. Eng. 2020, 146, 4020234. [Google Scholar] [CrossRef]
- Su, X.; Mao, J.; Wang, H.; Gao, H.; Li, D. Deep learning-based automated identification on vortex-induced vibration of long suspenders for the suspension bridge. Mech. Syst. Signal Proc. 2025, 224, 112070. [Google Scholar] [CrossRef]
- Yang, Y.; Hou, H.; Yao, G.; Wu, B. Vortex-Induced Vibration Performance Prediction of Double-Deck Steel Truss Bridge Based on Improved Machine Learning Algorithm. J. Mar. Sci. Eng. 2025, 13, 767. [Google Scholar] [CrossRef]
- Zhu, Q.; Xu, Y.L.; Zhu, L.D.; Li, H. Vortex-induced vibration analysis of long-span bridges with twin-box decks under non-uniformly distributed turbulent winds. J. Wind Eng. Ind. Aerodyn. 2018, 172, 31–41. [Google Scholar] [CrossRef]
- Zou, Y.; Donner, R.V.; Marwan, N.; Donges, J.F.; Kurths, J. Complex network approaches to nonlinear time series analysis. Phys. Rep. 2018, 787, 1–97. [Google Scholar] [CrossRef]
- Li, A.D.; He, Z.; Wang, Q.; Zhang, Y.; Ma, Y. A multi-objective evolutionary algorithm with mutual-information-guided improvement phase for feature selection in complex manufacturing processes. Eur. J. Oper. Res. 2025, 323, 952–965. [Google Scholar] [CrossRef]
- Wallot, S.; Mønster, D. Calculation of average mutual information (AMI) and false-nearest neighbors (FNN) for the estimation of embedding parameters of multidimensional time series in matlab. Front. Psychol. 2018, 9, 1679. [Google Scholar] [CrossRef] [PubMed]
Vibration State | Original Signal | Recurrence Plot | Statistic of Vertical Distance | RCS |
---|---|---|---|---|
Ambient vibration | 0.023 | |||
Formation stage of VIV | 0.254 | |||
Stabilized stage of VIV | 0.895 |
Vibration State | Ambient Vibration | Incipient Stage | Formation Stage | Fully Developed Stage |
---|---|---|---|---|
RCS | 0.0–0.1 | 0.1–0.5 | 0.5–0.8 | 0.8–1.0 |
Warning Level | Threshold |
---|---|
No warning | (0, 0.05] |
First level warning | (0.05, 0.5] |
Second level warning | (0.5, 0.8] |
Third level warning | (0.8~) |
Analytical Window (Unit: min) | Computing Time 1 (Unit: s) | Computing Time 2 (Unit: s) |
---|---|---|
1 | 1.7 | 10.3 |
2 | 6.9 | 61 |
3 | 16.4 | 338 |
4 | 37.5 | 473 |
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He, M.; Liang, P.; Lu, X.-S.; Pan, Y.-H.; Zhang, D. Automated Identification, Warning, and Visualization of Vortex-Induced Vibration. Sensors 2025, 25, 6169. https://doi.org/10.3390/s25196169
He M, Liang P, Lu X-S, Pan Y-H, Zhang D. Automated Identification, Warning, and Visualization of Vortex-Induced Vibration. Sensors. 2025; 25(19):6169. https://doi.org/10.3390/s25196169
Chicago/Turabian StyleHe, Min, Peng Liang, Xing-Shun Lu, Yu-Hao Pan, and Di Zhang. 2025. "Automated Identification, Warning, and Visualization of Vortex-Induced Vibration" Sensors 25, no. 19: 6169. https://doi.org/10.3390/s25196169
APA StyleHe, M., Liang, P., Lu, X.-S., Pan, Y.-H., & Zhang, D. (2025). Automated Identification, Warning, and Visualization of Vortex-Induced Vibration. Sensors, 25(19), 6169. https://doi.org/10.3390/s25196169