Extracting modal parameters of high-speed railway bridge using the TDD technique

Kim, Byeong Hwa; Lee, Jungwhee; Lee, Do Hyung

doi:10.1016/j.ymssp.2009.11.010

Cited by 21 publications

(9 citation statements)

References 10 publications

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“…To estimate the mechanical properties to be used for numerical analyses of railway bridges, Rauert et al [114] performed tests on railway ballast within the framework of the European research project DETAILS (Design for optimal life cycle costs of high-speed railway bridges by enhanced monitoring systems). Besides, Kim et al [115] and Feng et al [116] have also obtained key parameters through field tests. In most cases, the TTBDIMs were built without validations due to the difficulty of performing onsite tests on actual railway lines, resulting in the uncertainty of the established models.…”

Section: Field Test For Validating Train-track-bridge Dynamic Modelmentioning

confidence: 99%

Train–track–bridge dynamic interaction: a state-of-the-art review

Zhai

Han

Chen

et al. 2019

Vehicle System Dynamics

332

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Train-track-bridge dynamic interaction is a fundamental concern in the field of railway engineering, which plays an extremely important role in the optimal design of railway bridges, especially in high-speed railways and heavy-haul railways. This paper systematically presents a state-of-the-art review of train-track-bridge dynamic interaction. The evolution process of train-bridge dynamic interaction model is described briefly, from the simplest moving constant force model to the sophisticated train-track-bridge dynamic interaction model (TTBDIM). The modelling methodology of the key elements in the TTBDIM is systematically reviewed, including the train, the track, the bridge, the wheel-rail contact, the track-bridge interaction, the system excitation and the solution algorithm. The significance of detailed track modelling in the whole system is highlighted. The experimental research and filed test focusing on modelling validation, safety assessment and long-term performance investigation of the train-track-bridge system are briefly presented. The practical applications of train-track-bridge dynamic interaction theory are comprehensively discussed in terms of the system dynamic performance evaluation, the system safety assessment and train-induced environmental vibration and noise prediction. The guidance is provided on further improvement of the train-track-bridge dynamic interaction model and the challenging research topics in the future. ARTICLE HISTORY

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Section: Field Test For Validating Train-track-bridge Dynamic Modelmentioning

confidence: 99%

Train–track–bridge dynamic interaction: a state-of-the-art review

Zhai

Han

Chen

et al. 2019

Vehicle System Dynamics

332

View full text Add to dashboard Cite

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“…e TDD technique [34,35] extracts the spatial variables (mode shapes) and the temporal parameters (frequency and damping) separately. A flow chart of the TDD technique is shown in Figure 9.…”

Section: Extraction Of Modal Parameters Using Tdd Techniquementioning

confidence: 99%

“…e overall configuration of an installed NDE system is shown in Figure 2. For output-only ambient modal analysis, time domain decomposition (TDD) technique by Kim et al [34,35] has been adopted because of its practicability and accuracy. e NDE method embedded in the system is the FDIE method by Kim et al [10,11].…”

Section: Introductionmentioning

confidence: 99%

Online Monitoring of Flexural Damage Index of a Cable‐Stayed Bridge

Kim

2019

Shock and Vibration

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This work introduces a recent application of the online nondestructive damage assessment system into a cable-stayed bridge. A set of ambient modal parameters are automatically extracted every 20 minutes using real-time signal data collected from a total of 26 accelerometers attached on the deck plate of the bridge. Then, a set of modal flexibilities are reconstructed by the combination of the extracted modal parameters with the approximated modal mass of the girder. Next, the curvature of the modal flexibility is approximated by a central difference formula. Finally, the set of flexural damage index equations is constructed by comparing the modal curvature of the damaged state to that of the undamaged state. Solving the overdetermined flexural damage index equations, the desired damage index is finally quantified. The resulting index clearly indicates the location and severity of the potential structural damage on the girder. Based on the overall performance of the implemented health monitoring system, the bridge operator’s damage index control criteria are set to ±20% of the undamaged state.

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“…Some improved methods based on SSI have been applied to modal parameter identification of bridges and buildings [15]- [17]. A new modal parameter identification method using the free vibration responses of a bridge based on the time domain decomposition technique (TDD) was proposed by Kim et al [18]. Yan and Ren used power spectrum density transmissibility to identify the modal parameters of a five-story building and an arch bridge [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Identifying Railway Track Rail’s Modal Frequency From Wheel-Rail Excitation and Its Application in High-Speed Railway Monitoring

Gao

Xin

et al. 2019

IEEE Access

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High-speed railway track defects such as rail corrugation, wheel polygonal wear, and rail fastener clip failure are closely related to the modal frequency of the track structure. As the operation time increases, the modal parameters of the track structure will change, and it is necessary to know what they are to diagnose the vibration characteristics. Present methods of modal analysis are limited by measuring points and excitations and cannot be used to extract the operational parameters of the railway track. This paper proposes a novel approach using wheel-rail excitations to identify the rail modal frequency from vibration monitoring data. The approach decomposes the rail acceleration after the wheel-rail excitation passes into intrinsic mode functions and extracts their instantaneous frequencies. The modal frequencies of the rail can be identified from the instantaneous frequencies. To demonstrate the feasibility of the approach, the results using the proposed approach are compared with the modal frequencies of the rail identified using the eigensystem realization algorithm. Then the approach is applied to study the influence of the number of wheel-rail excitations and the number of monitoring points on the identification results in high-speed railway ballastless track rail. The paper concludes that the proposed approach can provide a reliable solution for the identification of the operational modal frequency of track rails based on the monitoring data. INDEX TERMS High-speed railway track, operational modal frequency, synchrosqueezed wavelet transform, variational mode decomposition.

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Extracting modal parameters of high-speed railway bridge using the TDD technique

Cited by 21 publications

References 10 publications

Train–track–bridge dynamic interaction: a state-of-the-art review

Train–track–bridge dynamic interaction: a state-of-the-art review

Online Monitoring of Flexural Damage Index of a Cable‐Stayed Bridge

A Novel Approach of Identifying Railway Track Rail’s Modal Frequency From Wheel-Rail Excitation and Its Application in High-Speed Railway Monitoring

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