Extraction of Bridge Modal Parameters Using Passing Vehicle Response

Tan, Chao; Uddin, Nasim; O’Brien, Eugene J.; McGetrick, Patrick; Kim, Chulwoo

doi:10.1061/(asce)be.1943-5592.0001477

Cited by 69 publications

(24 citation statements)

References 34 publications

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“…While in indirect monitoring systems the passing by vehicles is instrumented and the dynamic parameters of the bridge structure are obtained from the measured vehicular vibration response. These methods are also known as "drive-by" SHM methods [23]. The dynamic of moving load on bridges has been the target of many theoretical and numerical studies since the first report by Frýba [25] in the 1960s.…”

Section: Vehicle-bridge Interaction (Vbi)-based Methodsmentioning

confidence: 99%

“…Since then, several researches have been published to improve the topic into its current state [20]. Tan et al [23] proposed an algorithm to extract mode shapes and damping ratio using information from the VBI model. A numerical model of a sprung mass of a quarter-car model is adopted at a constant low speed to verify the proposed algorithm.…”

Section: Stages Of Damage Detectionmentioning

confidence: 99%

“…The effect of the deformation of a pneumatic tire is neglected in the mathematical model for the purpose of simplification. Tan et al [23] investigated the effect of road surface roughness in the identification of the natural frequencies of the bridge. It was stated that the roughness has a negative impact on the identification of higher modes because of their relatively lower amplitudes.…”

Section: Road Surfacementioning

confidence: 99%

“…The study mainly focused on smart methods by utilizing efficient smartphones, cameras, drones, and robotic sensors. In other studies such as those by Zhu and Law [ 22 ], Tan et al [ 23 ], and Kim et al [ 24 ], VBI-based techniques as well as drive-by methods have been the focus of the study. Even though some reviews have been carried out, a comprehensive review of the vehicle assisted techniques in bridge SHM is still missing.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

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Section: Vehicle-bridge Interaction (Vbi)-based Methodsmentioning

confidence: 99%

Section: Stages Of Damage Detectionmentioning

confidence: 99%

Section: Road Surfacementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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“…Yang et al used an analytical method to derive the dynamic responses of a bridge and identified the damping [20], mode shape [21], and resonance frequency [22] of the bridge based on vehicle excitation. In addition to the above traditional time-domain methods, time-frequency domain methods [23], artificial neural networks (ANN) [24] and Bayesian analysis [25] have been used to identify the modal parameters of bridges. There is some related research in other engineering fields as well.…”

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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Bridge influence surface identification method considering the spatial effect of vehicle load

Yang

et al. 2021

Struct Control Health Monit

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Summary Bridge influence surface is an important tool to study the bridge responses under the moving loads, which contains tremendous structural information. The structural deterioration during the service life would induce the variation of the bridge influence surface, which makes it essential to identify the influence surface exactly from the field measured data under moving vehicle loads. This paper firstly summarizes current development of bridge influence surface identification methods and classifies the methods in which the vehicle is respectively equivalent to line loads and concentrated loads. After that, the actual spatial distribution of the vehicle load is considered, based on which a new identification method is presented to solve the boundary problem of current influence surface identification methods. Then a numerical slab model with two fixed ends is illustrated to prove the validity of the method, and the accuracy and identifiable region of different methods in this condition are compared with the other methods. Influence factors of identification accuracy including noise level, axle number of vehicle and loading point are analyzed. Finally, laboratory verification using the same slab model is prepared to verify the effectiveness of the proposed identification method. Results of this study provide an effective identification method of bridge influence surface with very high identification accuracy, which can be used to full bridge influence surface test.

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Extraction of Bridge Modal Parameters Using Passing Vehicle Response

Cited by 69 publications

References 34 publications

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Vehicle-Assisted Techniques for Health Monitoring of Bridges

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

Bridge influence surface identification method considering the spatial effect of vehicle load

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