Simplified vehicle–bridge interaction for medium to long-span bridges subject to random traffic load

Eshkevari, Soheil Sadeghi; Matarazzo, Thomas J.; Pakzad, Shamim N.

doi:10.1007/s13349-020-00413-4

Cited by 28 publications

(13 citation statements)

References 33 publications

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“…Vehicle speed and bridge deck roughness also affect the vibration profile of the bridge. At higher speeds, vehicle vibration dominates the observed vibration signal, hiding the bridge vibration itself, and the vehicle speed needs to be relatively low to obtain good separation between bridge and vehicle vibration frequency, as previously reported: 18 km/h [ 23 ], 36 km/h [ 24 ], and 40 km/h [ 19 ].…”

Section: Related Workmentioning

confidence: 82%

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Aliansyah

Shimasaki

Senoo

et al. 2021

Sensors

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Vision-based structural displacement methods allow convenient monitoring of civil structures such as bridges, though they are often limited due to the small number of measurement points, constrained spatial resolution, and inability to identify the acting forces of the measured displacement. To increase the number of measurement points in vision-based bridge displacement measurement, this study introduces a front-view tandem marker motion capture system with side-view traffic counting to identify the force-inducing passing vehicles on the bridge’s deck. The proposed system was able to measure structural displacement at submillimeter resolution on eight measurement points at once at a distance of 40.8–64.2 m from a front-view camera. The traffic counting system with a side-view camera recorded the passing vehicles from two opposing lanes. We conducted a 35-min experiment for a 25 m-span steel road bridge with hundreds of cars passing over it and confirmed dynamic displacement distributions with amplitudes of several millimeters when large vehicles passed.

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Section: Related Workmentioning

confidence: 82%

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Aliansyah

Shimasaki

Senoo

et al. 2021

Sensors

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“…Most importantly, the asymptotic convergence of the AMSs to the exact operational mode shapes holds when the bridge is subjected to a stationary random spatio-temporal excitation. Recently, Sadeghi Eshkevari et al 42 confirmed theoretically and numerically that the collective effect of random traffic in urban bridges is extremely similar to a random Gaussian bridge excitation. This random function remains stationary as long as the traffic flow is normal.…”

Section: Methodsmentioning

confidence: 93%

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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“…For the implementation of vibration-based bridge health monitoring, sufficient external load is usually required to excite the bridge structure to a certain extent (Makki Alamdari et al, 2021). For a bridge subjected to a medium to large volume of random operational traffic, the spatio-temporal load pattern can be modeled as a random white noise with sufficient accuracy (Sadeghi Eshkevari et al, 2020). Therefore, the VBI model considered for bridge modal identification is shown in Figure 1.…”

Section: Vehicle-bridge Interaction Model Considering Operational Loadmentioning

confidence: 99%

“…Based on equation (1), the operational load is transformed into equivalent nodal forces at the nodes of the bridge beam elements. It can be viewed as applying the random load along the bridge span to consider its spatio-temporal characteristics (Sadeghi Eshkevari et al, 2020). The response of the test vehicle is then analyzed for the bridge modal identification.…”

Section: Dynamics Of the Test Vehiclementioning

confidence: 99%

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

Zhu

Guo

2022

Advances in Structural Engineering

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Bridge modal identification using an instrumented test vehicle as a moving sensor is promising but challenging. A key factor is to extract bridge dynamic components from vehicle responses measured when the bridge is operating. A new method based on an advanced adaptive signal decomposition technique, the successive variational mode decomposition (SVMD), has been developed to estimate the bridge modal parameters from the dynamic responses of a passing test vehicle. When bridge-related dynamic components are extracted from the decomposition, the natural excitation technique and/or random-decrement technique based fitting methods are used to estimate the modal frequencies and damping ratios of the bridge. Effects of measurement noise, moving speed and vehicle properties on the decomposition are investigated numerically. The superiority of SVMD in the decomposition is verified by comparing to another adaptive decomposition technique, the singular spectrum decomposition. The results of the proposed method confirm that the bridge modal frequencies can be identified from bridge related components with high accuracy, while damping ratio is more sensitive to the random operational load. Finally, the feasibility of the proposed method for bridge monitoring using a moving test vehicle is further verified by an in-situ experimental test on a cable-stayed bridge. The components related to the bridge dynamic responses are successfully extracted from vehicle responses.

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Simplified vehicle–bridge interaction for medium to long-span bridges subject to random traffic load

Cited by 28 publications

References 33 publications

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Single-Camera-Based Bridge Structural Displacement Measurement with Traffic Counting

Bridge modal property identification based on asynchronous mobile sensing data

Bridge modal identification based on successive variational mode decomposition using a moving test vehicle

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