Crowdsourcing bridge dynamic monitoring with smartphone vehicle trips

Matarazzo, Thomas J.; Kondor, Dániel; Milardo, Sebastiano; Eshkevari, Soheil Sadeghi; Santi, Paolo; Pakzad, Shamim N.; Buehler, Markus J.; Ratti, Carlo

doi:10.1038/s44172-022-00025-4

Cited by 31 publications

(25 citation statements)

References 87 publications

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“…The method has been extensively used for damage detection and modal identification. 39,40 A modified version of wavelet-based methods is synchrosqueezed wavelet transform (WSST), which was used by Matarazzo et al 34 yielding promising results.…”

Section: Methodsmentioning

confidence: 99%

“…The spatio-temporal response of a bridge is comprised of multiple amplitude-varying harmonics with location-dependent amplitudes and can be represented in the following form: 34 where a ( d,t ) is the bridge response collected by a mobile sensor with space-time coordinates ( d,t ). A m ( t ) is the modal amplitude as a function of time for mode m ∈ {1 ,…,M }.…”

Section: Methodsmentioning

confidence: 99%

“…The spatio-temporal response of a bridge is comprised of multiple amplitude-varying harmonics with locationdependent amplitudes and can be represented in the following form: 34 aðd, tÞ ¼…”

Section: Theorymentioning

confidence: 99%

“…Shirzad-Ghaleroundkhani et al 33 proposed an approach for natural frequency identification of bridges using smartphones. In a real-world experiment, by aggregating smartphone data from multiple private vehicle trips as well as data from ridesharing platforms such as Uber, Matarazzo et al 34 successfully identified multiple modal frequencies of the Golden Gate Bridge. These studies confirmed that by smartphone data crowdsourcing, the mechanical fingerprints of bridges are identifiable.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bridge modal property identification based on asynchronous mobile sensing data

Eshkevari¹,

Cronin

Matarazzo

et al. 2022

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…These values are considered the MPMFs. For a more extensive description of the algorithm, including the mathematical details, one may refer to [30].…”

Section: Most Probable Modal Frequenciesmentioning

confidence: 99%

Drive‐by frequencies extraction by means of synchrosqueezed wavelet transform

Benedetti,

Bernardini,

Argentino

et al. 2023

ce papers

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Bridges and viaducts are facing growing traffic, increasing loads as well as ageing and deterioration. Nowadays, vibration‐based Structural Health Monitoring (SHM) represents an effective solution to extract quantitative information regarding bridge structural condition. Bridge frequencies are fundamental parameters capable to reflect and summarize bridge dynamics and health condition. Typically, their identification is performed by direct approaches, which rely on the use of sensors installed on the structure under analysis. A cost‐efficient, movable, and flexible alternative is offered by drive‐by methods, consisting in the use of instrumented vehicles to extract bridge frequencies. This paper presents an experimental campaign carried out on a multiple‐span mixed railroad bridge, with the aim to identify its frequencies through onboard measurements. A set of MEMS wireless accelerometers is deployed on the vehicle, firstly allowing for vehicle pitch component mitigation. Vehicle accelerations were then used to extract bridge frequencies, by means of a Most Probable Modal Frequencies approach, relying on synchrosqueezed wavelet transform. At a cruise speed of 30 km/h promising results in terms of indirect frequency estimation were found, with no traffic disruption.

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