Crowdsourcing Bridge Vital Signs 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:10.48550/arxiv.2010.07026

Cited by 1 publication

(4 citation statements)

References 31 publications

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“…First, the frequencies are found using a traditional fixed sensor network, the state of practice in SHM, which were used as a reference for analysis. Second, the natural frequencies are found with the recently proposed algorithm MPMF 29 . The goal of determining the frequencies in such a way is to demonstrate that all of the results in the work can be found only using measurements obtained from passing vehicles.…”

Section: Absolute Modeshape Identification Methodologymentioning

confidence: 99%

“…For SVT data, two distinct datasets are evaluated. The first dataset, GGB-C, consists of 102 trips collected in a controlled environment 29 , i.e., key variables such as vehicle velocity and smartphone orientation were controlled. Uber provided the second SVT dataset GGB-UC from its ride-hailing fleet, which consists of 50 trips made by a diverse set of drivers and vehicles (thus, uncontrolled).…”

Section: Golden Gate Bridgementioning

confidence: 99%

“…to determine structural dynamical properties, which enables widespread bridge monitoring at a lower cost; and (ii) mobile sensors efficiently capture dense spatial vibration information, e.g., high-resolution structural mode shapes [25][26][27][28] . Recent work presented multiple real-world applications in which crowdsourced smartphone-vehicle-trip (SVT) data were used to accurately determine modal properties of two bridges 29 . These results emphasized the unique and significant advantages of crowdsourced data.…”

Section: Introductionmentioning

confidence: 99%

“…It is key to emphasize that this method uses prior estimates of natural frequencies. An approach for determin reviewed in the SI along with validations on the presented case studies29 .…”

mentioning

confidence: 99%

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Bridging the Gap: Commodifying Infrastructure Condition Data with Crowdsensing

Eshkevari

Matarazzo

Milardo

et al. 2023

Preprint

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Structural information deficits about our aging bridges allowed several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of large-scale deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive yet has produced structural information, i.e., modal frequencies, in crowdsensing applications. However, scalable bridge damage detection systems are unknown. Here we present the most extensive real-world study on bridge monitoring with crowdsourced smartphone-vehicle trips and simulate damage detection capabilities. Our method analyzes over 500 trips across four bridges with main spans ranging from 30 to 1300 meters in length, representing about one-quarter of US bridges, and extracts absolute value mode shapes, a damage-sensitive feature. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result is the potential to commodify data-driven structural assessments globally.

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Section: Absolute Modeshape Identification Methodologymentioning

confidence: 99%

Section: Golden Gate Bridgementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…It is key to emphasize that this method uses prior estimates of natural frequencies. An approach for determin reviewed in the SI along with validations on the presented case studies29 .…”

mentioning

confidence: 99%

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