Damage scenario analysis of bridges using crowdsourced smartphone data from passing vehicles

Sitton, Jase D.; Rajan, Dinesh; Story, Brett A.

doi:10.1111/mice.13130

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…In realistic environments, it is essential to eliminate the operational effects that can mask damage indicators in modal frequencies [ 134 ] and account for the vehicle properties for the precise removal of vehicular effects distorting indirect bridge data [ 135 ]. And the next step following drive-by modal identification is damage detection, which can also infer the remaining useful life of beam-like systems [ 136 ]. Recent progress in drive-by SHM shows promising progress in the way the contact-point response is representative of modal parameters [ 137 ] and accordingly bridge damage [ 138 ].…”

Section: Drive-by Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Ozer,

Kromanis

2024

Sensors

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Bridges are critical components of transportation networks, and their conditions have effects on societal well-being, the economy, and the environment. Automation needs in inspections and maintenance have made structural health monitoring (SHM) systems a key research pillar to assess bridge safety/health. The last decade brought a boom in innovative bridge SHM applications with the rise in next-generation smart and mobile technologies. A key advancement within this direction is smartphones with their sensory usage as SHM devices. This focused review reports recent advances in bridge SHM backed by smartphone sensor technologies and provides case studies on bridge SHM applications. The review includes modelbased and data-driven SHM prospects utilizing smartphones as the sensing and acquisition portal and conveys three distinct messages in terms of the technological domain and level of mobility: (i) vibration-based dynamic identification and damage-detection approaches; (ii) deformation and condition monitoring empowered by computer vision-based measurement capabilities; (iii) drive-by or pedestrianized bridge monitoring approaches, and miscellaneous SHM applications with unconventional/emerging technological features and new research domains. The review is intended to bring together bridge engineering, SHM, and sensor technology audiences with decade-long multidisciplinary experience observed within the smartphone-based SHM theme and presents exemplary cases referring to a variety of levels of mobility.

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Section: Drive-by Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Ozer,

Kromanis

2024

Sensors

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Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

Yang,

Xu,

Gao

et al. 2024

Computer aided Civil Eng

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Considering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second‐order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first‐order modal shape curve is constructed using the second‐order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on‐site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.

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Bridge monitoring using mobile sensing data with traditional system identification techniques

Cronin,

Sen,

Marasco

et al. 2024

Computer aided Civil Eng

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Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

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Damage scenario analysis of bridges using crowdsourced smartphone data from passing vehicles

Cited by 4 publications

References 40 publications

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

Bridge monitoring using mobile sensing data with traditional system identification techniques

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