Health monitoring of bridges

Kromanis, Rolands

doi:10.1016/b978-0-12-819946-6.00014-x

Cited by 4 publications

(5 citation statements)

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“…A detailed review of CV-based SHM at local and global levels together with the techniques involved in the listed tasks can be found in [ 8 ]. Visions and frameworks of using fully CV-based SHM systems for structural identification, in which both the structural input and output are obtained by cameras and CV-based techniques with both conventional cameras and smartphones, can be found in [ 79 , 80 ]. This section reviews CV-based bridge monitoring attempts at global and local levels using smartphones at LoM2.…”

Section: Computer Vision-based Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

“…Relevant studies, in which smartphone cameras were used for image collection, have demonstrated that structural defects can be detected and classified for a variety of civil structures [ 109 , 113 ]. A case study concerning the process of vibration parameter characterization is provided in the forthcoming sections based on the testbed monitored via multiple technologies, i.e., smartphone [ 80 ], as well as GNSS [ 114 , 115 ].…”

Section: Computer Vision-based Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

“…It is located in Nottingham, the UK. Studies were carried out to estimate its dynamic properties using smartphone cameras [ 80 ] and GNSS [ 114 , 115 ]. In this study, a video dataset from a smartphone, in which vibrations of the bridge are induced by the synchronized jumping of students at the center of its midspan, is analyzed.…”

Section: Example Case Studiesmentioning

confidence: 99%

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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: Computer Vision-based Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

Section: Computer Vision-based Smartphone Sensing For Bridge Monitoringmentioning

confidence: 99%

Section: Example Case Studiesmentioning

confidence: 99%

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Smartphone Prospects in Bridge Structural Health Monitoring, a Literature Review

Ozer,

Kromanis

2024

Sensors

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“…Smartphone applications for civil infrastructure monitoring have been widely researched [1,2]. Literature also covers a range of case studies when smartphones have been employed for bridge SHM [3,4]. The cameras supported in smartphones have improved at such a rapid pace that today's smartphones have the capability to capture images comparable to professional cameras.…”

Section: Introductionmentioning

confidence: 99%

“…Although such systems enhance spatial resolution with accurate measurements, their applications are also more expensive than using a single camera. Multi-point displacement measurements for the desired accuracy have been obtained also using a single camera [3,[18][19][20]. In a laboratory environment, accurate full-field static displacements of beams have been obtained with a single camera using a photogrammetric measurement approach [21] and tracking deformation contour [22], a robotic camera system with a single camera [23], and a holographic visual sensor consisting of two cameras [24].…”

Section: Introductionmentioning

confidence: 99%

A multiple camera position approach for accurate displacement measurement using computer vision

Kromanis

Kripakaran

2021

J Civil Struct Health Monit

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Engineers can today capture high-resolution video recordings of bridge movements during routine visual inspections using modern smartphones and compile a historical archive over time. However, the recordings are likely to be from cameras of different makes, placed at varying positions. Previous studies have not explored whether such recordings can support monitoring of bridge condition. This is the focus of this study. It evaluates the feasibility of an imaging approach for condition assessment that is independent of the camera positions used for individual recordings. The proposed approach relies on the premise that spatial relationships between multiple structural features remain the same even when images of the structure are taken from different angles or camera positions. It employs coordinate transformation techniques, which use the identified features, to compute structural displacements from images. The proposed approach is applied to a laboratory beam, subject to static loading under various damage scenarios and recorded using multiple cameras in a range of positions. Results show that the response computed from the recordings are accurate, with 5% discrepancy in computed displacements relative to the mean. The approach is also demonstrated on a full-scale pedestrian suspension bridge. Vertical bridge movements, induced by forced excitations, are collected with two smartphones and an action camera. Analysis of the images shows that the measurement discrepancy in computed displacements is 6%.

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