Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors

Lydon, Myra; Robinson, D. J.; Taylor, Su; Amato, Giuseppina; Brien, Eugene J O; Uddin, Nasim

doi:10.1007/s13349-017-0229-4

Cited by 44 publications

(15 citation statements)

References 16 publications

(22 reference statements)

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“…Bridge WIM is an SHM method to reconstruct the loading information of a bridge structure by determining the weight of the passing over vehicles [77]. Lydon et al [128,129] used fiber optic sensors for axle detection on an reinforced concrete bridge in Northern Ireland. The results confirmed the performance of the fiber optic sensors for gathering traffic loading information.…”

Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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Bridges are designed to withstand different types of loads, including dead, live, environmental, and occasional loads during their service period. Moving vehicles are the main source of the applied live load on bridges. The applied load to highway bridges depends on several traffic parameters such as weight of vehicles, axle load, configuration of axles, position of vehicles on the bridge, number of vehicles, direction, and vehicle’s speed. The estimation of traffic loadings on bridges are generally notional and, consequently, can be excessively conservative. Hence, accurate prediction of the in-service performance of a bridge structure is very desirable and great savings can be achieved through the accurate assessment of the applied traffic load in existing bridges. In this paper, a review is conducted on conventional vehicle-based health monitoring methods used for bridges. Vision-based, weigh in motion (WIM), bridge weigh in motion (BWIM), drive-by and vehicle bridge interaction (VBI)-based models are the methods that are generally used in the structural health monitoring (SHM) of bridges. The performance of vehicle-assisted methods is studied and suggestions for future work in this area are addressed, including alleviating the downsides of each approach to disentangle the complexities, and adopting intelligent and autonomous vehicle-assisted methods for health monitoring of bridges.

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Section: Vehicle-classification-based Methodsmentioning

confidence: 99%

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Shokravi

Bakhary

et al. 2020

Sensors

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“…WhenFBG is subjected to external factorssuch as pressure, vibration, temperature, stress and strain, refractive index and grating period varies, there will be corresponding changes in the reflected wavelength. Since the parameter of measurement is the wavelength of light which is not affected by electromagnetic fields, the process is immune to electromagnetic interference and hence is intrinsically more stable than any electrical monitoring system as explained in [6]. The reflected wavelength can be calculated as…”

Section: Fiber Bragg Gratingmentioning

confidence: 99%

Smart Monitoring of Flat Wheel in Railway Using Optical Sensors

Sharan¹,

Manna²,

Kaur³

2021

Smart Metering Technologies

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The need for improved safety, reliability and efficiency is one of the most important aspects of the railway industry worldwide. Optical sensors can be used in smart condition monitoring system that can allow real time and continuous monitoring of the structural and operational conditions of trains. Railway monitoring is carried by the use of Fiber Bragg Grating sensors which measures strain, vibration, temperature, acceleration in continuous manner. This chapter covers introduction and working of optical sensors, Finite Element Analysis of rail-wheel geometry and health monitoring of rail wheel. FBG as optical sensor is well known for its advantages such as easy multiplexing, wavelength encoding and multiparameter sensing, immune to electromagnetic interference, reliability, flexibility. Sensitivity of optical sensor in compare to traditional sensors goes as 1.2 pm/μɛ and 10 pm/μɛ for strain and temperature sensor at 1550 nm of wavelength.

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“…Kalhori et al (2017) found out in their field test that shear strain sensors installed close to the support were able to detect even closely spaced axles reliably. Another method suggested by Lydon et al (2017) utilised compressive strain measurements in the girder for the axle detection. The strain sensors were installed to the girder of the bridge so that it could measure the compressive strains (Figure 15 (b)) caused by the vehicle.…”

Section: Axle Detectionmentioning

confidence: 99%

Standardised bridge weigh-in-motion data and its applications in bridge engineering

Qvisen

Lin²,

Lilja

et al. 2021

IABSE Congress Reports

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<p>Applying actual traffic data in bridge analyses will provide more accurate results compared to the results obtained according to the Eurocode traffic load models. Bridge Weigh-in-Motion (B-WIM) measurements are an excellent tool to produce such data. Using B-WIM data as a part of the bridge design or assessment processes has a large potential, but the lack of widely adopted standardised data format hinders broader utilisation of it. This study proposes a new standardised format to present the measured B-WIM data so that in the future, developed software can directly utilise any available B-WIM data. This would make calculations with multiple different traffic compositions and types straightforward and enable the basis for further utilisation of B-WIM data in bridge design/assessment. To demonstrate the benefits, a fatigue case study of an orthotropic bridge deck was conducted, and the results were compared to ones obtained according to Eurocode FLM 4.</p>

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Improved axle detection for bridge weigh-in-motion systems using fiber optic sensors

Cited by 44 publications

References 16 publications

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Vehicle-Assisted Techniques for Health Monitoring of Bridges

Smart Monitoring of Flat Wheel in Railway Using Optical Sensors

Standardised bridge weigh-in-motion data and its applications in bridge engineering

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