Bridge Weigh-in-Motion system for the identification of train loads using fiber-optic technology

Pimentel, Ricardo; Ribeiro, Diogo; Matos, Luís M.P.; Mosleh, Araliya; Calçada, Rui

doi:10.1016/j.istruc.2021.01.070

Cited by 35 publications

(18 citation statements)

References 41 publications

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“…The time between detections was a product of the number of samples between peak arisings and the sample interval. Wheel voltage ratios were transferred to wheel loads in tonnes through cc; see Equation (2). The coefficients of transferring signals from channels to the train axle loads (calibration coefficients) were obtained through the comparison of the measured strain data with the load axles of known trains.…”

Section: Wheelspeeds Trainspeed and Traindirectionmentioning

confidence: 99%

“…On the other hand, study [2] uses trains with distributed loads between axles in a bogie to verify their methods of measuring wheel loads and estimating real train load distributions of passenger trains with equal axle weights in one bogie. Finally, study [25] presents the geometry of wagons with distributed loads in bogies for Jacobs bogie-type wagons and distributed loads in all axles of two-axle and bogie wagons.…”

Section: Measurement Errormentioning

confidence: 99%

“…The wheel loads can be obtained by directly measuring the wheel rail contact force [1] or indirectly detecting the force applied to the infrastructure. The monitoring of traffic loads from the infrastructure viewpoint allows a train set to be measured using one measurement system which can be installed on the bridge (bridge weigh-in-motion system, B-WIM [2,3]) or on the track (weigh-in-motion system, WIM [4,5]). The WILD system (wheel impact load detector [6]) is based on the same principle as the WIM system and is used for the identification of damaged wheels.…”

Section: Introductionmentioning

confidence: 99%

“…The B-WIM system [2] measures deformation on the level of bridge and calculates axle weights using Moses algorithm [7]. The basic of this algorithm is to minimize the difference between the measured and predicted bridge response based on the Influence Lines (IL) concept [8].…”

Section: Introductionmentioning

confidence: 99%

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Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads

Zakharenko

Frøseth

Rönnquist

2022

Sensors

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This paper presents a methodology for classifying train passages into different types with a weigh-in-motion (WIM) system to allow the calibration of railway fatigue load models and identify individual vehicles from the measurements for the continuous calibration of railway WIM stations from in-service trains. The quality assurance of the measured responses is demonstrated using statistical methods. This paper discusses the measurement station, the method used for processing the raw data, the algorithm used to identify the train types and vehicles automatically, and the limits of the obtained load spectra. The measurement errors are demonstrated to be satisfying for use in fatigue load model calibration. Furthermore, this paper proposes actions for accurately obtaining the actual traffic conditions and describes the future work required in this area.

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Section: Wheelspeeds Trainspeed and Traindirectionmentioning

confidence: 99%

Section: Measurement Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads

Zakharenko

Frøseth

Rönnquist

2022

Sensors

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“…Popular WIM sensors reported in the literature include bending plates [11], load cells [12], piezoelectric sensors [13,14] and in-pavement fiber Bragg gratings (FBGs) [15]. The specialization to bridge applications, also known as bridge WIM or B-WIM, is often conducted by measuring the bridge's deformations using strain sensors [16][17][18][19][20], accelerometers [21][22][23], or computer vision systems [24].…”

Section: Introductionmentioning

confidence: 99%

Innovative Carbon-Doped Composite Pavements with Sensing Capability and Low Environmental Impact for Multifunctional Infrastructures

et al. 2021

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Recently, smart composites that serve as multi-functional materials have gained popularity for structural and infrastructural applications yielding condition assessment capabilities. An emerging application is the monitoring and prediction of the fatigue of road infrastructure, where these systems may benefit from the ability to detect and estimate vehicle loads via weigh-in-motion (WIM) sensing without interrupting the traffic flow. However, off-the-shelf applications of WIM can be improved in terms of cost and durability, both on the hardware and software sides. This study proposes a novel multi-functional pavement material that can be utilized as a pavement embedded weigh-in-motion system. The material consists of a composite fabricated using an eco-friendly synthetic binder material called EVIzero, doped with carbon microfiber inclusions. The composite material is piezoresistive and, therefore, has strain-sensing capabilities. Compared to other existing strain-sensing structural materials, it is not affected by polarization and exhibits a more rapid response time. The study evaluates the monitoring capabilities of the novel composite according to the needs of a WIM system. A tailored data acquisition setup with distributed line electrodes is developed for the detection of moving loads. The aim of the paper is to demonstrate the sensing capabilities of the newly proposed composite pavement material and the suitability of the proposed monitoring system for traffic detection and WIM. Results demonstrate that the material is promising in terms of sensing and ready to be implemented in the field for further validation in the real world.

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Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses

Meixedo

Ribeiro

Santos

et al. 2021

Structural Integrity

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This chapter addresses unsupervised damage detection in railway bridges by presenting a novel AI-based SHM strategy using traffic-induced dynamic responses. To achieve this goal a hybrid combination of wavelets, PCA and cluster analysis is implemented. Damage-sensitive features from train-induced dynamic responses are extracted and allow taking advantage not only of the repeatability of the loading, but also, of its large magnitude, thus enhancing sensitivity to small-magnitude structural changes. The effectiveness of the proposed methodology is validated in a long-span bowstring-arch railway bridge with a permanent structural monitoring system installed. A digital twin of the bridge was used, along with experimental values of temperature, noise, trains loadings and speeds, to realistically simulate baseline and damage scenarios. The methodology proved highly sensitive in detecting early damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. The ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by environmental and operational variations, is achieved by carefully defining the modelling and fusion sequence of the information. A damage detection strategy capable of characterizing multi-sensor data while being sensitive to identify local changes, is proposed as a tool for real-time structural assessment of bridges without interfering with the normal service condition.

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Bridge Weigh-in-Motion system for the identification of train loads using fiber-optic technology

Cited by 35 publications

References 41 publications

Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads

Train Classification Using a Weigh-in-Motion System and Associated Algorithms to Determine Fatigue Loads

Innovative Carbon-Doped Composite Pavements with Sensing Capability and Low Environmental Impact for Multifunctional Infrastructures

Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responses

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