Experimental and numerical investigation of the performance of self-sensing concrete sleepers

Xu, Jinlong; Butler, Lynda L.; Elshafie, Mohammed

doi:10.1177/1475921719834506

Cited by 24 publications

(14 citation statements)

References 18 publications

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“…Xu et al [ 190 ] studied prestressed concrete sleepers enriched with fiber-optic-sensor –based systems, which were called self-sensing sleepers. The authors conducted laboratory tests in order to develop an estimation of rail seat load, detection of cracking, and identification of ballast settlement—all with the use of self-sensing sleepers.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

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Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

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“…The data for the statistical models were acquired using DIC track-side measurements. Another recent techniques for the sleeper support condition estimation are based structural health monitoring (SHM) technologies such as smart or self-sensing sleepers as shown in the studies [45,46]. The information collected using embedded fiber Bragg grating sensing for tension measurements or acceleration sensors.…”

Section: Review Of the Literaturementioning

confidence: 99%

Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

Sysyn¹,

Przybyłowicz²,

Nabochenko³

et al. 2021

Preprint

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Unsupported sleepers or void zones in ballasted tracks are one of the most recent and frequent track failures. The void failures have the property of intensive development that, without timely maintenance measures, can cause the appearance of cost-expensive local instabilities like subgrade damages. The reason of the intensive void development lies in the mechanics of the sleeper and ballast bed interaction. The particularity of the interaction is a dynamic impact that occur due to void closure. Additionally, void zones cause inhomogeneous ballast pressure distribution between the void zone and fully supported neighbour zones. The present paper is devoted studying the mechanism of the sleeper-ballast dynamic impact in the void zone. The results of experimental in-situ measurements of rail deflections showed the significant impact accelerations in the zone even for light-weight slow vehicles. A simple 3-beam numerical model of track and rolling stock interaction has shown the similar to the experimental measurements dynamic interaction. Moreover, the model shows that the sleeper accelerations are more than 3 times higher than the corresponding wheel accelerations and the impact point appear before the wheel enters the impact point. The analysis of ballast loadings shows the specific impact behaviour in combination with the quasistatic part that is different for void and neighbour zones, which are characterised with high ballast pre-stressed conditions. The analysis of void sizes influence demonstrate that the impact loadings, wheel and sleeper maximal accelerations appear at certain void depth after which the values decrease. The ballast quasistatic loading analysis indicates more than twice increase of the ballast loading in neighbour zones for long voids and almost full quasistatic unloading for short length voids. However, the used imitation model cannot explain the nature of the dynamic impact. The mechanism of the void impact is clearly explained by the analytic solution using a simple clamped beam. A simplified analytical expression of the void impact velocity shows that it is linearly related to the wheel speed and loading. The comparison to the numerically simulated impact velocities shows a good agreement and the existence of the void depth with the maximal impact. An estimation of the long-term influences for the cases of normal sleeper loading, high ballast pre-stress and quasistatic loading in the neighbour zones and high impact inside the void are performed.

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“…A schematic of the sensor network instrumented on the sleeper beam is shown in Figure 1. A more detailed specification of the instrumented sleepers considered in this study is given in Xu et al (2019). For illustrative purposes only, Figure 2 shows a 7.52 second representative data set of strain measurements from an FBG (located at x = 2000 along the top of an instrumented sleeper).…”

Section: Data From Instrumented Structurementioning

confidence: 99%

“…These are shown for X f1 (left), X f2 (middle) and X f3 (right). strand embedded in the sleeper during the testing procedure; a large increase at strain between 1100 and 1150 seconds was reported to likely be because of cracking along the top of the sleeper (Xu et al, 2019). The Gaussian process model is trained on (i.e.…”

Section: Experimental Curvature Datamentioning

confidence: 99%

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

Gregory

Lau

Girolami

et al. 2019

Journal of Computational Physics

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At the heart of structural engineering research is the use of data obtained from physical structures such as bridges, viaducts and buildings. These data can represent how the structure responds to various stimuli over time when in operation. Many models have been proposed in literature to represent such data, such as linear statistical models. Based upon these models, the health of the structure is reasoned about, e.g. through damage indices, changes in likelihood and statistical parameter estimates. On the other hand, physicsbased models are typically used when designing structures to predict how the structure will respond to operational stimuli. These models represent how the structure responds to stimuli under idealised conditions. What remains unclear in the literature is how to combine the observed data with information from the idealised physics-based model into a model that describes the responses of the operational structure. This paper introduces a new approach which fuses together observed data from a physical structure during operation and information from a mathematical model. The observed data are combined with data simulated from the physics-based model using a multi-output Gaussian process formulation. The novelty of this method is how the information from observed data and the physics-based model is balanced to obtain a representative model of the structures response to stimuli. We present our method using data obtained from a fibre-optic sensor network installed on experimental railway sleepers. The curvature of the sleeper at sensor and also non-sensor locations is modelled, guided by the mathematical representation. We discuss how this approach can be used to reason about changes in the structures behaviour over time using simulations and experimental data. The results show that the methodology can accurately detect such changes. They also indicate that the methodology can infer information about changes in the parameters within the physics-based model, including those governing components of the structure not measured directly by sensors such as the ballast foundation.

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Experimental and numerical investigation of the performance of self-sensing concrete sleepers

Cited by 24 publications

References 18 publications

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Mechanism of the Sleeper-Ballast Dynamic Impact in Void Zones

The synthesis of data from instrumented structures and physics-based models via Gaussian processes

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