Robust fibre optic sensor arrays for monitoring early-age performance of mass-produced concrete sleepers

Butler, Lynda L.; Xu, Jinlong; He, Ping; Gibbons, Niamh; Dirar, Samir; Middleton, Campbell; Elshafie, Mohammed

doi:10.1177/1475921717714615

Cited by 19 publications

(11 citation statements)

References 8 publications

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“…The research significance of this article is twofold in that it demonstrates a robust solution for long-term field monitoring of rail infrastructure using self-sensing sleepers, and it discusses the various rail track bed condition information that could potentially be acquired by self-sensing sleepers. This work also complements two previous studies 15,17 and will be used to inform future research where self-sensing sleepers installed on ballasted track on railway bridges will be used to monitor long-term performance of the track bed system.…”

Section: Introductionmentioning

confidence: 63%

“…A detailed discussion of the manufacturing process, early age behaviour, and results of qualification bending tests of these self-sensing sleepers was reported previously. 17 The self-sensing sleeper tested in this study is part of a larger batch of self-sensing sleepers which were selected for installation within a live railway network. Due to the low signal transmission loss of an optical fibre, individual self-sensing sleepers can be connected to a much larger monitoring system covering many kilometres of rail track.…”

Section: Self-sensing Sleeper Instrumentation and Specificationsmentioning

confidence: 99%

“…This second stage of loading was also intended to impose damage in the form of cracking and differential settlement between the rail seats and the mid-span of the self-sensing sleeper. Based on experimental results from positive bending qualification tests carried out in a previous study, 17 significant cracking was expected to occur in the concrete sleeper at loads above 225 kN (rail seat load). After the second stage of loading, the actuator force was decreased to 0 kN to complete the first load cycle.…”

Section: Full-scale Self-sensing Sleeper Performance Testmentioning

confidence: 99%

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

Butler

Elshafie

2019

Structural Health Monitoring

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Prestressed concrete sleepers with built-in fibre optic–based sensing systems have recently been developed to capture performance data within railway networks and to provide critical decision-support information to route managers and operators. To better understand how self-sensing sleepers can be fully utilized within the rail network, a study of their comprehensive performance under controlled conditions must be undertaken. This article presents the results of the full-scale laboratory testing of a self-sensing sleeper supported on ballast. A primary focus of this study was to investigate whether a self-sensing sleeper could also be used to estimate rail seat load, detect cracking, and identify differential ballast settlement. The ultimate capacity and resilience of the embedded fiber Bragg grating sensing system was tested by applying load up until concrete cracking followed by several cyclic load cycles. Through inference of the load versus strain response, the ability of the self-sensing sleeper to detect damage (concrete cracking and loss of ballast support) was evaluated. The experimental results revealed the effectiveness and robustness of the embedded sensing system to continue to provide reliable dynamic strain measurements well beyond the ultimate loading capacity of the prestressed sleeper. Cracking of the top surface of the sleeper was effectively detected by the fiber Bragg grating strain sensors at the mid-span section. After cracking, subsequent load cycles were carried out. During this period, the bottom fiber Bragg grating measurements captured the effects of differential ballast settlement under the rail seats. A three-dimensional nonlinear finite element model was developed to simulate the experimental test setup and to investigate the relation between fiber Bragg grating sensor measurements and rail track response. The combined experimental and numerical results suggest that a self-sensing sleeper may be deployed on an operational railway to provide reliable and long-term measurements of rail axle load and ballast pressure.

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Section: Introductionmentioning

confidence: 63%

Section: Self-sensing Sleeper Instrumentation and Specificationsmentioning

confidence: 99%

Section: Full-scale Self-sensing Sleeper Performance Testmentioning

confidence: 99%

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

Butler

Elshafie

2019

Structural Health Monitoring

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“…Introduction. Modern civil engineering is increasingly leveraging sensor technology to understand and monitor physical assets, such as bridges and pipe networks (Butler et al, 2016a(Butler et al, , 2018. This sensor technology is becoming cheaper to deploy and hence more widely used.…”

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confidence: 99%

Unsupervised streaming anomaly detection for instrumented infrastructure

2021

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SHM) often involves instrumenting structures with distributed sensor networks. These networks typically provide high frequency data describing the spatio-temporal behaviour of the assets. A main objective of SHM is to reason about changes in structures' behaviour using sensor data. We construct a streaming anomaly detection method for data from a railway bridge instrumented with a fibre-optic sensor network. The data exhibits trend over time, which may be partially attributable to environmental factors, calling for temporally adaptive estimation. Exploiting a latent structure present in the data motivates a quantity of interest for anomaly detection. This quantity is estimated sequentially and adaptively using a new formulation of streaming Principal Component Analysis. Anomaly detection for this quantity is then provided using Conformal Prediction. Like all streaming methods, the proposed method has free control parameters which are set using simulations based on bridge data. Experiments demonstrate that this method can operate at the sampling frequency of the data while providing accurate tracking of the target quantity. Further, the anomaly detection is able to detect train passage events. Finally the method reveals a previously unreported cyclic structure present in the data.

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“…Introduction Estimating the spatial distributions of mechanical properties in a heterogeneous solid body from the measurements of strains or displacements fields has wide ranging applications, including material characterisation, medical diagnosis and civil infrastructure monitoring. For example, there has been a recent burst of activity in the development of smart civil infrastructure and this includes application of strain measurement technologies like fibre optic sensing to monitor the structural health of tunnels (Gue et al, 2015), bridges (Ko and Ni, 2005) and concrete sleepers (Butler et al, 2017) to name a few. In these applications strains are measured at a small number of discrete locations.…”

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confidence: 99%

Bayesian inference of the spatial distributions of material properties

Vigliotti

Csänyi

Deshpande

2018

Journal of the Mechanics and Physics of Solids

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The inverse problem of estimating the spatial distributions of elastic material properties from noisy strain measurements is ill-posed. However, it is still typically treated as an optimisation problem to maximise a likelihood function that measures the agreement between the measured and theoretically predicted strains. Here we propose an alternative approach employing Bayesian inference with Nested Sampling used to explore parameter space and compute Bayesian evidence. This approach not only aids in identifying the basis function set (referred to here as a model) that best describes the spatial material property distribution but also allows us to estimate the uncertainty in the predictions. Increasingly complex models with more parameters generate very high likelihood solutions and thus are favoured by a maximum likelihood approach. However, these models give poor predictions of the material property distributions with a large associated uncertainty as they overfit the noisy data. On the other hand, the Bayes' factor peaks for a relatively simple model and indicates that this model is most appropriate even though its likelihood is comparatively low. Intriguingly, even for the appropriate model that has a unique maximum likelihood solution, the measurement noise is amplified to give large errors in the predictions of the maximum likelihood solution. By contrast, the mean of the posterior probability distribution reduces the effect of noise in the data and predicts the material properties with significantly higher fidelity. Simpler model selection criteria such as the Bayesian information criterion are shown to fail due to the non-Gaussian nature of the posterior distribution of the parameters. This makes accurate evaluation of the posterior distribution and the associated Bayesian evidence integral (by Nested Sampling or other means) imperative for this class of problems. The output of the Nested Sampling algorithm is also used to construct likelihood landscapes. These landscapes show the existence of multiple likelihood maxima when there is paucity of data and/or for overly complex models. They thus graphically illustrate the pitfalls in using optimisation methods to search for maximum likelihood solutions in such inverse problems.

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Robust fibre optic sensor arrays for monitoring early-age performance of mass-produced concrete sleepers

Cited by 19 publications

References 8 publications

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

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

Unsupervised streaming anomaly detection for instrumented infrastructure

Bayesian inference of the spatial distributions of material properties

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