A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Hong, Sunguk; Park, CheolJeong; Cho, Seong Jin

doi:10.3390/s21134606

Cited by 15 publications

(8 citation statements)

References 41 publications

(75 reference statements)

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“…A least-squares method trendline was calculated with an intercept of 1.04 and slope of 0.958, close to the 0 intercept and 1 slope of the testing set, showing that despite its scatter, the trend of the predicted data is very close to that of the testing set. Radiant heating typically raises rail temperature significantly above ambient, leading to the consideration of very high temperatures here ( 25 ).…”

Section: Resultsmentioning

confidence: 99%

Fuzzy Inference Model for Railway Track Buckling Prediction

Słodczyk,

Fletcher,

Gitman

et al. 2023

Transportation Research Record: Journal of the Transportation R

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The application of rail buckling models is often limited by uncertain information with respect to track properties, and many conventional models are poorly suited to network-wide or even regional application. Here, a methodology using fuzzy sets is presented that, when trained using buckling data can use inputs of track properties to predict the minimum buckling temperature increase for a particular track. An investigation of the impact of the size of training data and the influence of key track parameters on the minimum buckling temperature increase was conducted, and it was found that a high level of influence stems from the sleeper spacing and fastener torsional resistance parameters. The model was shown to give a low prediction error even for small dataset sizes of training data. The results of this work show the efficacy of a fuzzy sets based model when applied to track buckling prediction data, giving both a low error and rapid calculation times. The approach has potential for application for a wider array of variables, such as track geometry and vehicle dynamics, and is not limited to the study of track buckling owing to the flexibility of the fuzzy inference methodology.

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Section: Resultsmentioning

confidence: 99%

Fuzzy Inference Model for Railway Track Buckling Prediction

Słodczyk,

Fletcher,

Gitman

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…ANNs take on some of the most complex tasks thanks to their extreme flexibility. Such tasks include object tracking and recognition, natural language processing, and regression [ 38 , 39 , 40 , 41 , 42 ]. They are loosely based on the human brain and learn via the backpropagation algorithm, allowing for each individual neuron’s error to propagate through the structure.…”

Section: Machine Learning: Preprocessingmentioning

confidence: 99%

Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test

Belding

Enshaeian

Rizzo

2022

Sensors

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This paper describes a non-invasive inspection technique for the estimation of longitudinal stress in continuous welded rails (CWR) to infer the rail neutral temperature (RNT), i.e., the temperature at which the net longitudinal force in the rail is zero. The technique is based on the use of finite element method (FEM), vibration measurements, and machine learning (ML). FEM is used to model the relationship between the boundary conditions and the longitudinal stress of any given CWR to the vibration characteristics (mode shapes and frequencies) of the rail. The results of the numerical analysis are used to train a ML algorithm that is then tested using field data obtained by an array of accelerometers polled on the track of interest. In the study presented in this article, the proposed technique was proven in the field during an experimental campaign conducted in Colorado. A commercial FEM software was used to model the rail track as a short rail segment repeated indefinitely and under varying boundary conditions and stress. Three datasets were prepared and fed to ML models developed using hyperparameter search optimization techniques and k-fold cross validation to infer the stress or the RNT. The frequencies of vibration were extracted from the time waveforms obtained from two accelerometers temporarily attached to the rail. The results of the experiments demonstrated that the success of the technique is dependent on the accuracy of the model and the ability to properly identify the modeshapes. The results also proved that the ML was also able to predict successfully the neutral temperature of the tested rail by using only a limited number of experimental data for the training.

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“…However, modern tools used to determine the state of an object are based on principles developed to determine the first limit state, which determines the general suitability of an object to work in certain conditions [58][59][60]. The strength [61][62][63] and stability [64][65][66][67] indicators used to evaluate this state are obtained by numerical modeling to simulate quasi-dynamic deformation processes [68][69][70][71] occurring inside the elements under the influence of external [72][73][74] and climatic impulses [75][76][77].…”

Section: Introductionmentioning

confidence: 99%

Using Detailing Concept to Assess Railway Functional Safety

Bondarenko¹,

Campisi

Tesoriere

et al. 2022

Sustainability

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The ability to assess the risks of the functional safety of railway tracks allows harmonizing characteristics of track elements under certain operating conditions under certain maintenance for the efficient use of the track structure throughout its life cycle. The concept of detailing conditions of the interaction of the rolling stock and railway track was used for the productive solution of tasks of infrastructure functional safety assessment. The paper formed an approach to the analytical solution of determination problems of deformability parameters over time using the elastic waves theory. The formation method of interconnections between the technical and economic aspects of the operation of railway infrastructure was proposed. The criteria of deformability work and intensity of use were utilized for the effective use of the track structure through its life cycle. The results of calculations are presented to assess changes in the deformability behaviour of the track elements and structure when the force and speed parameters of the operating conditions change, as well as the algorithm of the method for estimating the operation deformability of the railway track. Thus, the proposed approach can be adapted to optimize objects by railway functional safety assessment at the stage of object operation simulation.

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A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Cited by 15 publications

References 41 publications

Fuzzy Inference Model for Railway Track Buckling Prediction

Fuzzy Inference Model for Railway Track Buckling Prediction

Vibration-Based Approach to Measure Rail Stress: Modeling and First Field Test

Using Detailing Concept to Assess Railway Functional Safety

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