Prediction of mechanical properties of rail pads under in-service conditions through machine learning algorithms

Ferreño, Diego; Sainz-Aja, José; Carrascal, I.; Cuartas, Miguel; Pombo, João; Casado, Julio; Diego, Soraya

doi:10.1016/j.advengsoft.2020.102927

Cited by 29 publications

(14 citation statements)

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“…Alternatively, a friendly application accessible for any user could be developed. This approach has been used by one of the authors of this paper in a recent contribution [30] where an application was created on the Microsoft.Net platform.…”

Section: Discussionmentioning

confidence: 99%

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

Ferreño

Serrano

Kirk

et al. 2022

Metals

Self Cite

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The long-term operating strategy of nuclear plants must ensure the integrity of the vessel, which is subjected to neutron irradiation, causing its embrittlement over time. Embrittlement trend curves used to predict the dependence of the Charpy transition-temperature shift, ΔT41J, with neutron fluence, such as the one adopted in ASTM E900-15, are empirical or semi-empirical formulas based on parameters that characterize irradiation conditions (neutron fluence, flux and temperature), the chemical composition of the steel (copper, nickel, phosphorus and manganese), and the product type (plates, forgings, welds, or so-called standard reference materials (SRMs)). The ASTM (American Society for Testing and Materials) E900-15 trend curve was obtained as a combination of physical and phenomenological models with free parameters fitted using the available surveillance data from nuclear power plants. These data, collected to support ASTM’s E900 effort, open the way to an alternative, purely data-driven approach using machine learning algorithms. In this study, the ASTM PLOTTER database that was used to inform the ASTM E900-15 fit has been employed to train and validate a number of machine learning regression models (multilinear, k-nearest neighbors, decision trees, support vector machines, random forest, AdaBoost, gradient boosting, XGB, and multi-layer perceptron). Optimal results were obtained with gradient boosting, which provided a value of R2 = 0.91 and a root mean squared error ≈10.5 °C for the test dataset. These results outperform the prediction ability of existing trend curves, including ASTM E900-15, reducing the prediction uncertainty by ≈20%. In addition, impurity-based and permutation-based feature importance algorithms were used to identify the variables that most influence ΔT41J (copper, fluence, nickel and temperature, in this order), and individual conditional expectation and interaction plots were used to estimate the specific influence of each of the features.

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Section: Discussionmentioning

confidence: 99%

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

Ferreño

Serrano

Kirk

et al. 2022

Metals

Self Cite

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“…Still, without much programming intervention, Microsoft developed a module in Power BI reporting tool called Influencer Visual based on the ML.NET framework, a predictive machine learning model using the Gradient Boosting algorithm called MART [25]. The algorithm employed was an efficient implementation and show promising results in the mechanical prediction [26] and error compensation [27]. Based on the Google Scholar search, the application of ML.NET into the customer service industry for customer intent prediction is not available.…”

Section: ) Customer Intent Predictionmentioning

confidence: 99%

Customer Intent Prediction using Sentiment Analysis Techniques

Lye

Teh

2021

2021 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applicat

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Analysing the voice of the customer (VoC) through the customer intent has many applications ranging from personalised marketing to behaviour study. Individuals express their feelings in a language that is frequently accompanied by ambiguity and figure of speech, making it difficult even for humans to understand. Customer feedback is crucial as part of the customer experience (CX) management in customer retention and improves the sales strategy. Modern research has been using machine learning and word embedding technique for sentiment analysis, and it is focused on the predictive model without further context. In this study, the customer feedback comes in the form of Net Promoter Score (NPS) with a text box for written feedback. We analyse the data and demonstrate a hybrid representation that has resulted in the accuracy improvement of the sentiment classification task and predicting customer intent. The datasets were first trained using Word2Vec with the previous dataset and then fit into the Random Forest classifier, tested as the best configuration to prevent overfitting. The hybrid representation is compared against the baseline sentiment polarity tool through few experiments; the results have shown that the hybrid model has improved accuracy for the sentiment classification task. Lastly, we performed customer intent prediction by using the Power BI influencer module. The outcome of the result can be used as a reference for IT management in decision making.

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“…In recent years, machine learning methods have had many applications in track defect identification and prediction. Machine learning methods were used to detect defects of important components of the track structure that are directly related to safe train operation, including the defects of rails [39,40], fasteners [41][42][43][44], rail pads [45], and turnout [46]. The classification of three types of rail defects-surface defect, cross level defect and depression in track profiles-by track geometry data on the basis of logistic regression and decision tree [39] and the classification of rail crack by acoustic emission waves on the basis of a multibranch convolutional neural network (CNN) [40] were explored.…”

Section: Introductionmentioning

confidence: 99%

“…The rail fastener defects were detected from images on the basis of a CNN [41], generative adversarial network, residual network [42], point cloud deep learning [43], and Faster region-CNN [44]. The dynamic stiffness of rail pads was predicted using several machine learning methodologies (multilinear regression, K nearest neighbors, regression tree, random forest, gradient boosting, multilayer perceptron, and support vector machine (SVM)) [45]. The fault detection approach for the HSR turnout based on a deep denoising auto-encoder and one-class SVM [46] was also proposed.…”

Section: Introductionmentioning

confidence: 99%

Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

Guo

Cui

2021

Applied Sciences

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High-speed railways (HSRs) are established all over the world owing to their advantages of high speed, ride comfort, and low vibration and noise. A ballastless track slab is a crucial part of the HSR, and its working condition directly affects the safe operation of the train. With increasing train operation time, track slabs suffer from various defects such as track slab warping and arching as well as interlayer disengagement defect. These defects will eventually lead to the deformation of track slabs and thus jeopardize safe train operation. Therefore, it is important to monitor the condition of ballastless track slabs and identify their defects. This paper proposes a method for monitoring track slab deformation using fiber optic sensing technology and an intelligent method for identifying track slab deformation using the random-forest model. The results show that track-side monitoring can effectively capture the vibration signals caused by train vibration, track slab deformation, noise, and environmental vibration. The proposed intelligent algorithm can identify track slab deformation effectively, and the recognition rate can reach 96.09%. This paper provides new methods for track slab deformation monitoring and intelligent identification.

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Prediction of mechanical properties of rail pads under in-service conditions through machine learning algorithms

Cited by 29 publications

References 50 publications

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

Customer Intent Prediction using Sentiment Analysis Techniques

Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring

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