Prediction of track geometry degradation using artificial neural network: a case study

Khajehei, Hamid; Ahmadi, Alireza; Soleimanmeigouni, Iman; Haddadzade, Mohammad; Nissen, Arne; Jebelli, Mohammad Javad Latifi

doi:10.1080/23248378.2021.1875065

Cited by 43 publications

(14 citation statements)

References 35 publications

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“…However, the authors in Lee et al [ 92 ] think the environmental factors are less important and consider the subgrade type and maintenance parameters (the number of compactions on ballast and initial TQI) instead. In the work of Khajehei et al [ 93 ], the authors used the Garson algorithm to calculate the relative importance of input variables, as shown in Table 5 , and found that the maintenance record, track degradation rate after tamping, and train load are more relevant to prediction accuracy than other variables.…”

Section: Prediction Methods Based On Machine Learningmentioning

confidence: 99%

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Liao

Liu

Wang³

et al. 2022

Sensors

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Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid development in computer science, many prediction models using machine learning methods have been developed. It is possible to discover the degradation pattern and develop accurate prediction models. The paper reviews the existing prediction methods for railway track degradation, including traditional methods and prediction methods based on machine learning methods, including probabilistic methods, Artificial Neural Network (ANN), Support Vector Machine (SVM), and Grey Model (GM). The advantages, shortage, and applicability of methods are discussed, and recommendations for further research are provided.

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Section: Prediction Methods Based On Machine Learningmentioning

confidence: 99%

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Liao

Liu

Wang³

et al. 2022

Sensors

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show abstract

“…Additionally, the temporal frequency of our method is higher than theirs. Khajehei et al 16 used ANN to predict the degradation rate of SD LL . They fed a variety of exogenous data into the model.…”

Section: Related Workmentioning

confidence: 99%

Spatiotemporal forecasting of vertical track alignment with exogenous factors

Kosukegawa

Mori

Suyari

et al. 2023

Sci Rep

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To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, a method is proposed to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory. The proposed method is also experimentally compared with other methods in terms of the forecasting performance. Additionally, an ablation study on exogenous factors is conducted to examine their individual contributions to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of vertical alignment.

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“…Such freight cars include depressed-center flat cars, long-large flat cars, and well-hole cars. Exceptional heavy-duty railway freight cars exceeding the railway gauge in transport may endanger traffic safety on adjacent lines and even cause train derailment accidents [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…Drones 2022, 6, x FOR PEER REVIEW 2 of 21 freight cars exceeding the railway gauge in transport may endanger traffic safety on adjacent lines and even cause train derailment accidents [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

Zhou

Gong

et al. 2022

Drones

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Identifying and detecting the loading size of heavy-duty railway freight cars is crucial in modern railway freight transportation. Due to contactless and high-precision characteristics, light detection and ranging-assisted unmanned aerial vehicle stereo vision detection is significant for ensuring out-of-gauge freight transportation security. However, the precision of unmanned aerial vehicle flight altitude control and feature point mismatch significantly impact stereo matching, thus affecting the accuracy of railway freight measurement. In this regard, the altitude holding control strategy equipped with a laser sensor and SURF_rBRIEF image feature extraction and matching algorithm are proposed in this article for railway freight car loading size measurement. Moreover, an image segmentation technique is used to quickly locate and dismantle critical parts of freight cars to achieve a rapid 2-dimension reconstruction of freight car contours and out-of-gauge detection. The robustness of stereo matching has been demonstrated by external field experiment. The precision analysis and fast out-of-gauge judgment confirm the measurement accuracy and applicability.

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Prediction of track geometry degradation using artificial neural network: a case study

Cited by 43 publications

References 35 publications

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review

Spatiotemporal forecasting of vertical track alignment with exogenous factors

LiDAR-Assisted UAV Stereo Vision Detection in Railway Freight Transport Measurement

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