Track Quality Index for High Speed Track

Fazio, Alfred E; Corbin, John

doi:10.1061/(asce)0733-947x(1986)112:1(46)

Cited by 14 publications

(2 citation statements)

References 1 publication

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“…Track Quality Index (TQI) [25] is a comprehensive index and assessment system that uses quantitative statistics to characterise the track's overall quality. It is the total of the standard deviations of the track irregularity in the vertical and horizontal direction, gauge, and twist irregularities elements.…”

Section: Track Quality Indexmentioning

confidence: 99%

The Analysis of High-Speed Railway Seismic-induced Track Geometric Irregularity

Li,

Yang,

Gao

et al. 2024

ACTA POLYTECH HUNG

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The high-speed railway in China passes through various seismic zones. As the track geometric irregularity is a critical aspect impacting train operation safety and traininduced vibration, it is meaningful to investigate the influence of earthquakes on the seismic-induced track geometric irregularity of high-speed railways. To determine the impact of earthquakes on track conditions, a complete study was conducted using various earthquake magnitude levels (3.0 to 7.0), varied limiting train speeds, and different track structural types (ballasted and ballastless track). The track quality index, long-wave irregularities, 10 m chord measurement, and vehicle vibration are analysed to suggest the change of track geometric irregularity and its influence after the earthquake. At the same time, the vehicle-track analysis model is used to calculate the difference in vehicle vibration under different seismic-induced track conditions. The vehicle acceleration, the rate of wheel load reduction, derailment coefficient indicators are investigated. These results imply that earthquakes have an impact on high-speed railways, causing track geometric irregularities to shift, which may contribute to increased vibration while trains are running. Compared to the ballasted track, whose normal speed is 250 km/h, the ballastless track, whose nominal speed is 350 km/h, was affected by the earthquake less. According to data, earthquakes have an influence on long waves greater than 1 m. While small and moderate earthquakes have a minor effect on railway safety, they do have an effect on train operating comfort because vehicle vibration is amplified as a result of the seismic-induced track geometric irregularity being worse.

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Section: Track Quality Indexmentioning

confidence: 99%

The Analysis of High-Speed Railway Seismic-induced Track Geometric Irregularity

Li,

Yang,

Gao

et al. 2024

ACTA POLYTECH HUNG

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“…Such a track geometry standard provides a convenient way to evaluate track quality. However, a track quality evaluation index should be derived from vehicle response in order for it to be ''performance-based'' [32]. Therefore, this paper advocates to take the VBA predicted by CNN-LSTM as an additional evaluation index.…”

Section: E Comparative Analysismentioning

confidence: 99%

Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Gao

Liu

et al. 2019

IEEE Access

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Track quality evaluation is fundamental for track maintenance. Around the world, track geometry standards are established to evaluate track quality. However, these standards may not be capable of detecting some abnormal track geometry conditions that can cause considerable vehicle-body vibration. And people gradually realized that track quality evaluation should be based not only on track geometry but also on vehicle performance. Vehicle-body vibration prediction is beneficial for locating potential track geometry defects, and the predicted accelerations can be used as an auxiliary index for assessing track quality. For this purpose, this paper gives a method to predict vehicle-body vibration based on deep learning, which represents one of the newest areas in artificial intelligence. By integrating convolutional neural network (CNN) and long short-term memory (LSTM), a CNN-LSTM model is proposed to make accurate and point-wise prediction. To achieve the optimal performance and explore the internal mechanism of the model, structural configurations and inner states are extensively studied. CNN-LSTM can take advantage of the powerful feature extraction capacity of CNN and LSTM, and outperforms the fully-connected neural network and the plain LSTM on the experimental data of a high-speed railway. In detail, CNN-LSTM has superior performance in predicting vertical vehicle-body vibration below 10 Hz and lateral vehicle-body vibration below 1 Hz. Moreover, analysis shows that the predicted vehicle-body acceleration can act as a performance-based evaluation index of track quality.

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Analysis and Modelling of the Track Quality Index of Railways

Laubscher

Jooste

2023

Lecture Notes in Production Engineering

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Track Quality Index for High Speed Track

Cited by 14 publications

References 1 publication

The Analysis of High-Speed Railway Seismic-induced Track Geometric Irregularity

The Analysis of High-Speed Railway Seismic-induced Track Geometric Irregularity

Deep Learning for Track Quality Evaluation of High-Speed Railway Based on Vehicle-Body Vibration Prediction

Analysis and Modelling of the Track Quality Index of Railways

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