Abstract
Peak management and mean management are common ways to manage the quality of high-speed railway tracks at present. The most popular method for evaluating such tracks is the track quality index (TQI) method, which can reflect the overall state of the equipment to a certain extent. However, this method is likely to ignore some potential risks that threaten the operation of a high-speed train. For more effective risk identification, an incentive factor-based dynamic comprehensive evaluation (DCE) method was introduced to assess the geometric parameters of a high-speed railway track. Moreover, the weights of geometric parameters were computed by a combination of the analytic hierarchy process (AHP) and entropy based on the correlation coefficient. The proposed method can highlight the sensitivity index of the geometric parameters, which is an advantage over the TQI method. A case study of a high-speed railway track was performed using the two methods, and the results were verified with the original data. It was found that the TQI method identified only one obvious risk while the proposed method identified one obvious risk and two potential risks. This suggests that the proposed method is more accurate in identifying the risky sections than the TQI method.
Highlights
With the rapid development of high-speed railway technology, the dynamic performance requirements for high-speed trains are becoming increasingly demanding due to the increase in vehicle speed
This method evaluates the average quality of track segments based on statistical characteristic value, and it is used as the key index to evaluate the state of track geometry [8], This paper describes the track quality index (TQI)
TQI is calculated as the ratio of the traced space curve length to the track segment length in the U.S [10]
Summary
With the rapid development of high-speed railway technology, the dynamic performance requirements for high-speed trains are becoming increasingly demanding due to the increase in vehicle speed. A CNN-LSTM (the combination of the convolutional neural network and short-term memory) model was proposed to predict vehicle-body vibration, which was helpful in locating potential track geometry defects [11] These evaluation methods can provide better qualitative results, they remain unable to adequately describe the change in service quality of track based on a single physical quantity. The TQI [30,31] is the primary method for comprehensive evaluation of high-speed railway lines This method evaluates the average quality of track segments based on statistical characteristic value, and it is used as the key index to evaluate the state of track geometry [8], This paper describes the TQI method in detail in the third section. Conclusions and future work are discussed in the fifth section
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