Risk Identification Method for High-Speed Railway Track Based on Track Quality Index and Time-Optimal Degree
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 8, Issue 2
Abstract
The real-time identification and early warning of the state of the track are significant to keep the high-speed railway (HSR) safe, stable, and comfortable. This paper proposes a new risk identification method based on the time-ordered weighted averaging operator and track quality index (TOWA-TQI) by using the time-weighted vector to aggregate time-optimal information of objects. Meanwhile, an energy coefficient is introduced to make the weighted track irregularity data as the same energy as the original track irregularity data that can control the weighted amplitude at the same level. To quickly classify the objects, this paper proposes a center and radius clustering (C-R clustering) method that can classify the points into different categories by judging that the distance from the central point is less than the corresponding radius. Moreover, the specific location is located by labeling the categories. Lastly, a practical case is carried out to verify that the proposed method is more accurate and effective.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request, including code of the case.
Acknowledgments
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this paper: This research was supported by National Natural Science Foundation of China (No. 51975038), Natural Science Foundation of Beijing Municipal (No. KZ202010016025), Fundamental Research Funds for Beijing University of Civil Engineering and Architecture (Nos. X18027 and X19022), and the BUCEA Doctor Graduate Scientific Research Ability Improvement Project. The authors gratefully acknowledge the support.
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Received: May 12, 2021
Accepted: Oct 29, 2021
Published online: Jan 17, 2022
Published in print: Jun 1, 2022
Discussion open until: Jun 17, 2022
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