Iterative Optimization Adjustment Method for Ballastless Track Irregularity of High-Speed Railway
Publication: Journal of Surveying Engineering
Volume 148, Issue 4
Abstract
Track irregularities generate train vibrations. The precise adjustment of a track after locking a long rail and dispersing its stress during railway operation and maintenance are crucial to ensure high-speed, stable, and safe train operation. Currently, the main method for rail adjustment utilizes the supporting software of a trolley to formulate an adjustment solution under the manual mode. Depending on the requirements of track irregularities, the deviations are modified in real time, and track irregularities are fed back by postadjustment deviations. Because the manual mode of adjustment is considerably influenced by human factors, the process is inefficient, time-consuming, and laborious. Accordingly, this paper proposed an iterative optimization adjustment method for ballastless track irregularity of railway (IOABLT). IOABLT utilizes feedback of the irregularity of adjusted rail alignments (obtained by the simplex method) of all segment units. This enables the method to modify the constraint boundary values and iteratively solve the corresponding adjustment model of the track. Then, the minimum and second minimum data sets of track irregularities are derived, and the total optimal solution of the adjustment model is determined based on the difference in the deviations of adjacent points and the change rate of the difference. The experimental results showed that this proposed method can automatically and efficiently achieve optimized rail adjustment under complex constraints. Further, it eliminates the influence of human subjective factors in formulating the precise track adjustment solution and attains optimal track regularity within the allowable adjustment range of the fastening system.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request (the vertical deviations of the rail).
Acknowledgments
The present work has been supported by the Project funded by China Postdoctoral Science Foundation (2019M663555).
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Received: Nov 27, 2021
Accepted: May 24, 2022
Published online: Aug 8, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 8, 2023
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