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http://dx.doi.org/10.7843/kgs.2020.36.3.5

Long-term Settlement Prediction of Railway Concrete Track Based on Recurrent Neural Network (RNN)  

Kim, Joonyoung (Division of Smart Interdisciplinary Engrg., Hannam Univ.)
Lee, Su-Hyung (Korea Railroad Research Institute)
Choi, Yeong-Tae (Korea Railroad Research Institute)
Woo, Sang Inn (Dept. of Civil and Environmental Engrg., Hannam Univ.)
Publication Information
Journal of the Korean Geotechnical Society / v.36, no.3, 2020 , pp. 5-14 More about this Journal
Abstract
The railway concrete track has been increasingly adopted for high-speed train such as KTX due to its high running stability, improved ride quality for the passengers, and low maintenance cost. However, excessive settlement of the railway concrete track has been monitored at embankment sections of the ◯◯ High-speed Line, resulting in the concerns on the safety of railway operation. In order to establish an effective maintenance plan for the concrete track railway exceeding the allowable residual settlement, it is essential to reasonably predict their long-term settlement behavior during the public period. In this study, we developed a model for predicting the long-term settlement behavior of concrete track using recurrent neural network (RNN) and examined the applicability of the developed model.
Keywords
Concrete railway track; Prediction; Recurrent neural network; Settlement;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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