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

Joonyoung Kim; Su-Hyung Lee; Yeong-Tae Choi; Sang Inn Woo

doi:10.7843/kgs.2020.36.3.5

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2020 Vol.36, Issue 3 Next Page

Research Article

Long-term Settlement Prediction of Railway Concrete Track Based on Recurrent Neural Network (RNN) 순환신경망을 활용한 콘크리트궤도의 장기 침하 거동 예측

31 March 2020. pp. 5-14

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Abstract

콘크리트궤도는 고속, 고밀도의 운행선로에서도 차량의 주행안정성이 높고, 이용승객에게 좋은 승차감을 제공할 수 있으며, 궤도 보수 비용을 대폭 절감시킬 수 있는 장점을 가지고 있어 고속선을 중심으로 점차 적용 구간이 늘어나고 있다. 하지만, OO고속철도 토공 구간의 노반 침하로 기인된 콘크리트궤도의 2차적 침하가 발생하는 사례가 빈번히 발생하고 있어, 이로 인한 철도 운행의 안정성 문제가 대두되고 있다. 허용 잔류침하량을 초과하는 콘크리트궤도에 대한 효율적인 유지보수 방안 수립을 위해서는 공용 기간 중 발생하는 콘크리트궤도의 장기 침하 거동을 합리적으로 예측하는 것이 필수적이다. 따라서, 본 연구에서는 다양한 인공지능 기술 중 순환신경망을 활용하여 콘크리트궤도의 장기 침하 거동을 예측하는 모델을 개발하였다. 또한 기존 모델의 침하 예측 결과와 비교를 통해 개발된 모델의 적용가능성을 모색하였다.

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 OO 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

References

Asaoka, A. (1978), "Observational Procedure of Settlement Prediction", Soils and Foundations, Vol.18, No.4, pp.87-101.

10.3208/sandf1972.18.4_87

Cha, Y. J., Choi, W., and Büyüköztürk, O. (2017), "Deep Learning-based Crack Damage Detection Using Convolutional Neural Networks", Computer-Aided Civil and Infrastructure Engineering, Vol.32, No.5, pp.361-378.

10.1111/mice.12263

Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014), "Learning Phrase Representations Using RNN Encoder-decoder for Statistical Machine Translation", arXiv preprint arXiv:1406.1078.

10.3115/v1/D14-1179

Chou, J. S., Tsai, C. F., Pham, A. D., and Lu, Y. H. (2014), "Machine Learning in Concrete Strength Simulations: Multi-nation Data Analytics", Construction and Building Materials, Vol.73, pp.771-780.

10.1016/j.conbuildmat.2014.09.054

Dahlberg, T. (2001), "Some Railroad Settlement Models - a Critical Review", Proc. Institution of Mech. Enginrs, Part F: Journal of Rail and Rapid Transit, Vol.215, No.4, pp.289-300, DOI: 10.1016/j.soildyn.2008.07.001.

10.1016/j.soildyn.2008.07.001

Hochreiter, S. and Schmidhuber, J. (1997), "Long Short-term Memory", Neural computation, Vol.9, No.8, pp.1735-1780.

10.1162/neco.1997.9.8.17359377276

Korea Rail Network Authority (2013), Railway Design Standards (Roadbed).

Soeung, S., Lee, S.H., Lee, S.J., Kim, B.J., and Yune, C.Y. (2018), "Causing Factors of Additional Settlement in High-Speed Railways in Korea", KSCE Journal of Civil Engineering, Vol.22, No.10, pp.3843-3851.

10.1007/s12205-018-1040-4

Tan, T. S., Inoue, T., and Lee, S. L. (1991), "Hyperbolic Method for Consolidation Analysis", Journal of Geotechnical Engineering, Vol.117, No.11, pp.1723-1737.

10.1061/(ASCE)0733-9410(1991)117:11(1723)

Tzanakakis, K. (2013), The railway track and its long-term behavior: A handbook for a railway track of high quality, Springer Science & Business Media, Berlin, DOI: 10.1007/978-3-642-36051-0.

10.1007/978-3-642-36051-023914346PMC3727911

Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 36
No :3
Pages :5-14
Received Date : 2019-10-29
Revised Date : 2020-02-29
Accepted Date : 2020-03-03
DOI :https://doi.org/10.7843/kgs.2020.36.3.5

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

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