13th Asia Pacific Transportation Development Conference
Prediction of Rail Corrugation Based on Non-Equal Interval Grey Model and BP Neural Network
Publication: Resilience and Sustainable Transportation Systems
ABSTRACT
Aiming at the prediction of the development of rail corrugation, in-depth analysis was carried out based on actual field-measured rail wave data, and a combined model of rail corrugation based on non-equal interval grey model and BP neural network was proposed. The advantage of this model is that the gray theory requires less data, and the BP neural network has strong nonlinear fitting ability. It can predict the future development of rails based on the original wave depth values measured by a small number of non-equal time intervals. The historical data of rail wave wear depth on a certain line are used for model training and prediction analysis. The results show that The average absolute error of the combined model prediction results is significantly reduced compared to the single gray model, and the prediction accuracy test level reaches level 1. It proves the effectiveness of this prediction method in the prediction of rail grinding, which provides important guiding significance for the development of track maintenance and polishing strategy by the public works department.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
Shen, G., Zhang, X. H., Guo, M. H. (2011) “Measurement and Analysis of Wave Wear of Metro Curved Rail.” Urban Rail Transit Research, 14 (04), 53-54+58.
Qu, J. J., Gao, L., Tian, X. Y., & Xin, T. (2010) “Study on medium and long term time-varying parameter prediction model of track geometric state based on Grey Theory.” Journal of Railway, 32 (02), 55-59.
Peng, L. Y., Zhang, J. C., Gou, J. Q., and Li, X. W. (2018) “Prediction method of railway track geometric irregularity based on BP neural network.” Journal of Railway, 40 (09), 154-158.
Ma, S., Gao, L., Liu, X. B., & Cai, X. P. (2019) “Prediction model for smooth state of ballastless track on passenger and freight lines.” China Railway Science, 40 (03), 24-31.
Han, J., Yang, Y., Chen, F., & Wu, X. H. (2014) “Track irregularity prediction based on non-equidistant weighted grey model and neural network.” Journal of Railway, 36 (01), 81-87.
Ma, Z. J., Tang, T., Liu, H. L., Peng, Q., & Jin, T. (2018) “Track quality prediction based on non-equidistant grey model and Elman neural network.” Journal of Harbin University of Technology, 50 (05), 137-144.
Jing, J. F., Deng, F. K., Li, K. C., & Huang, F. C. (2015) “Combination prediction of unequal time interval grey model and BP neural network model for seawater corrosion rate.” Material Protection, 48 (08), 33-36+7.
Jin, X. S., Li, X., Li, W., & Wen, Z. F. (2016) “Research progress of rail corrugated wear.” Journal of Southwest Jiaotong University, 51(02), 264-273.
Deng, J. L. (1987) Basic Method of Grey System, Central China Institute of Technology Press.
Zhou, K. L., and Kang, Y.H. (2005) Neural Network Model and MATLAB Simulation Programming, Tsinghua University Press.
Zhou, H., Wang, X. G., & Li, X.Z. (2010) “Research on Grey Combination Forecasting Model of unequal time interval.” Practice and understanding of mathem. 40(21), 107-112.
Shu, S. H., Xiang, G., He, W. J., Wu, C. G., Zhao, M., & Yuan, Y. X. (2009) “Application of grey model in long-term water consumption forecasting in cities.” Journal of Harbin Institute of Technology, 41(02), 85-87.
Shi, F., Wang, X. C., Yu, Lei., et al. (2010) 30 Case Analysis of MATLAB Neural Network. Beijing: Beijing University of Aeronautics and Astronautics Press, 9-10.
Sun, L. J., and Yang, J. (2011) “B.Application of non-equal interval gray model in prediction of ground pressure disasters.” Metal Mine, 2011(10), 51-54.
Wang, E. Z., Guo, X. L., & Zhang, L. P. (2015) “Experimental Study on Delaying the Development of Rail Grinding and Reducing Noise and Noise.” Railway Construction, 2015(08), 109-111.
Information & Authors
Information
Published In
Resilience and Sustainable Transportation Systems
Pages: 440 - 449
Editors: Fengxiang Qiao, Ph.D., Texas Southern University, Yong Bai, Ph.D., Marquette University, Pei-Sung Lin, Ph.D., University of South Florida, Steven I Jy Chien, Ph.D., New Jersey Institute of Technology, Yongping Zhang, Ph.D., California State Polytechnic University, and Lin Zhu, Ph.D., Shanghai University of Engineering Science
ISBN (Online): 978-0-7844-8290-2
Copyright
© 2020 American Society of Civil Engineers.
History
Published online: Jun 29, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.