Page Header

Home > Vol 15, No 4 (Oct.–Dec. 2022) > Kanjanun

GRNN Prediction Model for Temperature-Induced Deformation of CRTS II Unballasted Slab Track

Kitisak Kanjanun, Yan Bin, Yao Shuang'ao, Sakda Katawaethwarag

Abstract


The General Regression Neural Network (GRNN) is one of the algorithms of artificial neural networks (ANN) that receives much attention in prediction applications. This research used the GRNN to predict the temperatureinduced deformation of unballasted track structures based on experimental data considering external weather conditions, such as sunshine duration, rain conditions, daily maximum temperature, daily minimum temperature, and daily average wind speed. The GRNN network predicts the average absolute error of the prediction results (0.0318 ℃), the maximum absolute error (1.7729 ℃), and the GRNN prediction sample mean squared error (0.070701). The average relative error is 0.32%. The finding of this study shows that the GRNN prediction method has good accuracy and robustness. Furthermore, it can promote the research of unballasted track temperature fields that are related to concrete structures.

Keywords



[1] Y. Bin, G. Dai, and N. Hu, “Recent development of design and construction of short span highspeed railway bridges in China,” Engineering Structures, vol. 100, pp. 707–717, Oct. 2015.

[2] R. Bastin, “Development of German non-ballasted track forms,” in Proceeding of the Institution of Civil Engineer-transport, vol. 159, no. 1, pp. 25–39, Feb. 2006.

[3] X. Song, C. Zhao, and X. Zhu, “Temperatureinduced deformation of CRTS II slab track and its effect on track dynamical properties,” Science China Technological Sciences, vol. 57, no. 10, pp. 1917–1924, Oct. 2014.

[4] H. Xiao, Y. Zhang, Q.-h. Li, F. Jin, and N. Mahantesh, “Analysis of the initiation and propagation of fatigue cracks in the CRTS II slab track inter-layer using FE-SAFE and XFEM,” Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, vol. 233, no. 7, pp. 678–690, Aug. 2019.
[5] Y. Zhong, L. Gao, P. Wang, and S. J. Liang, “Mechanism of interfacial shear failure between CRTSII slab and ca mortar under temperature loading,” Gongcheng Lixue/Engineering Mechanics, vol. 35, no. 2, pp. 230–238, Feb. 2018.

[6] L. Zhou, Y. Yuan, L. Zhao, A. D. G. Mahunon, L. Zou, and W. Hou, “Laboratory investigation of the temperature-dependent mechanical properties of a CRTS-Ⅱ ballastless track-bridge structural system in summer,” Applied Sciences, vol. 10, no.16, Aug. 2020, Art. no. 5504.

[7] Y. Bin, G. Dai, and H. T. Su, “A meteorological parameters-based prediction model of vertical temperature gradient of track plate,” Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), vol. 42, no. 12, pp. 9–13, Dec. 2014.

[8] R. Yang, J. Li, W. Kang, X. Liu, and S. Cao, “Temperature characteristics analysis of the ballastless track under continuous hot weather,” Journal of Transportation Engineering, Part A: Systems, vol. 143, no. 9, Sep. 2017, Art. no. 04017048.

[9] G. Dia, H. T. Su, Y. Bin, and J. P. Zhu, “Nonlinear temperature distribution of longitudinal platetype ballastless track in spring,” Journal of South China University of Technology (Natural Science Edition), vol. 44, no. 2, pp. 20–25, Feb. 2016.

[10] T. Khatib, A. Mohamed, K. Sopian, and M. Mahmoud, “Assessment of artificial neural networks for hourly solar radiation prediction,” International Journal of Photoenergy, vol. 2012, Jun. 2012, Art. no. 946890.

[11] D. F. Specht, “A general regression neural network,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568–576, Nov. 1991.

[12] Z. Juan and Y. Kaiyun, “General regression neural network forecasting model based on PSO algorithm in water demand,” in 2010 Third International Symposium on Knowledge Acquisition and Modeling, 2010, pp. 51–54.

[13] M. Martinez-Blanco, G. Ornelas-Vargas, L. Solis-Sánchez, R. Castañeda-Miranada, H. Vega- Carrillo, J. C. Padilla, I. Garza-Veloz, M. L. Martinez-Fierro, and J. M. Ortiz-Rodriguez, “A comparison of back propagation and generalized regression neural networks performance in neutron spectrometry,” Applied Radiation and Isotopes, vol. 117, pp. 20–26, Nov. 2016.

[14] N. Ceryan, U. Okkan, and A. Kesimal, “Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks,” Rock Mechanics and Rock Engineering, vol. 45, no. 6, pp. 1055–1072, Nov. 2012.

[15] H. Cigizoglu, “Generalized regression neural network in monthly flow forecasting,” Civil Engineering and Environmental Systems, vol. 22, no. 2, pp. 71–81, Jun. 2005.

[16] H. K. Cigizoglu and M. Alp, “Generalized regression neural network in modelling river sediment yield,” Advances in Engineering Software, vol. 37, no. 2, pp. 63–68, Feb. 2006.

[17] H. Zhao and S. Guo, “Annual energy consumption forecasting based on PSOCA-GRNN model,” Abstract and Applied Analysis, vol. 2014, Aug. 2014, Art. no. 217630.

[18] Z. Alizadeh, J. Yazdi, J. H. Kim, and A. K. Al-Shamiri, “Assessment of machine learning techniques for monthly flow prediction,” Water, vol.10, no. 11, Nov. 2018, Art. no. 1676.

[19] A. O. Ratip, A. W. Okoth, and R. Wanyonyi, “A generalized regression neural network model for maize production in Trans Nzoia County,” International Journal of Mathematics Trends and Technology (IJMTT), vol. 65, no.10, pp. 54–60, Oct. 2019.

[20] S. Lee, S. Jung, and J. Lee, “Prediction model based on an artificial neural network for userbased building energy consumption in South Korea,” Energies, vol. 12, no. 4, Feb. 2019, Art. no. 608.

[21] O. Zumin and L. Fujian, “Analysis and prediction of the temperature field based on in-situ measured temperature for CRTS-II ballastless track,” Energy Procedia, vol. 61, pp. 1290–1293, 2014.

[22] H. Su, Y. Bin, and G. Dai, “Temperature filed experimental study of longitudinally connected ballastless track on bridge in one year period,” IABSE Symposium Report, vol. 106, no. 8, pp. 580–589, May 2016.

[23] Z. W. Li, X. Z. Liu, and Y.-L. He, “Identification of temperature-induced deformation for HSR slab track using track geometry measurement data,” Sensors, vol. 19, no. 24, Dec. 2019, Art. no. 5446.

[24] G. Dai, H. T. Su, and Y. Bin, “Experimental study on the vertical temperature gradient of longitudinally connected slab ballastless track on bridge in autumn,” Hunan Daxue Xuebao/ Journal of Hunan University (Natural Sciences), vol. 42, no. 3, pp. 94–99, Mar. 2015.

[25] K. Kerbouche, S. Haddad, A. Rabhi, A. Mellit, M. Hassan, and A. E. Hajjaji, “A GRNN based algorithm for output power prediction of a PV panel,” in Artificial Intelligence in Renewable Energetic Systems. Cham, Switzerland: Springer, 2018.

[26] H. K. Cigizoglu, “Artificial neural networks in water resources,” in Integration of Information for Environmental Security. Dordrecht, Netherlands: Springer, 2008, pp. 115–148.

[27] E. Freyssinet, “The deformation of concrete,” Magazine of Concrete Research, vol. 3, no. 8, pp. 49–56, Dec. 1951.

Full Text: PDF

DOI: 10.14416/j.asep.2021.12.003

Refbacks

  • There are currently no refbacks.


Copyright © 2022 by King Mongkut’s University of Technology North Bangkok. All rights reserved.