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

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Published: Sep 9, 2022
DOI: https://doi.org/10.14416/j.asep.2021.12.003
Keywords:
Track engineering Temperature-induced deformation The artificial neural network GRNN algorithm

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Kitisak Kanjanun
Department of Railway Engineering, School of Civil Engineering, Central South University, Changsha, Hunan, China
Kitisak Kanjanun
Department of Railway Engineering, School of Civil Engineering, Central South University, Changsha, Hunan, China
Yan Bin
Department of Railway Engineering, School of Civil Engineering, Central South University, Changsha, Hunan, China
Yao Shuang'ao
Department of Railway Engineering, School of Civil Engineering, Central South University, Changsha, Hunan, China
Sakda Katawaethwarag
Department of Teacher Training in Civil Engineering, Faculty of Technical Education, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand

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.

Article Details

How to Cite
Kanjanun, K., Kanjanun, K., Bin, Y., Shuang’ao, Y., & Katawaethwarag, S. (2022). GRNN Prediction Model for Temperature-Induced Deformation of CRTS II Unballasted Slab Track. Applied Science and Engineering Progress, 15(4), 5662. https://doi.org/10.14416/j.asep.2021.12.003
Issue
Vol. 15 No. 4 (2022)
Section
Research Articles

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