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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



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DOI: 10.14416/j.asep.2021.12.003

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