Prediction of turnout support deterioration through dynamic traintrack interactions integrated with artificial intelligence

Due to the increase of rolling stocks’ speed and limited area for railway project construction which result in sharper curves. Railway turnouts are components that are highly impacted by this phenomenon. Therefore, the loads and vibrations applied to them are high and result in support deterioration. This study aims to use axle box accelerations of rolling stocks to predict railway turnout support deterioration. The key parameter used to measure the deterioration is support stiffness. Support stiffness deterioration can be occurred by different causes such as the exceeded load applying to rail infrastructure, regular application, or extreme events such as flooding. These causes all make the turnout support deterioration and the turnout support stiffness will decrease. Besides the rail infrastructure deteriorates and maintenance needs to be performed which results in cost, it also results in worse passenger comfort because the track infrastructure is less stable. The finite element method is applied to develop rolling stock models and generate numerical data showing the relationship between railway turnout support stiffness deterioration and axle box accelerations. Finite element models are verified with field data to ensure that the results from simulations are reliable. A predicted model is developed using a convolutional neural network model.