Forecast Method of Track Irregularity of Heavy-haul Railway Based on BP Neural Network

Abstract

Abstract The track irregularity is an important factor for the transport safety of heavy-haul railway. Limited by the technique of data mining and analysis, the data obtained by rail inspection vehicles cannot fully identify the status of track irregularity. In this paper, based on the characteristics of track irregularity, the BP neural network is used to predict the geometric irregularity parameters of heavy-haul railway tracks, identify the status of track irregularity, and provide support for the decision-making of maintenance strategy. In order to further verify the accuracy of the BP neural network, the single and double hidden layer networks are established to predict 20 sets of the 7 indicators of track irregularity. According to the prediction results, the mean square errors of the single and double hidden layer networks are 0.064 and 0.051, respectively. The result shows that the multi-hidden layer BP neural network has higher accuracy, which provides a new idea for the research on the prediction model of track irregularity.