Differential Settlement of Track Foundations Identification Based on GRU Neural Network

Abstract

The timely identification of differential settlement of track foundations is of great significance for the safety of train operation and the maintenance of track structures. However, traditional monitoring techniques cannot meet the requirements of efficient, real-time, and automatic monitoring of track foundation settlement. In order to solve these problems, a real-time identification method based on a gated recurrent unit (GRU) neural network is proposed for the differential settlement of track foundations monitoring. According to parameter sensitivity analysis, the vertical acceleration of the vehicle is selected as the known data fed into the GRU network for differential settlement identification. Then the GRU network is employed to establish the nonlinear relationship between the vertical acceleration of the vehicle and the differential settlement of the track foundation. The results indicate that the longitudinal continuous differential settlement distribution curve of track foundations could be accurately identified with GRU neural network through the real-time vibration response of the vehicle–track. The current method may provide a new means for the real-time and efficient identification of the differential settlement of track foundations.