Identification method for subgrade settlement of ballastless track based on vehicle vibration signals and machine learning

High-frequency train loads and complex environmental factors over a long period will inevitably cause subgrade settlement, an issue that adversely affects line smoothness. The existing monitoring methods are inefficient, manpower consuming and small in scope. This paper takes the subgrade settlement disease of the double-block ballastless track as the research object and proposes a subgrade settlement identification method based on vehicle vibration signals. In addition, it establishes a vehicle-track-subgrade vertical coupling model that com-bines feature extraction with algorithms such as support vector machines (SVMs) and convolutional neural networks (CNNs) to accurately identify subgrade settlement diseases on ballastless tracks. The results show: I) the subgrade settlement is far more impactful than fastener failure and debonding of the supporting layer on the vehicle vibration, with the main frequency band concentrated in 0 to 10 Hz, and the vertical vibration accel-eration of the vehicle body and bogie can be used as a sensitive feature to identify ballastless track subgrade settlement, II) both the particle swarm optimization (PSO)-SVM and CNN-SVM algorithms can effectively determine the existence of subgrade settlement and the values of the settlement wavelength and amplitude. Both algorithms run a 100 % identification of subgrade settlement, with the lowest identification rates of settlement wavelength being 97.56 % and 100 %, respectively and the lowest identification rates of amplitude being 84.78 % and 97.56 %, respectively. In general, the overall accuracy of the CNN-SVM always outperforms PSO-SVM, and III) changes to the vehicle passing speed will create a more obvious interference effect on the identification of the settlement amplitude. For the PSO-SVM algorithm, the identification rate for the amplitude has a difference of up to 8.70 %, while this is 2.44 % for the CNN-SVM algorithm, indicating the CNN-SVM algorithm is more robust than the PSO-SVM algorithm.