| Citation: | LIU Quan-min, FU Wei-wang, SONG Li-zhong, GAO Kui, SONG Zi-wei. Identification of structural parameters of ballastless track based on SSA-CNN[J]. Journal of Traffic and Transportation Engineering, 2025, 25(6): 12-22. doi: 10.19818/j.cnki.1671-1637.2025.06.002
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To obtain the in-situ structural parameters of ballastless track in service, a structural parameter identification method was developed by combining the finite element model of ballastless track with the data-driven sparrow search algorithm (SSA)-convolutional neural network (CNN). The finite element model of the ballastless track was established, and frequency response functions (FRFs) under different parameters were calculated within the given parameter space to form a dataset. 70% of the data was taken as the training set, and the remaining data was used as the test set. By using the FRFs of the training set as inputs and track's structural parameters as outputs, the SSA-CNN parameter identification model was trained and verified by the test set. A hammer test on the ballastless track was carried out, and the measured FRFs were input into the parameter identification model to obtain fastener stiffness, damping, and the elastic modulus of CA mortar. The identified parameter values were substituted into the track's finite element model to calculate the vibration response under the same excitation. The calculated results were in good agreement with the test results. Research results show that when the parameter identification model is trained with a dataset containing 10% Gaussian noise, the average absolute percentage errors for identifying fastener stiffness, damping, and the elastic modulus of CA mortar are 7.90%, 1.00%, and 3.03%, respectively, verifying the reliability of the parameter identification model. The track's structural parameters can be captured accurately using the parameter identification method in this paper and the hammer test data. The vibration response of the rail is beneficial to identifying parameters or damage of fastener, while the vibration data of the slab is suitable for identifying the elastic modulus or delamination of the CA mortar layer. The developed parameter identification method for ballastless track is an effective analytical tool for detecting and assessing the service performance of interlayer connection components in ballastless tracks.
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