A method for predicting vibration characteristics of track structure based on rail acceleration and deep learning

The dynamic test on track structure plays a crucial role in evaluating the operational state. Nonetheless, existing testing approaches involve numerous sensors, resulting in high costs and time consumption. This study proposes an economical, high-precision, and swift solution to predict vibration responses and displacement variations in track structures using rail acceleration. Taking the ordinary track structure and the damping track structure as examples, based on the high correlation between the vibration responses of the rail, track slab and tunnel wall, combined with the proposed Bayesian optimized temporal convolutional neural network model (BOA-TCN), and taking the rail acceleration envelope and vibration level as input, the vibration of tunnel wall and the displacement of rail and track slab are estimated. The results demonstrate that the BOA-TCN model effectively solves the limitation of traditional neural network in accurately capturing vibration characteristics in the frequency range of 1 similar to 100 Hz. The absolute error of the total vibration level prediction is significantly reduced, only 0.74 dB. The single point prediction accuracy of track structure displacement is increased by 80%, the absolute error is 50% of the traditional model, and R-2 can be increased to more than 90%.