A novel efficient probabilistic prediction approach for train-induced ground vibrations based on transfer learning

To deal with the issues of high computational cost and prediction uncertainty of numerical models in train-induced ground-borne vibration prediction, a prediction method based on transfer learning is proposed in this study. In this method, the vehicle-track-coupled analytical model and three-dimensional finite element model are first used to calculate the train-induced ground vibration under various condition variables, and these data were used as training samples to pre-train the deep neural network models. Numerous train-induced ground vibration experiments were then conducted along the metro lines in Beijing, and those measured vibration data were used to fine-tune the pre-trained deep neural network model with the transfer learning strategy. A random variable obeying a Gaussian distribution is assumed over the predicted vibration acceleration levels to model the randomness of train-induced vibration, and the parameters of this distribution were determined by the statistical results of vibration monitoring data in the metro tunnels. The fully trained model could complete the prediction of train-induced ground vibration in seconds. Finally, a case study was carried out, by comparing the probabilistic prediction results with the statistical results of the field measurements, and the feasibility and the improvement of the proposed method were demonstrated.

Automated summary

In this study, a prediction method based on transfer learning is proposed to address the issues of high computational cost and prediction uncertainty in train-induced ground-borne vibration prediction. The method uses vehicle-track-coupled analytical model and three-dimensional finite element model to calculate train-induced ground vibration under various conditions as training samples for pre-training deep neural network models. The pre-trained models are then fine-tuned with measured vibration data using the transfer learning strategy. A Gaussian distribution is assumed for the predicted vibration acceleration levels to model the randomness of train-induced vibration. The fully trained model can complete the prediction in seconds. A case study demonstrates the feasibility and improvement of the proposed method by comparing the probabilistic prediction results with field measurements.