Probabilistic Prediction and Assessment of Train-Induced Vibrations Based on Mixture Density Model

Probabilistic Prediction and Assessment of Train-Induced Vibrations Based on Mixture Density Model

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This study presents a probabilistic prediction method for train-induced vibrations by combining a deep neural network (DNN) with the mixture density model in a cascade fashion, referred to as the DNN-RMDN model in this paper.A benchmark example is conducted to demonstrate and evaluate the prediction performance of Pennants the DNN-RMDN model.Subsequently, the model is applied to a case study to investigate and compare the uncertainties of train-induced vibrations in the throat area and testing line area of a metro depot.After training, the model is capable of accurately predicting the probability density function (PDF) of train-induced vibrations at different distances from the track and at different frequencies.

Utilizing the predicted PDF, probabilistic assessments can be performed to ascertain the likelihood of surpassing predefined limits.By employing a mixture density model instead of a single Gaussian distribution, the DNN-RMDN model achieves more accurate prediction of the PDF for train-induced vibrations.The proposed probabilistic assessment framework can effectively assist in vibration screening during the Dry planning phase and in selecting and designing vibration mitigation measures of appropriate levels.