Probabilistic Prediction and Assessment of Train-Induced Vibrations Based on Mixture Density Model
Auteur(s): |
Ziyu Tao
Lingshan He Desi Tu Chao Zou |
---|---|
Médium: | article de revue |
Langue(s): | anglais |
Publié dans: | Buildings, 22 octobre 2024, n. 11, v. 14 |
Page(s): | 3468 |
DOI: | 10.3390/buildings14113468 |
Abstrait: |
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 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 planning phase and in selecting and designing vibration mitigation measures of appropriate levels. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | Cette oeuvre a été publiée sous la license Creative Commons Attribution 4.0 (CC-BY 4.0). Il est autorisé de partager et adapter l'oeuvre tant que l'auteur est crédité et la license est indiquée (avec le lien ci-dessus). Vous devez aussi indiquer si des changements on été fait vis-à-vis de l'original. |
11.64 MB
- Informations
sur cette fiche - Reference-ID
10804461 - Publié(e) le:
10.11.2024 - Modifié(e) le:
25.01.2025