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Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Auteur(s):





Médium: article de revue
Langue(s): en 
Publié dans: Advances in Civil Engineering, , v. 2019
Page(s): 1-8
DOI: 10.1155/2019/3453614
Abstrait:

A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

Copyright: © 2019 Yuchao Zheng et al.
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.

  • Informations
    sur cette fiche
  • Reference-ID
    10312878
  • Publié(e) le:
    09.05.2019
  • Modifié(e) le:
    11.07.2019