0
  • DE
  • EN
  • FR
  • Base de données et galerie internationale d'ouvrages d'art et du génie civil

Publicité

Prediction of Mechanical Strength Based on Deep Learning Using the Scanning Electron Image of Microscopic Cemented Paste Backfill

Auteur(s):





Médium: article de revue
Langue(s): anglais
Publié dans: Advances in Civil Engineering, , v. 2018
Page(s): 1-7
DOI: 10.1155/2018/6245728
Abstrait:

The mechanical strength of cemented backfill is an important indicator in mining filling. To study the nonlinear relationship between cemented paste backfill (CPB) and mechanical response, a deep learning technique is employed to establish the end-to-end mapping relationship between the scanning electron microscope (SEM) images and mechanical strength. A seven-layer convolution neural network is set up in the experiment, and the relationship between the SEM image and mechanical strength is established. In addition, the difference between the measured and predicted values is calculated and the mean and variance of the error are analyzed. The average accuracy of the mechanical strength prediction is found to be 8.28%. Thus, the proposed method provides a new technique for the quantitative analysis of mechanical strength of microscale CPB.

Copyright: © 2018 Xuebin Qin 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
    10218584
  • Publié(e) le:
    28.11.2018
  • Modifié(e) le:
    02.06.2021
 
Structurae coopère avec
International Association for Bridge and Structural Engineering (IABSE)
e-mosty Magazine
e-BrIM Magazine