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Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation

Auteur(s):


Médium: article de revue
Langue(s): en 
Publié dans: The Open Civil Engineering Journal, , n. 1, v. 9
Page(s): 53-60
DOI: 10.2174/1874149501509010053
Abstrait:

In order to forecast the displacement of deep foundation pit support, this document proposes a new method which combines the cross validation method and supports vector machine (SVM) based on random small samples.Because the random small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function of support vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model of underground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that this method can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In the aspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practical engineering.

Copyright: © 2016 Zhou Shengquan 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
    10175655
  • Publié(e) le:
    30.12.2018
  • Modifié(e) le:
    03.08.2019