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

Author(s):


Medium: journal article
Language(s): English
Published in: The Open Civil Engineering Journal, , n. 1, v. 9
Page(s): 107-114
DOI: 10.2174/1874149501509010107
Abstract:

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:

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.

  • About this
    data sheet
  • Reference-ID
    10175640
  • Published on:
    30/12/2018
  • Last updated on:
    02/06/2021
 
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