Random Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation
Zhou Shengquan*, Zhao Xiaolong, Yao Zhaoming
Identifiers and Pagination:Year: 2015
First Page: 53
Last Page: 60
Publisher Id: TOCIEJ-9-53
Article History:Received Date: 17/9/2014
Revision Received Date: 17/12/2014
Acceptance Date: 23/12/2014
Electronic publication date: 31/3/2015
Collection year: 2015
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.