Open AccessRandom Small Sample Prediction Model on Displacement of Extensive Deep Soil Excavation
Author(s) -
Shengquan Zhou,
Xiaolong Zhao,
Zhaoming Yao
Publication year - 2015
Publication title -
the open civil engineering journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 16
ISSN - 1874-1495
DOI - 10.2174/1874149501509010053
Subject(s) - support vector machine , displacement (psychology) , cross validation , kernel (algebra) , random forest , data mining , function (biology) , algorithm , sample (material) , computer science , engineering , artificial intelligence , mathematics , psychology , chemistry , chromatography , combinatorics , evolutionary biology , psychotherapist , biology
In order to forecast the displacement of deep foundation pit support, this document proposes a new method whichcombines the cross validation method and supports vector machine (SVM) based on random small samples.Because therandom small monitoring data are difficult to fit and forecast, the cross validation method and different kernel function ofsupport vector machine algorithm arerepeatedly used to establish and optimize the displacement prediction model ofunderground continuous wall, and then uses validation samples to test the accuracy of the models. The results show that thismethod can meet the requirements of precision relatively well, and Cauchy kernel function is better than the other. In theaspect of accuracy of model fitting and prediction, this method has great advantages, which can be applied to practicalengineering.