期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2018
卷号：189
期号：2
页码：022055
DOI：10.1088/1755-1315/189/2/022055
语种：English
出版社：IOP Publishing
摘要：With the improvement of data collection and storage ability, numerous data are accumulated in the field of geotechnical engineering, which provides the opportunity for the application of the machine learning techniques. An increasing number of researchers adopted machine learning technique to solve the problems which cannot be addressed by using traditional methods. The most advanced machine learning algorithm, Support Vector Machine (SVM), has been widely utilized in geotechnical engineering. The study aims to review the analytical method and application of SVM in geotechnical engineering. Firstly, the basic principles of SVM are introduced. Secondly, the application of the SVM algorithms is presented. The review suggests that SVM can be effectively used for classifying rock and soil mass, predicting the slopes stability accurately and deformation displacement. Meanwhile, physical strength parameters and the models used for earthquake mitigation that are produced by using SVM are the closest to real value.
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