Open AccessUtilization of a least square support vector machine (LSSVM) for slope stability analysis
Author(s) -
Pijush Samui,
D.P. Kothari
Publication year - 2011
Publication title -
scientia iranica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.299
H-Index - 51
eISSN - 2345-3605
pISSN - 1026-3098
DOI - 10.1016/j.scient.2011.03.007
Subject(s) - support vector machine , cohesion (chemistry) , artificial neural network , slope stability , stability (learning theory) , mathematics , friction angle , factor of safety , control theory (sociology) , computer science , geotechnical engineering , engineering , artificial intelligence , machine learning , chemistry , control (management) , organic chemistry
This paper examines the capability of a least square support vector machine (LSSVM) model for slope stability analysis. LSSVM is firmly based on the theory of statistical learning, using regression and classification techniques. The Factor of Safety (FS) of the slope has been modelled as a regression problem, whereas the stability status (s) of the slope has been modelled as a classification problem. Input parameters of LSSSVM are: unit weight (γ), cohesion (c), angle of internal friction (ϕ), slope angle (β), height (H) and pore water pressure coefficient (ru). The developed LSSVM also gives a probabilistic output. Equations have also been developed for the slope stability analysis. A comparative study has been carried out between the developed LSSVM and an artificial neural network (ANN). This study shows that the developed LSSVM is a robust model for slope stability analysis