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- Title
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers.
- Authors
Longjun Dong; Xibing Li
- Abstract
The relationships between geological features and rockmass behaviors under complex geological environments were investigated based on multiple intelligence classifiers. Random forest, support vector machine, bayes' classifier, fisher's classifier, logistic regression, and neural networks were used to establish models for evaluating the rockmass stability of slope. Samples of both circular failure mechanism and wedge failure mechanism were considered to establish and calibrate the comprehensive models. The classification performances of differentmodeling approaches were analyzed and compared by receiver operating characteristic (ROC) curves systematically. Results show that the proposed random forest model has the highest accuracy for evaluating slope stability of circular failure mechanism, while the support vector Machine model has the highest accuracy for evaluating slope stability of wedge failuremechanism. It is demonstrated that the established randomforest and the support vector machinemodels are effective and efficient approaches to evaluate the rockmass stability of slope.
- Subjects
STABILITY theory; STATISTICAL models; RECEIVER operating characteristic curves; LOGISTIC regression analysis; FINITE element method
- Publication
Mathematical Problems in Engineering, 2013, p1
- ISSN
1024-123X
- Publication type
Article
- DOI
10.1155/2013/395096