Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2013, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2013/395096
Keywords
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Funding
- National Natural Science Foundation of China [50934006]
- National Basic Research (973) Program of China [2010CB732004]
- China Scholarship Council
- Doctoral Candidate Innovation Research Support Program by Science & Technology Review [kjdb201001-7]
- Scholarship Award for Excellent Doctoral Student from Ministry of Education of China
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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 different modeling 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 failure mechanism. It is demonstrated that the established random forest and the support vector machine models are effective and efficient approaches to evaluate the rockmass stability of slope.
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