Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers. (12th September 2013)

• Record Type: Journal Article
• Authors: Dong, Longjun; Li, Xibing
• Other Names: Zhang Baochang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-09-12
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/395096
• Languages: English
• ISSNs: 1024-123X

Abstract

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 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.