Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers

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.

A Comparison of Mine Seismic Discriminators Based on Features of Source Parameters to Waveform Characteristics

“…Generally using the priori probability to describe the level of awareness, and then the posterior probability was obtained by modifying it [22][23][24][25][26].…”

Coupled Model of Artificial Neural Network and Grey Model for Tendency Prediction of Labor Turnover

“…Trend item of deformation, which is related by time, is predicted with Grey theory [22]. Random item of trend item, which is a complex nonlinear sequence, is calculated by ANN [18].…”

“…The model uses the advantages of "accumulative generation" of a Grey prediction method, which weakens the original sequence of random disturbance factors and increases the regularity of data. It also takes full advantage of the ANN model approximation performance because it has fast solving speed and can describe the nonlinear relationship easily and avoid the defects of Grey theory [19][20][21][22].…”

Theoretical and Case Studies of Interval Nonprobabilistic Reliability for Tailing Dam Stability

“…Through the introduction and intersection of some new disciplines and theories, some new methods of slope stability analysis, such as reliability analysis based on the probability theory and mathematical statistics [1,2], the comprehensive evaluation method based on the fuzzy/statistical mathematics [3][4][5][6], the gray system evaluation method based on the gray system theory [7][8][9][10][11], and the neural network evaluation method based on the neural network theory [12][13][14][15][16], are gradually formed. These evaluation methods have achieved good application in slope stability analysis and evaluation and promoted the development of slope stability research.…”