Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers
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 w...
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2013-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/395096 |
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doaj-85f127813aba4825805baec9e0b16e052020-11-24T21:04:21ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/395096395096Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple ClassifiersLongjun Dong0Xibing Li1School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaThe 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.http://dx.doi.org/10.1155/2013/395096 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Longjun Dong Xibing Li |
spellingShingle |
Longjun Dong Xibing Li Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers Mathematical Problems in Engineering |
author_facet |
Longjun Dong Xibing Li |
author_sort |
Longjun Dong |
title |
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers |
title_short |
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers |
title_full |
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers |
title_fullStr |
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers |
title_full_unstemmed |
Comprehensive Models for Evaluating Rockmass Stability Based on Statistical Comparisons of Multiple Classifiers |
title_sort |
comprehensive models for evaluating rockmass stability based on statistical comparisons of multiple classifiers |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2013/395096 |
work_keys_str_mv |
AT longjundong comprehensivemodelsforevaluatingrockmassstabilitybasedonstatisticalcomparisonsofmultipleclassifiers AT xibingli comprehensivemodelsforevaluatingrockmassstabilitybasedonstatisticalcomparisonsofmultipleclassifiers |
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1716771419305541632 |