A new approach for application of rock mass classification on rock slope stability assessment

The objective of this paper is to present a new rock mass classification system which can be appropriate for rock slope stability assessment. In this paper an evaluation model based on combining the Analytic Hierarchy Process (AHP) and the Fuzzy Delphi method (FDM) was presented for assessing slope...

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Bibliographic Details
Published inEngineering geology Vol. 89; no. 1; pp. 129 - 143
Main Authors Liu, Ya-Ching, Chen, Chao-Shi
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 12.01.2007
Elsevier
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Summary:The objective of this paper is to present a new rock mass classification system which can be appropriate for rock slope stability assessment. In this paper an evaluation model based on combining the Analytic Hierarchy Process (AHP) and the Fuzzy Delphi method (FDM) was presented for assessing slope rock mass quality estimates. This research treats the slope rock mass classification as a group decision problem, and applies the fuzzy logic theory as the criterion to calculate the weighting factors. In addition, several rock slopes of the Southern Cross-Island Highway in Taiwan were selected as the case study examples. After determining the slope rock mass quality estimates for each cases, the Linear Discriminant Analysis (LDA) model was used to classify those that are stable or not, and the discriminant functions which can determine failure probability of rock slopes were carried out by the LDA procedure. Afterward, the results may be compared with slope unstable hazards occurring actually, and then the relation and difference between them were discussed. Results show that the proposed method can be used to assess the stability of rock slopes according to the rock mass classification procedure and the failure probability in the early stage.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0013-7952
1872-6917
DOI:10.1016/j.enggeo.2006.09.017