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Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms

International Journal of Mining Science and Technology 2016年第6期26卷 959-966页

作者： jalali zakaria Mining Engineering Department Higher Educational Complex o.f ZarandShahid Bahonar University of KermanKerrnan ***Iran

Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.

关键词： SMR based on continuous functions Slope stability analysis K-means and FCM clustering algorithms Validation of clustering algorithms Sangan iron ore mines

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