Development of slope mass rating system using K-means and fuzzy c-means clustering algorithms |
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| Affiliation: | 1. School of Mines, China University of Mining and Technology, Xuzhou 221116, China;2. Jining No. 2 Coal Mine, Yankuang Group, Jining 272071, China;1. Department of Mining Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran;2. Faculty of Mining, Metallurgy and Petroleum Engineering, Amir Kabir University of Technology, Tehran 158754413, Iran;3. Department of Petroleum and Mining Engineering, Shahid Bahonar University of Kerman, Kerman 7616914111, Iran;1. School of Civil Engineering, Wuhan University, Wuhan 430072, China;2. State Key Laboratory of Geomechanics and Geotechnical Engineering, Institute of Rock and Soil Mechanics, Chinese Academy of Sciences, Wuhan 430071, China;1. State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Beijing 100083, China;2. School of Mechanics and Civil Engineering, China University of Mining and Technology, Beijing 100083, China;1. State Key Laboratory Breeding Base for Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao 266590, China;2. College of Mining and Safety Engineering, Shandong University of Science and Technology, Qingdao 266590, China;3. Chongqing Branch, China Coal Research Institute, Chongqing 400039, China;1. School of Safety Engineering, China University of Mining and Technology, Xuzhou 221116, China;2. Chinese Academy of Safety Science and Technology, Beijing 110013, China;3. Henan Province Coal Science Research Institute Co., Ltd., Beijing 100013, China |
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| Abstract: | 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. |
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| Keywords: | 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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