中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Distance discriminant analysis method for stability prediction of rock slope in hydropower engineering regions

文献类型:会议论文

作者Li, Xiuzhen1; Li, Shengwei2
出版日期2011
会议名称International Conference on Mechanic Automation and Control Engineering
会议日期2011.7
会议地点内蒙古
关键词slope stability prediction distance discriminant analysis method posterior probability
页码3068 - 3071
通讯作者李秀珍
英文摘要

The stability prediction of rock slopes is a key and complicated problem. We took 24 typical rock slopes in hydropower engineering regions in China as examples, to build distance discriminant analysis (DDA) model with posterior probability for slope stability prediction. The 5 combined indexes, i. e, slope rock mass quality coefficient (SRMR), orientation coefficient of structural plane (F), modified coefficient of structural plane types (λ), slope height coefficient (ℰ) and modified coefficient of construction methods (Kw), were used as the discriminant factors. The analysis results show that DDA method has a lower error rate, the average accuracy rate is 91.67%. By using the DDA method, we can obtain the posterior probability belonging to the different classification as well as the classification of slope stability. Therefore, the method may provide a new way for slope stability prediction.

收录类别EI
会议主办者内蒙古工业大学
会议录MACE 2011 - Proceedings
语种英语
源URL[http://ir.imde.ac.cn/handle/131551/17978]  
专题成都山地灾害与环境研究所_山地灾害与地表过程重点实验室
作者单位1.Key Laboratory of Mountain Hazards and Surface Processes, Chengdu Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
2.Chengdu Center for Hydrogeology and Engineering Geology, Sichuan Provincial Geology and Mineral Resources Bureau, Chengdu 610081, China
推荐引用方式
GB/T 7714
Li, Xiuzhen,Li, Shengwei. Distance discriminant analysis method for stability prediction of rock slope in hydropower engineering regions[C]. 见:International Conference on Mechanic Automation and Control Engineering. 内蒙古. 2011.7.

入库方式: OAI收割

来源:成都山地灾害与环境研究所

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