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基于统计和聚类分析的黄土台塬塬边裂缝危险性分级 被引量:5

Risk Classification of Cracks in the Loess Platform Edge Based on Statistics and Cluster Analysis
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摘要 黄土台塬塬边裂缝的发育阶段对边坡失稳起着一定的控制性作用。陕西省泾阳南塬地区因长年大规模灌溉共发生了42处、60余起黄土边坡滑动,群发性滑坡的后缘共发育有68条塬边裂缝,总长为4 683 m,塬边裂缝的发育阶段和分布情况与黄土边坡失稳密切相关。基于对泾阳南塬地区塬边裂缝的调查和统计,分析了2007年至2016年以来该地区塬边裂缝属性变化。结果显示:裂缝每年新增4条,增长速率达326 m/a;结合统计分类和K-means聚类两种方法,以裂缝段的形式对研究区裂缝段进行了危险性分极,将68条裂缝的773条裂缝段划分为5个危险性等级;最后通过区内的新滑坡事件验证了裂缝危险性分级结果的可靠性。该方法可为研究黄土台塬塬边裂缝及区域防灾减灾工作提供依据。 The developmental stage of plateau-edge crack in loess plateau plays a certain controlling effect on the slope instability.There are 42 loess landslide places and more than 60 slips due to long-term large-scale irrigation in the South Jingyang Plateau in Shaanxi Province.There are 68 plateau-edge cracks in the back margin of the landslide group with a total length of 4 683 m, which shows the development stage and distribution of plateau-edge crack is closely related to the landslides.Based on the investigation and statistical analysis of plateau-edge cracks in the South Jingyang Plateau area and the comparison of the property variation of plateau-edge cracks from 2007 to 2016,this paper finds the cracks belong to the stage of expansion with 4 new ones per year and the growth rate of 326 m/a.In addition,the paper applies statistical classification and K-means clustering method to dividing 773 crack segments of 68 cracks into 5 risk levels.Then the paper verifies the reliability of the crack risk classification results by the new landslide events in the area,and the method can provide a basis for the study of cracks and the prevention and mitigation of regional disaster.
作者 王卓 许强 魏勇 李骅锦 WANG Zhuo;XU Qiang;WEI Yong;LI Huajin(State Key Laboratory of Geohazard Prevention and Geo-environment Protection,Chengdu University of Technology,Chengdu 610059,China)
出处 《安全与环境工程》 CAS 北大核心 2020年第1期62-68,74,共8页 Safety and Environmental Engineering
基金 国家自然科学基金重大项目(41790445) 国家自然科学基金重点项目(41630640)。
关键词 塬边裂缝 黄土滑坡 裂缝段 统计分类 K-MEANS聚类 危险性分级 plateau-edge crack loess landslide crack segment statistical classification K-means cluster risk classification
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