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滑坡失稳机理分析及预警预测研究 被引量:6

Mechanism analysis and early warning prediction of landslide instability
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摘要 在构建滑坡变形现状预警模型和变形预测模型的基础上,以藕塘滑坡为工程背景,开展了其失稳机理分析并进行了其变形现状评价和发展趋势评价.研究结果表明,藕塘滑坡成因包含固有成因和诱发成因两类,前者是滑坡发生的基础条件,后者是滑坡发生的激发条件,两类成因均较为完备,即其致灾条件充分;同时,通过滑坡变形的现状预警,得出藕塘滑坡现状预警等级为Ⅲ级,处于一般稳定阶段,危险性一般,且通过变形预测分析,发现滑坡变形仍将持续增加,稳定性趋于不利方向发展,因此,建议加强滑坡变形监测频率,并做好避让措施准备,以避免成灾损失. Based on the construction of a landslide deformation status warning model and a deformation prediction model,the instability mechanism analysis is carried out first,then the deformation status evaluation and development trend evaluation are carried out.The example analysis shows that the causes of the landslide include two types:the inherent and induced causes,the former is the basic condition of landslide occurrence,and the latter is the excitation of landslide occurrence,the conditions are both complete,the conditions for disaster are sufficient;meanwhile,through the current situation warning of landslide deformation,it is concluded that the present situation warning level of the landslide in lotus pond is grade Ⅲ,which is in the general stable stage and the danger is general.Through the deformation prediction and analysis,the deformation of the landslide will continue to increase and the stability tends to develop in the unfavorable direction.Therefore,it is suggested to strengthen the monitoring frequency of landslide deformation rate,and prepare for avoidance measures to avoid disaster losses.
作者 耿海深 吕文斌 栗燊 蒋国良 GENG Hai-shen;Lü Wen-bin;LI Shen;JIANG Guo-liang(Hydrogeological and Geothermal Geological Key Laboratory of Qinghai Province,Xining 810008,Qinghai,China;Hydro Geology and Engineering Geology and Environmental Geology Survey Institute of Qinghai Province,Xining 810008,Qinghai,China;Disaster Reduction Center of Qinghai Emergency Management Department,Xining 810001,Qinghai,China)
出处 《西北师范大学学报(自然科学版)》 CAS 北大核心 2021年第6期103-109,共7页 Journal of Northwest Normal University(Natural Science)
基金 国家自然科学基金资助项目(51901123)。
关键词 滑坡 失稳机理 预警等级 变形预测 支持向量机 landslide instability mechanism warning level deformation prediction support vector machine
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