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堰塞坝溃决参数及其寿命预测 被引量:1

Estimation of breach parameters and longevity of landslide dam
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摘要 堰塞坝溃决洪水对下游影响区域的人民生命财产、基础设施以及生态环境构成严重威胁,提高堰塞坝溃决参数及其寿命预测的准确度是应急处置的迫切需求。本文对全球1957组堰塞坝案例分别进行地理、统计学分析,在得到溃决参数主要影响因素的基础上,选取数据库中拥有完整信息的48组案例,利用非线性回归方法分别建立了洪峰流量、破坏深度、溃口顶宽、溃口底宽和溃决时长的预测模型,其均具有较高的拟合程度。之后分析了堰塞坝溃决参数的敏感性,结果显示坝高对溃决过程有显著影响。此外,基于19组寿命信息充分的案例,使用不同自变量和算法分别建立了蓄水阶段持续时间、溢流阶段持续时间的预测模型,并采用加权融合法提出了相应的融合模型。该成果可为量化评估堰塞坝溃决过程参数和寿命预测提供参考。 The significant floods formed by the breaking of landslide dams seriously threaten people’s lives,social stability,and ecological environment in the affected areas downstream.It is an urgent need to improve the accuracy of landslide dam failure parameters and longevity prediction for emergency treatment.Based on the geographical and statistical analysis of the 1957 group of landslide dam cases around the world,the main influencing factors of the breach parameters were determined.The prediction models of peak outflow,breach size,and breaching duration were established by nonlinear regression method,which all have a high degree of fitting.The factors’sensitivity to breach parameters was analyzed quantitatively,and the result indicates that the dam height is the most essential factor for dam break.Furthermore,various independent variables and algorithms are used to establish the prediction models of the duration of infilling,overflowing,and breaching stages,respectively.Finally,the corresponding fusion models were proposed by using the weighted fusion method.The results can provide a reference for quantitative evaluation of the parameters of the dam failure process and longevity prediction.
作者 冯震宇 杨兴国 周家文 廖海梅 FENG Zhenyu;YANG Xingguo;ZHOU Jiawen;LIAO Haimei(College of Civil Engineering,Guizhou University,Guiyang 550025,China;State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,China)
出处 《水利学报》 EI CSCD 北大核心 2024年第3期367-377,共11页 Journal of Hydraulic Engineering
基金 国家自然科学基金区域创新发展联合基金项目(U20A20111) 国家自然科学基金青年基金项目(42107189)。
关键词 堰塞坝 溃决参数 寿命分析 预测模型 landslide dam breach parameter longevity analysis prediction model
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