基于大数据理论尾矿坝灾害风险评估
Tailings Dam Disaster Risk Assessment Based on Big Data Theory
李占科 1魏宁宇 2郑磊 1彭瑞雪 2郭艳艳 2马骏腾2
作者信息
- 1. 中钢集团赤峰金鑫矿业有限公司
- 2. 北方工业大学土木工程学院
- 折叠
摘要
以实际工程为背景,结合尾矿坝溃坝事故案例及《尾矿库安全规程》(GB 39496-2020)建立尾矿坝风险评价指标体系,根据熵权法确定尾矿坝风险指标体系中的二级指标权重,通过系统研究与分析确定了坝体失稳和洪水漫顶所占权重最大,其次是渗透破坏和安全管理.对于指标体系中的三级指标,采用控制变量的方式,通过大数据模拟计算得到各项指标与安全系数的变化关系,并进行拟合和反分析,确定三级指标权重.通过对二级指标与三级指标权重进行综合计算和系统分析,得出坝体外坡比和堆积容重所占权重比例较大,其次是汇水面积、干滩长度和浸润线埋深等指标.基于评价体系及大数据体系计算和分析,应用模糊综合评价计算方法,最终确定当前尾矿坝安全风险等级为低风险.
Abstract
Taking the actual project as background,the risk assessment index system of tailings dam is established by combining the case of tailings dam break accident and the"Safety Regulations of Tailings Pond"(GB 3946-2020).The weight of the secondary index in the risk index system of tailings dam is deter-mined according to the entropy weight method.Through systematic research and analysis,it is determined that the dam body instability and flood overtopping account for the largest weight,followed by seepage failure and safety management.For the three-level indicators in the index system,the control variable method is used to obtain the change relationship between each indicator and the safety factor through big data simula-tion calculation,and the fitting and reverse analysis are carried out to determine the weight of the third index-es.Through the comprehensive calculation and systematic analysis of the weights of the second and third in-dexes,it is concluded that the weight ratio of the external slope and the bulk density of the dam body are larg-er,followed by the catchment area,the length of the dry beach and the buried depth of the infiltration line.
关键词
尾矿库/大数据理论/熵权法/风险评估Key words
tailings pond/big data theory/entropy weight method/risk assessment引用本文复制引用
出版年
2024
NETL
NSTL
万方数据