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基于文献计量和ISM的尾矿库洪水风险评估

陈国芳1, 张新如1, 李海港2,3, 郑宇2,3, 汪柳月1

1.江西理工大学 应急管理与安全工程学院，江西 赣州  341000；2.江西省应急管理科学研究院，江西 南昌  330000；3.安全生产风险监测预警与防控江西省重点实验室，江西 南昌  330000

摘要: 目的 洪水是影响尾矿库安全的重要因素，明晰洪水对尾矿库的风险传导路径有利于帮助识别关键风险因素，优化防控措施。  方法 结合文献计量法和专家决策筛选出尾矿库洪水风险的重要影响指标，利用解释结构模型（interpretative structural modeling， ISM）对指标层次进行划分，最后基于事故树分析（fault tree analysis， FTA）解析灾害的演化路径，并提出相应预防措施。 结果 结果表明：（1）基于文献计量法总共筛选出24个尾矿库洪水风险影响因素，结合平均权重值与专家经验确定10个相对重要的尾矿库洪水风险影响因素；（2）基于ISM计算得出10个影响因素和洪水风险间的相互影响关系，确定尾矿库洪水灾害的直接、间接和最根本影响因素；（3）结合ISM和事故案例，建立尾矿库洪水灾害事故树，通过布尔代数运算得出18种致灾路径和9种预防事故的路径；（4）分析事故树的结构重要度后发现对尾矿库洪水风险影响最大的事件是排洪能力不足、洪峰流量大和初始浸润线埋深浅。 结论 提出的文献计量法、ISM与FTA相结合的方法不仅实现了客观指标筛选与系统建模的融合，而且为尾矿库防洪实现从“被动应对”向“主动阻断”的转变提供了理论支撑。

关键词:尾矿库;洪水风险;文献计量法;解释结构模型;事故树

doi:10.16186/j.cnki.1673-9787.2025030057

基金项目:国家重点研发计划项目（2023YFC3012200）

收稿日期:2025/03/25

修回日期:2025/05/19

出版日期:2025/10/14

Flood risk assessment of tailings dams based on bibliometrics and ISM

Chen Guofang1, Zhang Xinru1, Li Haigang2,3, Zheng Yu2,3, Wang Liuyue1

1.School of Emergency Management and Safety Engineering， Jiangxi University of Science and Technology， Ganzhou  340010， Jiangxi， China；2.Jiangxi Academy of Emergency Management Science， Nanchang  330030， Jiangxi， China；3.Jiangxi Provincial Key Laboratory of Safety Production Risk Monitoring， Early Warning， Prevention and Control， Nanchang  330030， Jiangxi， China

Abstract: Objectives Floods are a critical factor affecting the safety of tailings dams. Clarifying the risk transmission pathways of floods in tailings dams helps identify key risk factors and optimize preventive measures. Methods Bibliometric analysis and expert judgment were used to select important flood risk indicators. The hierarchical structure of these indicators was determined using Interpretative Structural Modeling （ISM）. Disaster evolution pathways were further analyzed through Fault Tree Analysis （FTA）， and corresponding preventive measures were proposed.  Results （1） A total of 24 flood risk factors were identified through bibliometric analysis， from which 10 key factors were determined based on average weight values and expert experience. （2） ISM revealed the interrelationships among the 10 factors and flood risks， identifying direct， indirect， and fundamental influencing factors.  （3） A fault tree model for flood disasters in tailings dams was constructed by integrating ISM with accident cases. Boolean algebraic operations yielded 18 disaster-causing paths and 9 prevention paths. （4） Structural importance analysis indicated that insufficient drainage capacity， high peak flood flow， and shallow initial phreatic line are the most critical events influencing flood risks. Conclusions The proposed methodology， which integrates bibliometric analysis， ISM， and FTA， achieves the fusion of objective indicator screening and system modeling，  and provides theoretical support for transforming flood prevention in tailings dams  from passive response to active prevention.

Key words: tailing reservoir; flood risk; bibliometric analysis; interpretative structural modeling; fault tree analysis