Research on Destructiveness of Dam Failure of Tailings Reservoirs Based on Two-Dimensional Cloud Model
-
摘要: 为准确评估尾矿库溃坝后泥石流对下游构筑物的冲击破坏概率与破坏严重程度,依据尾矿库自身溃坝灾害特性、溃坝后泥石流流变特性与仿真模拟技术,构建了针对桥梁工程的冲击作用风险评估体系。基于该体系,结合二维云模型理论生成概率云与严重程度云:先基于信息熵获取权重,再基于逆向云模型生成评价云图,基于标准云模型生成左右半云,最终通过云相似度确定评价云与标准云相似性,并与权值结合得到各尾矿库实际安全状况。以受溃坝泥石流冲击破坏作用的桥梁工程为例进行实证研究后得出,该风险评估指标体系实用性强,后二维云模型能够修正传统云模型直接去除区间断点处数据的不足,提高评价结果准确性与风险评估过程的全面性。Abstract: This study is designed to accurately assess the probability of impact damage to downstream structures caused by debris flows after the dam failure of tailings reservoirs and the severity of the damage. Specifically, an impact risk assessment system for bridge projects was constructed according to the characteristics of the dam failure disaster of tailings reservoirs, the rheological characteristics of the debris flow after dam failure, and simulation technology. On the basis of this system, the two-dimensional (2D) cloud model theory was employed to generate the probability cloud and the severity cloud. For this purpose, weights were obtained with information entropies. Then, the evaluation cloud map was generated by the backward cloud model, and the left and right half clouds were generated by the standard cloud model. Finally, the similarity between the evaluation cloud and the standard cloud was determined by cloud similarity, and the actual safety status of each tailings reservoir was obtained by investigating the weights. A bridge project under impact damage by the debris flow after dam failure was discussed as an example of empirical research. The results show that the proposed risk assessment index system is highly practical, and the improved 2D cloud model can correct the weakness of the traditional cloud model that it directly removes the data at the interval breakpoint and thereby enhance the accuracy of the evaluation results and the comprehensiveness of the risk assessment process.
-
Key words:
- tailings reservoir /
- bridge /
- impact probability /
- severity /
- 2D cloud model
-
[1] MASCARO I, BENVENUTI B, CORSINI F, et al. Mine wastes at the polymetallic deposit of Fenice Capanne (Southern Tuscany, Italy).Mineralogy, geochemistry, and environmental impact[J]. Environmental Geology, 2001, 41(3/4):417-429. [2] 梅国栋.尾矿库溃坝灾害脆弱性评估指标体系及方法研究[J].中国安全生产科学技术,2012,8(12):11-15. [3] 王仪心,米占宽.尾矿坝溃坝安全风险分析评价方法[J].金属矿山, 2019(6):184-188. [4] 孙鸿昌,郝喆,杨青潮.精细地形下的尾矿坝稳定性及溃坝模拟分析[J].水文地质工程地质, 2022, 49(3):136-144. [5] 辛保泉,万露,耿龙龙,等.尾矿库溃坝室外模型试验及灾害预测分析[J].中国安全生产科学技术,2018,14(5):102-108. [6] OWEN J R, KEMP D, LEBRE É, et al. Catastrophic tailings dam failures and disaster risk disclosure[J]. International journal of disaster risk reduction, 2020, 42. DOI:10.1016/j.ijdrr.2019. 101361. [7] DU Z, GE L, NG A H M, et al. Risk assessment for tailings dams in Brumadinho of Brazil using InSAR time series approach[J]. Science of the Total Environment, 2020, 717. DOI:10.1016/j.scitotenv.2020. 137125. [8] GE W, LI Z, LI W, et al. Risk evaluation of dam-break environmental impacts based on the set pair analysis and cloud model[J]. Natural Hazards, 2020, 104(2):1641-1653. [9] TONGLE X, YINGBO W, KANG C. Tailings saturation line prediction based on genetic algorithm and BP neural network[J]. Journal of Intelligent & Fuzzy Systems, 2016, 30(4):1947-1955. [10] FAN Q, TIAN Z, WANG W. Study on risk assessment and early warning of flood-affected areas when a dam break occurs in a mountain river[J]. Water, 2018, 10(10).DOI: 10.3390/w10101369. [11] 戴剑勇,王雯雯,黄晓庆.基于网络云模型的尾矿库溃坝安全评估[J].安全与环境学报,2022,22(1):1-7. [12] 徐镇凯,刘璇,魏博文,等.基于云模型的尾矿库溃坝风险模糊评价模型[J].南水北调与水利科技,2016,14(6):122-127. [13] 姜洲,黄艳华,吴贤国,等.基于云模型和D-S证据理论的尾矿库失稳溃坝警情评价模型及应用[J].水电能源科学,2016,34(10):47-51. [14] 冯春辉.基于属性拓扑的中医诊断数据因果关系推断方法研究[D].秦皇岛:燕山大学,2017. [15] 陈忠源,戴自航.水库边坡稳定性评价的云模型[J].工程地质学报,2020,28(3):619-625. [16] 代劲,胡彪,王国胤,等.分布轮廓与局部特征融合的云模型不确定性相似度量[J].电子与信息学报, 2022, 44(4):1429-1439. [17] 莫俊文,滕仓国,李甲,等.基于熵权-二维云模型的高铁建设工程系统韧性评价[J].铁道科学与工程学报,2022,19(1):26-33.
点击查看大图
计量
- 文章访问数: 58
- HTML全文浏览量: 14
- PDF下载量: 0
- 被引次数: 0