Anomaly identification of monitoring data and safety evaluation method of tailings dam

被引:5
|
作者
Dong, Kai [1 ,2 ]
Yang, Dewei [1 ]
Yan, Jihao [1 ,3 ]
Sheng, Jinbao [1 ]
Mi, Zhankuan [1 ]
Lu, Xiang [2 ]
Peng, Xuehui [1 ]
机构
[1] Nanjing Hydraul Res Inst, Nanjing, Peoples R China
[2] Sichuan Univ, Coll Water Resources & Hydropower, Chengdu, Peoples R China
[3] Nanjing R&D Hydroinformat Technol Co Ltd, Nanjing, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
tailings dam; cloud model; RBF neural network model; abnormal monitoring data; seepage field; safety evaluation; STABILITY ANALYSIS; SEEPAGE;
D O I
10.3389/feart.2022.1016458
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The seepage field of tailings dam is closely related to the safety state. Real-time evaluation of seepage field safety based on monitoring data is of great significance to ensure the safe operation of tailings pond. The premise of accurately evaluating the safety status is to ensure reliability of the data, and it is necessary to identify the anomalies of the monitoring data. Because of the complex influence factors of seepage field of tailings dam, the traditional anomaly identification method based on regression model fails due to its low fitting accuracy. Therefore, a novel abnormal identification method of monitoring data based on improved cloud model and radial basis function neural network model, which can accurately identify anomaly data and distinguish the environmental quantity response. Based on the coupling relationship between the seepage field and the slope stability, the surrogate model between the depth of saturation line and the safety factor of slope stability is constructed, and the real-time safety evaluation method of seepage field is put forward. The proposed methods are applied to an engineering example. The misjudgment rates of the abnormal data identification method are less than 5%, and it has better applicability than the traditional regression model. The constructed real-time safety evaluation model accurately reflected the health status of the seepage field, and realized the quantitative assessment of the safety of tailings dam. This provides reliable data support for the operation management and the risk control of tailings pond.
引用
收藏
页数:12
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