Sequential probabilistic back analysis on hydraulic conductivity of tailings materials

Abstract

In order to ensure seepage analysis accuracy of tailings damꎬ deduce hydraulic conductivity probability distribution of tailings material and to reduce its uncertaintyꎬ sequential probabilistic back analysis method of material parameters based on Bayesian updating was proposed. Thenꎬ a surrogate model of water table and likelihood function were constructed. Finallyꎬ with Daheishan tailings dam taken as an exampleꎬ sequential probabilistic back analysis of hydraulic conductivity of multi ̄layered tailings materials was conducted based on monitoring data of water tables. The results show that the proposed approach can effectively infer hydraulic conductivity and probability distributions as well as reduce their variation coefficients which is reduced by 18􀆰 25% for soil layer closer to monitoring points. Realistic uncertainties of hydraulic conductivity and representation cannot be well deduced only from monitoring information of water levelsꎬ and it is necessary to further collect field information of multiple sources and incorporate it into 第 6 期 蒋水华等: 尾矿材料渗透系数序贯概率反演分析 probabilistic back analysis.