Sequential probabilistic back analysis on hydraulic conductivity of tailings materials

doi:10.16265/j.cnki.issn1003-3033.2020.06.023

Abstract

Abstract: In order to ensure seepage analysis accuracy of tailings dam, deduce hydraulic conductivity probability distribution of tailings material and to reduce its uncertainty, sequential probabilistic back analysis method of material parameters based on Bayesian updating was proposed. Then, a surrogate model of water table and likelihood function were constructed. Finally, with Daheishan tailings dam taken as an example, 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 levels, and it is necessary to further collect field information of multiple sources and incorporate it into probabilistic back analysis

Key words: tailings dam, hydraulic conductivity, sequential probabilistic back analysis, Bayesian updating, uncertainty

CLC Number:

X936

JIANG Shuihua, ZENG Shaohui, HUANG Jinsong, YAO Chi. Sequential probabilistic back analysis on hydraulic conductivity of tailings materials[J]. China Safety Science Journal, 2020, 30(6): 158-165.

References

[1] 张媛媛, 杨凯. 尾矿库生命周期溃坝风险演化研究[J]. 中国安全科学学报, 2017, 27(7): 1-6.
ZHANG Yuanyuan, YANG Kai. Research on evolution of risk of tailings dam break in lifecycle[J]. China Safety Science Journal, 2017, 27(7): 1-6.
[2] 梁萱. 尾矿库逐渐溃坝三维数值模拟研究[D]. 南昌: 南昌大学, 2019.
LIANG Xuan. Three dimensional numerical simulation study on gradual dam failure of tailings pond[D]. Nanchang: Nanchang Unersity, 2019.
[3] 李康宏, 邓祥辉, 何萌, 等. 基于钻孔资料的某尾矿坝渗透系数反演分析有限元模型建立[J]. 岩石力学与工程学报, 2004, 23(增1): 4 329-4 332.
LI Kanghong, DEGN Xianghui, HE Meng. et al. The finite element model of permeability inversion analysis for a tailings dam based on borehole data is established[J]. Chinese Journal of Rock Mechanics and Engineering, 2004, 23(S1): 4 329-4 332.
[4] 蒋卫东, 李夕兵. 尾矿坝浸润线时空混沌模型及反分析[J]. 中南工业大学学报:自然科学版, 2003, 34(6): 704-707.
JIANG Weidong, LI Xibing. Spatiotemporal chaos model of seepage linein tailings dam and its back analysis [J]. Journal of Central South University of Technology:Natural Science, 2003, 34(6): 704-707.
[5] 徐维生, 柴军瑞, 陈兴周, 等. 东沟尾矿坝渗透系数反演分析[J]. 工程勘察, 2009,37(12): 66-69.
XU Weisheng, CHAI Junrui, CHEN Xingzhou, et al. Inversion analysis of seepage parameters for Donggou tailing filling dam[J]. Geotechnical Investigation and Surveying, 2009,37 (12): 66-69.
[6] 徐维生, 柴军瑞, 李娟娟, 等. 尾矿库坝体加高方案排水分析[J]. 水土保持通报, 2010, 30(5): 226-228.
XU Weisheng, CHAI Junrui, LI Juanjuan, et al. Drainage analysis of dam body elevation scheme for tailings pond [J]. Bulletin of Soil and Water Conservation, 2010, 30(5): 226-228.
[7] BHARAT T V, SHARMA J. Prediction of compression and permeability characteristics of mine tailings using natural computation and largestrain consolidation framework[C]. Geo Frontiers 2011: Advances in Geotechnical Engineering, 2011: 3 868-3 877.
[8] ELRAMLY H, MORGENSTERN N R, CRUDEN D M. Probabilistic stability analysis of a tailings dyke on presheared clay shale[J]. Canadian Geotechnical Journal, 2003, 40(1): 192-208.
[9] 蒋水华, 李典庆. 边坡可靠度更新的贝叶斯方法[M]. 北京: 科学出版社, 2019: 45-49.
[10] STRAUB D, PAPAIOANNOU I. Bayesian updating with structural reliability methods[J]. Journal of Engineering Mechanics, 2015, 141(3) 04014134.
[11] DIAZDELAO F A, GARBUNOINIGO A, AU S K, et al. Bayesian updating and model class selection with subset simulation[J]. Computer Methods in Applied Mechanics and Engineering, 2017, 317: 1 102-1 121.
[12] AU S K, BECK J L. Estimation of small failure probabilities in high dimensions by subset simulation[J]. Probabilistic Engineering Mechanics, 2001, 16(4): 263-277.
[13] 吕鹏, 王晓玲, 吴斌平, 等. 基于熵-盲数的贝叶斯渗流参数反演分析研究[J]. 水力发电学报, 2019, 38(4): 108-118.
LYU Peng, WANG Xiaoling, WU Binping, et al. The inverse analysis of Bayesian seepage parameters based on entropy blind number[J]. Journal of Hydroelectric Engineering, 2019, 38(4): 108-118.
[14] 蒋水华, 李典庆, 周创兵. 基于拉丁超立方抽样的边坡可靠度分析非侵入式随机有限元法[J]. 岩土工程学报, 2013, 35(增2): 70-76.
JIANG Shuihua, LI Dianqing, ZHOU Chuangbing. Non invasive stochastic finite element method for slope reliability analysis based on Latin hypercube sampling [J]. Chinese Journal of Geotechnical Engineering, 2013, 35(S2): 70-76.
[15] 曹荣国. 大黑山尾矿坝渗流与稳定性分析研究[D]. 长春:吉林大学, 2017.
CAO Rongguo. The study and analysis of seepage and stability about tailings dam in Daheishan[D]. Changchun:Jilin Unersity, 2017.
[16] 郑欣, 李全明, 许开立, 等. 考虑变量相关性的尾矿坝坡失稳溃坝概率计算方法[J]. 中国安全生产科学技术, 2015, 11(2): 23-27.
ZHENG Xin, LI Quanming, XU Kaili, et al. A method for calculating the probability of landslide failure of tailings dam considering the correlation of variable [J]. Journal of Safety Science and Technology, 2015, 11(2): 23-27.
[17] DUNCAN J M. Factors of safety and reliability in geotechnical engineering[J]. Journal of Geotechnical and Geoenvironmental Engineering, 2000, 126(4): 307-316.