Citation

Jiang S, Zeng S, Huang J, Yao C. China Saf. Sci. J. 2020; 30(6): 158-165.

Copyright

(Copyright © 2020, China Occupational Safety and Health Association, Publisher Gai Xue bao)

DOI

10.16265/j.cnki.issn1003-3033.2020.06.023

PMID

unavailable

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. © 2020 China Safety Science Journal

Language: zh