Copula-based approach coupling information diffusion distribution for slope reliability analysis

  • Xinlong Zhou
  • Guang Zhang
  • Shaohua HuEmail author
  • Junzhe Li
  • Dequan Xuan
  • Chang Lv
Original Paper


Uncertain probability distributions and interdependencies of geotechnical parameters have profound impacts on slope reliability analysis, especially under incomplete probabilistic information. In this paper, a copula-based approach coupling information diffusion (ID) distribution is proposed to reduce such uncertainties. Firstly, ID theory is introduced to establish marginal distribution of strength parameter. Copulas are subsequently performed to characterize the corresponding dependence structure and link the proposed ID margins. At last, equivalent samples are simulated and plugged into Monte Carlo simulation (MCS) to estimate slope reliability. The effect of the proposed method is validated by a slope case. Results show that the proposed ID algorithm holds remarkable superiority in estimating the marginal distribution with considering random volatility of geotechnical parameter. For slope reliability, both marginal distributions and interdependencies of geotechnical parameters barely affect the factor of safety (FS) but significantly impact failure probability (pf). Unreasonable estimation of probability distribution may lead to large deviation in calculation of slope failure probability. Comparably, the proposed copula-based approach coupling ID distribution can give a reasonable and robust reliability results with considering random volatility.


Slope reliability Copulas Dependence structure Random volatility Information diffusion 


Funding information

This work was supported by the CRSRI Open Research Program (CKWV2016388/KY), the National Natural Science Foundation of China (51609184), Hubei province technical innovation special major project (2019ACA143), and the National Key Research and Development Program of China (2017YFC0804600).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Safety Science and Emergency ManagementWuhan University of TechnologyWuhanPeople’s Republic of China
  2. 2.Changjiang River Scientific Research Institute of Changjiang Water Resources CommissionWuhanPeople’s Republic of China
  3. 3.China United Engineering Corporation LimitedZhejiangPeople’s Republic of China

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