| Application | PLAXIS 2D |
| Version | PLAXIS 2D |
| Original Title | On the use of different constitutive models in data assimilation for slope stability |
| Original Author | M. Mohsan, P.J. Vardon, F.C. Vossepoel |
| Publication year | 2021 |
| Date created | 05 August 2021 |
| Date modified | 04 October 2021 |
Fig. 5. Geometry of the slope (dimensions in m) and black circles represents the measurement points.
Summary
In this publication, the authors used the data assimilation to assimilate the slope deformation data in a numerical model of slope stability to improve the parameter and factor of safety estimation. They also investigated the effect of using different constitutive models (the Mohr-Coulomb model and the Hardening Soil model) under different conditions (close-to-failure and far-from-failure cases).
Fig. 13. Probability distribution of factor of safety at 2000 days based on the prior and estimated parameters at 1000 and 2000 days. Two curves (FoS_HS_estimated parameters at 1000 days and FoS_HS_estimated parameters at 2000 days) are overlapping on the left side and three curves (FoS with prior parameters, FoS_MC_estimated parameters at 1000 days and FoS_MC_estimated parameters at 2000 days) are overlapping on the right side of the figure (far-from-failure).
Abstract
A recursive ensemble Kalman filter (EnKF) is used as the data assimilation scheme to estimate strength and stiffness parameters simultaneously for a fully coupled hydro-mechanical slope stability analysis. Two different constitutive models are used in the hydro-mechanical model: the Mohr-Coulomb (MC) model and the Hardening Soil (HS) model. The data assimilation framework allows the investigation of the effect of constitutive behaviour on its ability to estimate the factor of safety using measurements of horizontal nodal displacement at the sloping face. In a synthetic study, close-to-failure and far-from-failure cases of prior property estimations illustrate the effect of initial material property distribution with different material models. The results show that both models provide a reliable factor of safety when the distribution of prior parameters is selected close-to-failure. However, the HS model results in the improved estimation of factor of safety for the far-from-failure case while this is not the case for the MC model. In addition, for the same level of accuracy the computational effort required for the HS model is comparatively less than for the MC model.
This article was published in Computers and Geotechnics, Volume 138 (2021), and is available as open access here:
doi: https://doi.org/10.1016/j.compgeo.2021.104332
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.