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The dynamic modelling of the behaviour of the Ouarkiss earthen dam under seismic loads was performed using the finite elements method (FEM), with an approach in effective stresses. The soil behaviour is described by the Mohr-Coulomb criterion. A numerical method and a procedure of analysis are presented in this work. The seismic response of an earthen dam was evaluated. Particular emphasis is placed on the calculation of stresses, displacements, deformations and interstitial overpressures recorded during the seismic solicitation. It has been shown that numerical simulation is able to highlight the fundamental aspects of the displacements and deformations processes experienced by the structure of dam and to produce preliminary results for the evaluation of the seismic behaviour of the structure taking into account the physical non-linearity of the materials constituting the body of the dam and the effect of the rigidity of the different zones of the dam and the foundation.In this study, the seismic behaviour of the Ouarkiss earthen dam will be analysed taking into account the physical non-linearity of the materials constituting the body of the dam and the effect of the rigidity of the different zones of the dam and of the foundation [4,5].Materials whose behaviour is elastoplastic are characterised by the appearance of elastic reversible deformations and irreversible plastic deformations. On the loading surface, two behaviour cases are possible: in the case of the Mohr-Coulomb model, the loading surface does not evolve, we then speak of a perfectly plastic elastic law; when the loading surface evolves during the loading, we are talking about an elastoplastic model with work hardening. GeoScience EngineeringVolume 66 (2020), No. 1

In this research study, a combination of lower and upper bound finite element limit analysis (FELA) and artificial neural network (ANN) has been adopted in order to forecast critical seismic coefficients (kc) of homogeneous earth dams (HED) subjected to pseudo-static seismic loading. To achieve this, the results of kc obtained by OptumG2 software were used in the development of the ANN and MR models. The ANN models have shown higher prediction performance than the MR models based on the performance indices. The most appropriate architecture was found 8-14-1, as this gave the best kc predict with the minimum statistical measures of error and the high determination coefficient (> 99%). Consequently, the ANN model can be used to easily and accurately predict kc value of the HED as the best substitute for the conventional methods.

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