Investigation of the Influence of Roughness on the Shear Resistance of Concrete-Rock Interfaces Using Random Field Simulations, Numerical Simulations, and Neural Network Modeling: Proposition of Two Approaches for the Estimation of the Peak Shear Strength
Résumé
This paper presents a new approach to determine more robust shear failure criteria, focusing on rough concrete-rock interfaces. The proposed method could also be applied to rock joints. The new approach is based on the distribution and variety of interfaces numerically tested in terms of surface roughness. For this reason, random field simulations are performed using the turning bands method to generate an extensive database of synthetic rough rock surfaces. With this database of synthetic rough rock surfaces, numerical simulations of direct shear tests are carried out. Finally, analytical and neural network models are proposed using the database of shear strength obtained from the finite element simulations to estimate the peak shear strength of concrete-rock interfaces. The performances of the analytical and neural network models in estimating this peak shear strength are evaluated by computing the percentage error and the mean absolute error (MAE) between the predicted and the numerically obtained values. Both models lead to satisfactory predictions. Nevertheless, it is worth noting that the neural network model mildly outperforms the analytical model regarding the magnitude of the error. Furthermore, the neural network model reproduces the possible non-bijective aspect of the correlation roughness-peak shear strength.
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