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The evaluation of seismic slope performance during earthquakes is important, because the failure of slope (such as an earth dam, natural slope, or constructed earth embankment) can result in significant financial losses and human. It is important, therefore, to be able to forecast such displacements induced by earthquake. However, the traditional forecasting methods, such as empirical formulae, are inaccurate because most of them do not take into consideration all the relevant factors. In this paper, new intelligence method, namely relevance vector regression (RVR) optimized by dolphin echolocation (DE) and grey wolf optimizer (GWO) algorithms is introduced to forecast the earthquake induced displacements (EID) of slopes. The DE and GWO algorithms is combined with the RVR for determining the optimal value of its user-defined paramee RVR. The performances of the proposed predictive models were examined according to two performance indices, i.e., coefficient of determination (R²) and mean square error (MSE). The obtained results of this study indicated that the RVR-GWO model is a reliable method to forecast EID with a higher degree of accuracy (MSE= 0.0160 and R²= 0.9955).

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During project planning, the prediction of TBM performance is a key factor for selection of tunneling methods and preparation of project schedules. During the construction, TBM performance need to be evaluated based on the encountered rock mass conditions. In this paper, the model based on a relevance vector regression (RVR) optimized by dolphin echolocation algorithm (DEA) for prediction of specific rock mass boreability index (SRMBI) is proposed. The DEA is combined with the RVR for determining the optimal value of its user-defined parameters. The optimized RVR by DEA was employed to available data given in the open source literature. In this model, rock mass uniaxial compressive strength, brittleness index (B_i), volumetric joint account (J_v), and joint orientation (J_o) were used as the input, while the SRMBI was the output parameter. The performances of the suggested predictive model were tested according to two performance indices, i.e., mean square error and determination coefficient. The results show that the RVR- DEA model can be successfully utilized for estimation of the SRMBI in mechanical tunneling.