Genetic algorithm optimized Taylor Kriging surrogate model for system reliability analysis of soil slopes

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A Kriging-based surrogate model provides a logically strict and efficient tool to evaluate the system reliability of a slope. However, the constant trend function adopted in the ordinary Kriging (OK) cannot always well capture the nonlinear non-smooth properties of a slope stability problem. Although the universal Kriging (UK) with a linear or a quadratic trend function could be an alternative for some cases, a higher order nonlinear trend function is preferable for some more complicated nonlinear non-smooth cases in the slope stability analysis. To address this problem, a genetic algorithm (GA) optimized Taylor Kriging (TK) surrogate model is proposed for the system reliability analysis of soil slopes in this paper. The proposed surrogate model allows a unified framework of the Kriging, considering different extents of nonlinear properties according to the Taylor expansion order (e.g., can be as high as the fourth order). The GA is introduced to search for the optimal correlation parameters, of which the effectiveness is verified by an analytical example. The feasibility of the proposed surrogate model is then validated by two analytical examples before its application to the practical slope reliability analyses. The results show that the UK model can be incorporated into the TK model, and the TK model provides a higher accuracy and efficiency when facing the highly nonlinear slope stability problems. It is also found that the UK model cannot fully capture the potential nonlinear properties existed in a slope stability model as compared with the higher order TK model.