Slope reliability evaluation using an improved Kriging active learning method with various active learning functions

To enhance the efficiency of slope reliability analysis, this paper presents an improved active learning Kriging method integrated with the Monte Carlo simulation. The turbulent flow of water-based optimization (TFWO) algorithm is used to estimate the correlation parameter of Kriging method, enhancing the accuracy of the Kriging surrogate modeling (KSM) used to approximate the complex and implicit performance functions. An active learning procedure is applied to update the KSM with the aid of the best sample points determined by the active learning function. For illustration and validation, three cases with different layered soil are examined using the improved method. The results show that, compared with other reliability methods, the improved method not only has better computational efficiency, but also maintains the accuracy. For the complex multiple-layered slope problems, accurate results can be obtained in only 0.19 h. Moreover, the active learning function EFF and U have a fine performance for slope reliability compared to other functions used in reliability evaluation.