Active searching algorithm for slope stability reliability based on Kriging model

The complexity of slope engineering is not only reflected in the variability of geotechnical parameters, but also in the implictic, nonanalytic and unascertain properties of the performance function. In response to these characteristics, based on the limit balance model, a direct solution algorithm for slope stability reliability is introduced. First, the limit balance model for slopes is called to obtain the geotechnical parameters and the samples corresponding to the slope stability factors. Secondly, the Kriging anisotropic dependence mapping method is used to change the performance function into a random process and to determine the control variables of the process. Then combined with the Monte Carlo simulation and active learning method, and based on the searching rules to adjust the training samples, the probable failure zone of the random process is found by iterative loop. Finally, the random process function of the failure zone is called to work out the failure probability of slopes. The case studies and calculated results show that the accuracy of the proposed method is quite similar to that of the Monte Carlo simulation, and it is simpler and more practical.