The strength reduction method (SRM) has many advantages compared with the limit equilibrium method (LEM) in computing the safety factor of slopes

but the high computational cost

to some extent

limits the application of SRM in system reliability analyses of slopes. To effectively reduce the number of numerical analyses required for reliability analyses to alleviate the computation work when employing SRM

an efficient analysis method based on active-learning radial basis function (ARBF) surrogate model is introduced. This model uses the active-learning function to select trained samples near the limit state surface to update the surrogate model

which accelerates the convergence speed of the training process. With the linear kernel-based radial basis function

the optimization procedure of model parameters is simplified

by which a concise and stable surrogate model can be established. Moreover

an initial sampling strategy considering the characteristics of soil slopes is proposed to fully take advantage of active-learning process. Once a stable surrogate model is established

Monte Carlo simulation (MCS) is used to calculate the probability of system failure. As a comparison

two conventional reliability methods: active-learning Kriging (AK) model and the quadratic response surface method (QRMS)

together with two typical soil slope cases

are tested to illustrate the computational efficiency and model stability of the introduced ARBF.