岩土参数的随机性会直接影响边坡稳定性评价结果的精度。首先，依据边坡参数的常用分布特征，利用拉丁超立方抽样法生成若干组边坡土性参数和几何参数的随机样本，用有限元强度折减法求解各组样本对应的边坡安全系数。再考虑土性参数的空间变异性，在二维随机场模型下将蒙特卡罗模拟和有限元强度折减法相结合求解各组样本对应的边坡失效概率。然后，利用样本数据及其安全系数和失效概率对径向基函数（RBF）神经网络进行训练和测试，从而建立边坡安全系数和失效概率的预测模型。算例表明，二维随机场模型能相对精确地考虑参数的空间变异性；在此基础上建立的神经网络模型对边坡的安全系数和失效概率具有较高的预测精度，且能极大地节省边坡稳定性分析的时间。

The precision of slope stability assessment is highly affected by the randomness of soil parameters. Massive groups of soil parameters and slope geometry parameters are randomly generated by Latin hypercube sampling method according to their common distribution characteristics. For each group of parameters, safety factor is calculated by the strength reduction finite element method (SRFEM); and failure probability with consideration of the spatial variation of soil properties is investigated by combining Monte Carlo simulation and SRFEM under the two-dimensional random field. The sample data and corresponding safety factors and failure probabilities are then implemented in the training and testing processes of radial basis function (RBF) neural network to establish forecast models for slope stability analysis. The simulation results of an example show that the two-dimensional random field model can reasonably well reflect the spatial variation of soil properties; and the created RBF neural network-based forecast models not only has high prediction precision on safety factor and failure probability, but also can effectively save the computational time.