Due to the complexity and obvious random distribution features of joints and fractures in the rock slope

accurately simulating the random fracture network is crucial for slope stability evaluation. However

the current methods cannot effectively make full use of the measured data of structural outcrops. This paper adopts a Bayesian updating approach to optimize the probability distributions of geometric and shear strength parameters of structural planes and correct the random fracture network of rock mass by using field measured data. The field measured data of structural planes are characterized as sample distributions. Based on these

a non-intrusive stochastic finite element method is employed to conduct reliability analysis of jointed rock slopes considering the uncertainties of geometric parameters (e.g.

dip and trace length) and shear strength parameters (e.g.

friction angle and cohesion) of structural planes at the same time. Finally

a simplified slope model selected from the left bank of Xiaowan hydropower station is adopted to validate the effectiveness of the proposed method. The results indicate that the probability distributions of structural plane parameters inferred from the Bayesian updating approach agree well with the corresponding analytical solutions. The Bayesian updating approach can effectively reduce the estimation of uncertainties and optimize the probability distributions of the structural plane parameters by incorporating the field measured data. Furthermore

more realistic fracture network models and reliability analysis results of the slope can be obtained. When the posterior information of structural plane parameters is used to generate a fracture network model and conduct slope reliability analysis

the estimated posterior probability of slope failure will be greatly smaller than the prior probability of slope failure.