The prediction of slope stability is difficult due to the spatial variability of soil parameters and measurement errors from core samples. This study proposed a Bayesian updating framework that based on the random finite element model (RFEM) method. The proposed approach was applied in a field excavation slope project. The field data of a soil slope with anti-slide bored piles under four-stage excavations were compared to elucidate the spatial variability of the cohesion, friction angle and stiffness in different soil layers. Then, a Bayesian updating model with random finite element analysis was developed by considering the COV and SOF of the soil parameters. The proposed analysis approach utilized the prior information from the site investigation and field measurement results. The posterior statistical characteristic values of the soil parameters were obtained from equivalent samples calculated by Markov Chain Monte Carlo simulations. The results indicated that the proposed method can reduce the effect of uncertainty related to the soil properties. Finally, the effects of soil parameters and excavation conditions on the slope stability were established, enabling an evaluation of safety based on field measurement results.

由于土壤参数的空间变异性和岩心样品的测量误差，很难预测边坡的稳定性。这项研究提出了一种基于随机有限元模型（RFEM）方法的贝叶斯更新框架。所提出的方法已应用于野外开挖边坡工程中。比较了四阶段基坑下带抗滑钻孔桩的土质边坡的现场数据，以阐明不同土层内聚力，摩擦角和刚度的空间变异性。然后，通过考虑土壤参数的COV和SOF，建立了带有随机有限元分析的贝叶斯更新模型。所提出的分析方法利用了来自现场调查和现场测量结果的先验信息。从通过马尔可夫链蒙特卡洛模拟计算的等效样本中获得土壤参数的后验统计特征值。结果表明，所提出的方法可以减小与土壤特性有关的不确定性的影响。最后，建立了土壤参数和开挖条件对边坡稳定性的影响，从而可以根据现场测量结果对安全性进行评估。