基于改进贝叶斯更新方法的边坡参数概率反分析及可靠度评估

Probabilistic back analysis of slope parameters and reliability evaluation using improved Bayesian updating method

某一特定场地的岩土力学参数在地质作用下普遍呈现固有的不确定性,融合现场观测数据进行概率反分析可有效缩减这一不确定性。虽然基于子集模拟的贝叶斯更新(Bayesian Updating with Subset simulation,简称BUS)方法可以将等量场地信息的高维概率反分析问题转化为等效的结构可靠度问题,但是当现场观测数据增多时,构建的似然函数值会变得非常小,甚至低于计算机浮点运算精度,会严重影响概率反分析计算效率与精度。为此,提出了一种基于并联系统可靠度分析的改进BUS方法,从基于乔列斯基分解的中点法出发,将接受率低的总失效区域分解为多个接受率高的子失效区域,从而避免因融合大量现场观测数据引起的“维度灾难”问题,实现对边坡岩土力学参数的准确概率反分析。最后,通过一不排水饱和黏土边坡案例验证了提出方法的有效性,结果表明提出的方法能够融合大量钻孔数据和边坡服役状态等观测信息高效进行岩土力学参数概率反分析及边坡可靠度评估,为高维空间变异参数概率反分析和边坡可靠度评估提供了一种有效的工具。

The geomechanical parameters for a particular site exhibit inherent uncertainties due to geological processes,and probabilistic back analysis incorporating field observation data can effectively reduce these uncertainties.Although the BUS(Bayesian Updating with Subset simulation)method can transform the high-dimensional probabilistic back analysis problem with the equality site information into an equivalent structural reliability problem,the value of the constructed likelihood function can become extremely small or even lower than the computer floating-point operation accuracy as the field observation data increase,which might seriously affect the computational efficiency and accuracy of probabilistic back analysis.To this end,this paper proposes an improved BUS method based on the parallel system reliability analysis.Starting from the Cholesky decomposition-based midpoint method,the total failure domain with a low acceptance rate is decomposed into several sub-failure domains with a high acceptance rate so as to avoid the“curse of dimensionality”arising from the integration of a large amount of field observation data,and to achieve accurate back analysis of the geomechanical parameters of slopes.Finally,the effectiveness of the proposed method is validated through a case study of an undrained saturated clay slope.The results show that the proposed method can integrate a large number of borehole data and the observation information of slope service state for efficient probabilistic back analysis of geomechanical parameters and slope reliability evaluation with reasonable accuracy.The proposed method provides an effective tool for high-dimensional probabilistic back analysis of spatially variable soil parameters and slope reliability evaluation.