勘察数据变化条件下空间变异土坡稳定可靠度协同更新方法

Collaborative reliability updating of slopes with spatially varying soilproperties considering different site investigation data

贝叶斯理论为合理表征土性参数的空间变异性以及量化勘察数据(如不排水剪切强度值)对边坡稳定可靠度的影响提供了有效工具。然而,勘察数据随边坡空间位置变化,而且土性参数空间变异性表征模型(如随机场模型)包含高维不确定性参数。勘察数据的动态变化与高维随机参数反演导致基于贝叶斯理论的边坡稳定可靠度更新具有显著的计算量。为此,提出了考虑变化勘察数据的空间变异土坡稳定可靠度协同更新方法。该方法首先基于贝叶斯更新框架BUS模拟条件随机场,执行边坡稳定性分析;然后基于拒绝抽样原理和协同式分析方法根据不同勘察数据表征岩土体参数的空间变异性,并更新边坡的稳定可靠度。当勘察数据在边坡不同空间位置变化时,避免了重新模拟条件随机场以及执行边坡稳定性分析,同时继承了BUS解决高维随机参数反演的特性,实现了基于贝叶斯理论的空间变异边坡稳定可靠度快速更新。以单层非平稳随机场土坡为例验证了所提方法的合理性和有效性。结果表明,所提方法为勘察数据变化条件下土性参数空间变异性动态表征以及边坡稳定可靠度实时更新提供重要工具。

The Bayesian theory provides an effective tool to properly characterize the spatial variability of soil properties and quantify the effect of site investigation data(e.g.,undrained shear strength data)on reliability of slope stability.However,the site investigation data sequentially appears at different spatial locations of a slope,and the model to characterize the spatially varying soil properties(e.g.,random field model)usually involves a great number of uncertain parameters.These pose a great computational challenge for Bayesian updating of slope reliability considering spatially varying soil properties.A collaborative reliability updating approach for the slope stability with spatially varying soil properties considering different site investigation data is proposed.It first makes use of the Bayesian updating with structural reliability methods(BUS)to simulate random fields and perform slope stability analyses,and then employs the rejection sampling principle and collaborative analysis to characterize the spatially varying soil properties and update the reliability of slope stability considering different test data.As the site investigation data spatially appears within a slope,repeated simulations of conditional random fields and a significant number of slope stability analyses are avoided.Moreover,the combination of the BUS makes it possible for efficient slope reliability updating using the Bayesian analysis that involves high-dimensional model parameters.A single-layered soil slope with a non-stationary random field is employed to demonstrate the effectiveness and validity of the proposed approach.It is shown that the proposed approach provides an effective tool for dynamic characterization of spatial variability of soils and real-time reliability updating of slope stability under different site investigation data.