This study develops an efficient Bayesian updating method with monitoring data for predicting the deformation of high rock slopes induced by excavation. The importance ranking based on random forest is introduced to identify the key rock parameters as random variables in the Bayesian updating. The surrogate models using support vector machine are constructed to approximate the physical numerical models using FLAC3D for evaluating slope deformation. A practical example involving deformation prediction of the excavated left-bank rock slope for the well-known Baihetan hydropower station in southwest China is presented. The results indicate that the developed Bayesian updating method can efficiently and accurately update the posterior distributions of rock parameters and predict the deformation of high rock slopes induced by excavation. Incorporating the monitoring data of displacement into the Bayesian updating can effectively reduce the uncertainty of rock parameters and displacement prediction. As a result, the displacement predictions made by the Bayesian updating are closer to the monitoring data than the prior displacement predictions. In addition, incorporating more monitoring data of displacement from the previous excavation stages produces more accurate displacement predictions for subsequent excavation stages.

中文翻译：

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利用监测数据对开挖引起的高岩质边坡进行高效的贝叶斯更新变形预测


本研究利用监测数据开发了一种高效的贝叶斯更新方法，用于预测开挖引起的高岩质边坡变形。引入基于随机森林的重要性排序，将关键岩石参数识别为贝叶斯更新中的随机变量。使用支持向量机构建代理模型，以近似使用 FLAC3D 评估边坡变形的物理数值模型。本文以西南著名的白鹤滩水电站为例，对开挖的左岸岩石边坡进行变形预测。结果表明，所开发的贝叶斯更新方法可以高效、准确地更新岩石参数的后验分布，并预测开挖引起的高岩质边坡变形。将位移监测数据纳入贝叶斯更新，可以有效降低岩石参数和位移预测的不确定性。因此，贝叶斯更新所做的位移预测比之前的位移预测更接近监测数据。此外，结合更多来自先前挖掘阶段的位移监测数据，可以为后续挖掘阶段提供更准确的位移预测。