Abstract The purpose of this work is to highlight the impact of input parameters uncertainty in discrete fracture network (DFN) models and their engineering applications. We show how the error of an input parameter, here the volumetric discontinuity intensity P32, impacts the DFN model and two important rock mechanics engineering applications: the in-situ fragmentation size distribution and the potential of formation of removable blocks around tunnels, as two key parameters at block cave mining designs. The volumetric discontinuity intensity (P32) is estimated by two different approaches: the first one estimates P32 directly from 1D data and it is straightforward to implement, while the second one is based on the simulation of DFN models and needs both 1D and 2D data sets, which makes it less flexible and time consuming. The estimated values of P32 obtained from the direct approach are found to be more accurate than those by the simulation approach, with significant impacts observed in the constructed discrete fracture network models and in the estimation of the in-situ fragmentation size distribution and potential of formation of removable blocks around tunnels.

中文翻译：

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研究裂缝强度（P32）估计误差对原位岩石破碎和隧道周围块体形成潜力评估的影响

摘要 这项工作的目的是强调离散裂缝网络 (DFN) 模型及其工程应用中输入参数不确定性的影响。我们展示了输入参数的误差，这里是体积不连续强度 P32，如何影响 DFN 模型和两个重要的岩石力学工程应用：原位破碎尺寸分布和隧道周围形成可移动块的潜力，作为两个关键块洞开采设计中的参数。体积不连续强度（P32）通过两种不同的方法估计：第一种方法直接从一维数据估计 P32，实现起来很简单，而第二种方法基于 DFN 模型的模拟，需要一维和二维数据集，这使得它不那么灵活和耗时。