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Integrating photogrammetry and discrete fracture network modelling for improved conditional simulation of underground wedge stability

机译:集成摄影测量和离散断裂网络建模,改进了地下楔形稳定性的条件模拟

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Over the last decade, the advantages of discrete fracture network (DFN) models over more conventional tools for key block stability analysis have become increasingly apparent. Without their need for a series of simplifying assumptions regarding the fracture system, rock wedge formation and excavation geometry, DFN's ability to accurately capture the underground rock mass is clear. Coupled with the probabilistic consideration of block formation and joint strength parameters, they provide a valuable tool to the engineer for risk-based underground stability assessments. However, recent changes in DFN technology have allowed a step change in modelling realism to be incorporated. A major improvement is the ability to generate DFN models directly conditioned to photogrammetric surveys so that the kinematic assessment is carried out on a structural description that accurately reflects the scanned location. This conditioned DFN model is embedded within an unconditioned stochastic description of the rock mass away from the scanned rock mass exposure, thus, providing a model that is constrained by the available geotechnical data (boreholes, scanning, trace mapping) but accurately conditioned to the key observed structures. The result is an ability to optimise excavation and ground support designs with a method that intelligently handles the natural heterogeneity imposed by the rock mass, combining what we see with what we know.
机译:在过去的十年中,在更传统的关键块稳定性分析中对更传统的工具的离散骨折网络(DFN)模型的优点变得越来越明显。如果不需要一系列关于骨折系统的简化假设,摇滚楔形形成和挖掘几何形状,DFN准确捕获地下岩石质量的能力很清楚。再加上块状形成和关节强度参数的概率考虑,它们为工程师提供了有价值的工具,用于基于风险的地下稳定性评估。然而,DFN技术的最近变化允许在建模现实中纳入一步变化。主要改进是能够将DFN模型直接调节到摄影测量调查,以便在结构描述上进行运动评估,该结构描述是准确地反映扫描位置的结构描述。这种调节的DFN模型嵌入了远离扫描的岩石质量曝光的岩石质量的无条件的随机描述中,从而提供了由可用的岩土数据(钻孔,扫描,踪迹映射)约束的模型,而是准确地调节到钥匙观察到的结构。结果是利用智能处理岩石质量施加的自然异质性的方法优化挖掘和地面支持设计的能力,与我们所知道的求助。

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