A Machine Learning driven flow modelling of fragmented rocks in cave mining

该项目旨在开发一种综合方法，利用微观尺度和宏观尺度信息来预测块体尺度行为，以便建立更好的洞穴开采设计。将研究不同矿物组成对岩石储能和断裂性能的影响，以检验块状洞穴开采的岩石破碎情况。随后将开发基于机器学习的模型，以建立对块状规模、岩体性质和矿床可崩落性的各种预测模型。最后，我们将发展一个新的基于双重损伤概念的本构模型，该模型将捕捉岩石的破碎，并使用光滑粒子流体动力学来模拟大规模矿山布置中的洞穴传播。

The project aims to develop an integrated method that uses micro scale and macro scale information to predict block scale behaviour so that a better cave mining design can be established. The role of various mineral composition on the energy storage and fracture properties of rocks will be investigated to examine rock fragmentation for block cave mining. Later Machine Learning based models will be developed to establish various predictive models for Block Scale rock mass behaviour and caveability of ore deposit. Finally, we will develop a new constitutive model based on a dual damage concept that will capture the rock fragmentation and simulate the cave propagation in a large scale mine layout using Smoothed-particle hydrodynamics.