Predicting rock fragmentation and blast-induced damage

在采矿中，挖掘的稳定性对工人的安全至关重要，获得足够的岩屑尺寸可以极大地提高生产率。爆破是一种常用的岩石破碎开挖方法。爆破设计的经验指南被广泛使用，即使这些指南涉及一定程度的不确定性，因此存在潜在的不稳定性和不充分的碎片。最佳的爆破效果通常需要多次爆破设计，而且这种做法既昂贵又耗时。在预测岩石破碎时，需要由岩体中的自然节理相交形成的原位块体的大小。离散裂隙网络(DFN)是代表原地块体大小分布的三维节理系统。以往的研究表明，DFN与有限元/离散元混合方法相结合，在预测块体崩落过程中的岩石破碎方面具有潜力。这种方法在爆破中有潜在的应用，以获得现场节理网络在整体爆破结果中所起作用的有价值的知识。这项研究的主要目标是利用DFN和数值模拟工具开发爆炸损伤和岩石破碎的预测模型。次要目标是建立现场评估爆炸造成的碎片和损害的程序，并就最佳爆炸设计提出切实可行的建议。该项目将受益于与矿业公司的合作，这些公司认识到这项研究的价值，并同意提供进入矿场的机会。最先进的DFN工具MoFrac将提供真实的裂缝网络，以确定现场块体大小和爆炸诱导的裂缝强度。使用DFN作为爆炸诱导破碎的数值模拟的输入，将在建模过程中保留裂缝特性。将模拟结果与现场测量结果进行比较，可以对预测模型进行校准。以往在优化矿场岩土数据收集活动方面的研究贡献，是为数值模拟选择可靠参数的关键方面。一般的建模实践往往忽略了收集的数据中的潜在差距。这项研究的一个主要贡献是发展了基于DFN的爆炸损伤和破碎预测模型，其主要优点是为数值模拟提供了真实的断裂网络。另一项有价值的研究成果是根据岩石性质和节理方向开发了实用、量化和用户友好的建议。这将使工程师、爆破工和矿山管理人员受益，并将提高现场工作人员的安全。更好地定义爆炸损伤区可以改善稳定性分析。通过更容易和更便宜的材料处理，对岩石破碎的更大控制提高了矿山的生产率。

In mining, the stability of excavations is paramount for the safety of workers and achieving adequate rock fragment sizes can considerably increase productivity. Blasting is a common method used for rock fragmentation and excavation. Empirical guidelines for blast design are widely used, even if these involve a degree of uncertainty and, consequently, potential instabilities and inadequate fragmentation. Optimal blast results often require multiple iterations of blast design and this practice is costly and time-consuming. The size of the in-situ blocks formed by the intersection of natural joints in the rock mass is required to predict rock fragmentation. Discrete fracture networks (DFN) are 3D joint systems representing the distribution of in-situ block sizes. Previous research demonstrated the potential of DFNs, coupled with hybrid Finite/Discrete element methods, for the prediction of rock fragmentation in a block caving operation. This method has potential applications in blasting to obtain valuable knowledge of the role of in-situ joint networks in the overall blast outcome. The principal objective of this research is to develop predictive models for blast-induced damage and rock fragmentation using DFNs and numerical modelling tools. The secondary objectives are to establish a procedure for the field assessment of blast-induced fragmentation and damage and to provide practical recommendations regarding the optimal blast design. This project will benefit from the collaboration with mining companies that recognize the value of this research and agreed to provide access to mine sites. The state of the art DFN tool MoFrac will provide realistic fracture networks to determine the in-situ block sizes and blast-induced fracture intensity. Using DFNs as an input to numerical simulations of blast-induced fragmentation will preserve fracture properties through the modelling process. A comparison of the simulation results with field measurements will calibrate the prediction model. Previous research contributions in optimizing geotechnical data collection campaigns at mine sites are key aspects for the selection of reliable parameters for numerical modelling. General modelling practice often overlook the potential gaps in the collected data. A major contribution of this research is the development of DFN-based predictive models for blast-induced damage and fragmentation, with the main advantage of providing realistic fracture networks for numerical modelling. Another valuable research outcome is the development of practical, quantified and user-friendly recommendations based on rock properties and joint orientation. These will benefit engineers, blasters and mine management; and will increased safety for the personnel working on site. A better definition of the blast damage zone can lead to improved stability analyses. Greater control on rock fragmentation increases mine productivity, with easier and cheaper materials handling.