Predicting rock fragmentation and blast-induced damage

在采矿中，开挖的稳定性对于工人的安全至关重要，并且实现足够的岩石碎片尺寸可以显著提高生产率。爆破是用于岩石破碎和挖掘的常用方法。爆破设计的经验准则得到广泛使用，即使这些准则涉及一定程度的不确定性，因此可能存在不稳定性和不充分的破碎。最佳爆破效果通常需要多次迭代爆破设计，这种做法既昂贵又耗时。 预测岩石破碎需要岩体中天然节理相交形成的原位块体的尺寸。离散裂隙网络（DFN）是代表原位块体尺寸分布的三维节理系统。以前的研究表明，DFNs的潜力，再加上混合有限元/离散元方法，预测块崩落作业中的岩石破碎。这种方法在爆破中具有潜在的应用，以获得有价值的知识的作用，在整体爆破结果的现场联合网络。本研究的主要目的是开发预测模型，爆炸引起的损害和岩石破碎，使用DFNs和数值模拟工具。第二个目标是建立一个实地评估爆炸造成的破碎和破坏的程序，并就最佳爆炸设计提出切实可行的建议。 这一项目将受益于与认识到这一研究价值并同意提供矿区准入的采矿公司的合作。最先进的DFN工具MoFrac将提供真实的裂缝网络，以确定原位块体尺寸和爆炸诱导裂缝强度。使用DFN作为输入到爆炸引起的破碎的数值模拟将通过建模过程中保持断裂特性。将模拟结果与现场测量结果进行比较，将校准预测模型。以往的研究成果在优化岩土工程数据收集活动在矿山网站的可靠参数的选择数值模拟的关键方面。一般的建模做法往往忽略了所收集数据中的潜在差距。这项研究的一个主要贡献是基于DFN的预测模型爆炸引起的损害和破碎的发展，提供现实的裂缝网络的数值建模的主要优势。另一项有价值的研究成果是根据岩石特性和节理方向提出实用、量化和用户友好的建议。这将有利于工程师、爆破工和矿山管理人员，并将提高现场工作人员的安全。更好地定义爆炸破坏区可以改善稳定性分析。对岩石破碎的更好控制提高了矿山生产率，使材料处理更容易和更便宜。

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.