Predicting rock fragmentation and blast-induced damage

预测岩石破碎和爆炸引起的损坏

• 批准号: RGPIN-2020-06525
• 负责人: Fillion, MarieHélène
• 金额: $ 1.89万
• 依托单位: Laurentian University of Sudbury
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=718378
• 关键词: Predicting; rock; fragmentation; blast; induced

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.

在采矿中，开挖的稳定性对工人的安全至关重要，达到适当的岩石破碎尺寸可以大大提高生产率。爆破是岩石破碎和开挖的常用方法。爆炸设计的经验准则被广泛使用，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破碎不足。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。