Application of Bayesian Belief Networks to selection of critical slope movement criteria in open pits

贝叶斯置信网络在露天矿临界边坡运动准则选择中的应用

• 批准号: 462562-2014
• 负责人: Tannant, Dwayne
• 金额: $ 0.29万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Engage Plus Grants Program
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=557871
• 关键词: Application; Bayesian; Belief; Networks; selection

Large open pit copper/molybdenum mines in British Columbia routinely monitor the performance and response of pit walls to on-going mining activities. The primary monitoring techniques involve the use of reflective prisms, total stations, and wire-line extensometers. More recently, these have been supplemented with ground-based synthetic aperture radar measurements. Unfortunately, decision makers at the mines have difficulty interpreting the significance of the measured deformations. The challenge is to interpret the trends and magnitudes of the measured displacements such that meaningful decisions can be made and appropriate and timely operational responses implemented that minimize production disruptions while ensuring the safety of the workplace. The proposed research will develop and apply a novel Bayesian Belief Network model to identify pit wall movement criteria that should be used by open pits in BC. The movement criteria developed within the Bayesian Belief methodology will be tailored for specific geotechnical conditions and pit wall configurations. The selection of appropriate displacement thresholds will allow the mines to minimize operational delays while ensuring a safe working environment in the pit.

不列颠哥伦比亚省的大型露天铜/钼矿定期监测坑壁的性能和对正在进行的采矿活动的响应。 主要的监测技术包括使用反射棱镜、全站仪和绳索伸缩仪。 最近，地面合成孔径雷达测量对这些方法进行了补充。 不幸的是，矿山的决策者很难解释测量到的变形的重要性。 挑战在于解释测量位移的趋势和幅度，以便做出有意义的决策，并实施适当和及时的运营响应，最大限度地减少生产中断，同时确保工作场所的安全。 拟议的研究将开发和应用一种新的贝叶斯信念网络模型，以确定坑壁运动的标准，应使用的露天矿在BC。 在贝叶斯信念方法中制定的移动标准将针对特定的岩土条件和坑壁配置进行定制。 选择适当的位移阈值将使矿山能够最大限度地减少运营延迟，同时确保矿井中的安全工作环境。