The paper presents the results of roof fall hazard analysis for the copper mines in Poland. At first, RMR and RFRI systems have been checked as a tool for roof fall hazard assessment. It was proved that rock mass deteriorates in time because of mining operation at the site, hence the roof fall phenomena analysis needs a new approach. In the next step of the analysis the key factors of roof deterioration were determined, and divided into four groups: geological, mining, technical and monitoring. It was demonstrated that only regular roof monitoring can ensure the proper rock fall hazard assessment. Subsequently, the artificial neural network was created and several dozen simulations were conducted. As a result, two dimensionless indices were developed. The first one shows predisposition of a part of the rock mass to destruction, dislocation and deformation - $C_{RF}^P$ - Coefficient of Roof Fall – Predisposition and the second one represents predisposition and possibility of maintaining of the working - $C_{RF}^M$ - Coefficient of Roof Fall - Maintenance. The research on roof fall details and the running workings’ roof observations allowed for categorization of the values of both indices, and providing that way the information about roof stability $C_{RF}^P$, and the information about necessary measures and solutions for the supervisory staff in regard of monitoring of the roof rocks $C_{RF}^M$. Both indices allow for a reliable roof fall hazard assessment by comprehensively combining information from monitoring, observations and investigations.

本文介绍了波兰的铜矿顶板坠落危害分析结果。首先，已将RMR和RFRI系统作为屋顶跌落危险评估的工具进行了检查。事实证明，由于现场采矿作业，岩体会随时间恶化，因此，冒顶现象的分析需要一种新的方法。在下一步分析中，确定了屋顶退化的关键因素，并将其分为四类：地质，采矿，技术和监测。结果表明，只有定期进行屋顶监测才能确保对落石危险进行适当的评估。随后，创建了人工神经网络，并进行了数十次仿真。结果，开发了两个无量纲指标。第一个显示了部分岩体易于破坏，错位和变形的倾向-$C_{\mathrm{[R}F}^P$-屋顶跌落系数-易感性，第二个代表易感性和维持工作的可能性-$C_{\mathrm{[R}F}^{\mathrm{中号}}$ -屋顶跌落系数-维修。对屋顶跌落细节的研究以及对运行过程的屋顶观测结果可以对两个指标的值进行分类，并以此方式提供有关屋顶稳定性的信息。$C_{\mathrm{[R}F}^P$以及有关监督人员对屋顶岩石进行监控所需采取的措施和解决方案的信息 $C_{\mathrm{[R}F}^{\mathrm{中号}}$。通过综合结合监测，观察和调查获得的信息，这两个指数都可以对屋顶坠落危险进行可靠的评估。