García-Gonzalo Esperanza, Fernández-Muñiz Zulima, García Nieto Paulino José, Bernardo Sánchez Antonio, Menéndez Fernández Marta
Mathematics Department, Universidad de Oviedo, Oviedo 33007, Spain.
Department of Mining Technology, Topography and Structures, University of León, León 24071, Spain.
Materials (Basel). 2016 Jun 29;9(7):531. doi: 10.3390/ma9070531.
The mining industry relies heavily on empirical analysis for design and prediction. An empirical design method, called the critical span graph, was developed specifically for rock stability analysis in entry-type excavations, based on an extensive case-history database of cut and fill mining in Canada. This empirical span design chart plots the critical span against rock mass rating for the observed case histories and has been accepted by many mining operations for the initial span design of cut and fill stopes. Different types of analysis have been used to classify the observed cases into stable, potentially unstable and unstable groups. The main purpose of this paper is to present a new method for defining rock stability areas of the critical span graph, which applies machine learning classifiers (support vector machine and extreme learning machine). The results show a reasonable correlation with previous guidelines. These machine learning methods are good tools for developing empirical methods, since they make no assumptions about the regression function. With this software, it is easy to add new field observations to a previous database, improving prediction output with the addition of data that consider the local conditions for each mine.
采矿业在设计和预测方面严重依赖实证分析。一种名为临界跨度图的实证设计方法，是专门基于加拿大大量充填采矿的案例历史数据库，为巷道式开挖中的岩石稳定性分析而开发的。这种实证跨度设计图表针对观测到的案例历史，绘制了临界跨度与岩体质量评级的关系图，并且已被许多采矿作业用于充填采矿采场的初始跨度设计。已采用不同类型的分析方法将观测到的案例分为稳定、潜在不稳定和不稳定组。本文的主要目的是提出一种定义临界跨度图岩石稳定区域的新方法，该方法应用机器学习分类器（支持向量机和极限学习机）。结果表明与先前的准则具有合理的相关性。这些机器学习方法是开发实证方法的良好工具，因为它们对回归函数不做任何假设。有了这个软件，很容易将新的现场观测数据添加到先前的数据库中，通过添加考虑每个矿山当地条件的数据来改进预测输出。
