Predicting areas of increased propensity for roof deformation is crucial for the proactive management of geotechnical risk in underground coal mines. Current practices rely largely on assessing rock mass strength or characterization indices in isolation. Validation of applied ground support systems typically, and in part, comprises a review of observed deformation and analysis of extensometer data from adjacent previously mined areas. This information is seldom routinely assessed relative to the rock mass strength or characterization data. Typically, roof deformation is a response to several combining factors such as mining-induced stress, roof lithology, rock mass strength, and roadway geometry. Therefore, it is optimal to develop an approach to integrate these factors to quantify their relative significance to causing roof deformation, with the goal of establishing a reliable quantitative model for predicting roof deformation.

中文翻译：

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使用机器学习优化煤矿顶板表征模型


预测顶板变形倾向增加的区域对于地下煤矿岩土风险的主动管理至关重要。目前的实践很大程度上依赖于单独评估岩体强度或特征指标。所应用的地面支撑系统的验证通常部分包括对观察到的变形进行审查以及对来自相邻先前开采区域的引伸计数据进行分析。很少对岩体强度或特征数据相关的信息进行常规评估。通常，顶板变形是对多种综合因素的响应，例如采矿引起的应力、顶板岩性、岩体强度和巷道几何形状。因此，最好开发一种方法来整合这些因素，以量化它们对引起屋顶变形的相对重要性，以建立可靠的预测屋顶变形的定量模型。