煤巷围岩分类的Fisher判别分析法
Fisher Discriminant Analysis Method for Classifying Mine-lane Surrounding Rock
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摘要: 基于Fisher判别理论建立了煤巷围岩分类的Fisher判别分析（FDA）模型。选取巷道埋深、巷道跨度、采动影响系数，围岩强度、松动圈厚度和节理发育情况6个指标因子作为FDA模型的预测指标体系，以实测数据作为训练样本，获得了相应的判别函数。通过分析计算，去掉了重要性较弱的松动圈厚度1个指标，得到了4个判别函数。为了验证模型的有效性，首先利用15组实测数据作为学习样本对模型进行训练，采用回代估计法检验模型的有效性，回判的误判率为0，然后将建立的模型应用于同一地区的实际工程数据判别中，判别效果较好。结果表明，FDA模型简单、准确，是快速判别煤巷围岩分类的一种有效方法。
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关键词:
- 煤巷围岩 /
- 分类 /
- Fisher判别分析 /
- 松动圈 /
- 节理发育
Abstract: Based on Fisher discriminant theory, the Fisher Discriminant Analysis (FDA) model was established for classifying mine-lane surrounding rocks. Six factors such as roadway depth, roadway span, coefficient of mining influence, strength of surrounding rock, thickness of loose zone, situation of joint development were selected as the indicator system of the FDA mode. Discriminant function for classifying mine-lane surrounding rocks is obtained in accordance with the measured data as training samples. One weak indicator, which is thickness of loose zone, was removed from the indicator system through analysis, and then we got four discriminate functions. In order to verify the validity of the FDA model, 15 groups of measured data are used as samples to make training, the backward substitution method was adopted to verify the effectiveness of the model, with the misjudgment rate of 0, then, the FDA model was applied to engineering data in the same area, and the effect is good. The results show that the FDA model is simple and accurate; it is an effective method for the rapid identification of the classification of mine-lane surrounding rocks. -
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