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作 者:耿元玲 黄德镛[1] 杜志锦 杨洋[1] 郭琦 GENG Yuanling;HUANG Deyong;DU Zhijin;YANG Yang;GUO Qi(School of Land and Resources Engineering,Kunming University of Science and Technology,Kunming 650093,China)
机构地区:[1]昆明理工大学国土资源工程学院,昆明650093
出 处:《有色金属(矿山部分)》2021年第3期22-27,共6页NONFERROUS METALS(Mining Section)
基 金:国家自然科学基金资助项目(U1602232)。
摘 要:围岩稳定性对于矿山巷道的安全至关重要,为提高围岩稳定性评价的准确率和减少评价过程中的繁琐操作,提出因子分析与邻近算法耦合,建立因子分析-邻近算法模型(FA-KNN模型),并将该模型用于对围岩稳定性的评价中。选取影响围岩稳定性的6个指标,因子分析对原始变量处理可得到3个相互独立的公共因子,再利用邻近算法对3个公共因子指标计算得到各个测试样本的类别。该模型在因子分析的基础上对围岩等级进行判别,其判别结果与实际情况一致,准确率为100%,而KNN模型、BP神经网络、PCA-RF模型的评价准确率分别为50%、70%、90%,FA-KNN模型相较于其它模型有更好的预测评价效果。研究表明:因子分析-邻近算法模型具有高效、准确的特点,对围岩稳定性的评价判别有一定的指导意义。The stability of surrounding rock is very important to the safety of mine roadways.In order to improve the accuracy of surrounding rock stability evaluation and reduce the tedious operation in the evaluation process,this paper proposed to couple factor analysis and proximity algorithm,established a factor analysis-proximity algorithm model(FA-KNN model),and used this model for surrounding rock evaluation of rock stability.This paper selected six indicators that affect the stability of surrounding rock,and factor analysis can process the original variables to obtain three mutually independent common factors,and then used the neighbor algorithm to calculate the three common factor indicators to obtain the category of each test sample.The model judged the grade of surrounding rock on the basis of factor analysis,and the judgment result was consistent with the actual situation,and the accuracy rate was 100%.The evaluation accuracy rates of KNN model,BP neural network,and PCA-RF model were 50%,70%,90%,respectively.FA-KNN model has better prediction and evaluation effect than other models.The research results show that:the factor analysis-neighboring algorithm model has the characteristics of high efficiency and accuracy,and has certain guiding significance for the evaluation and judgment of the stability of surrounding rock.
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