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Bayes discriminant analysis method for predicting the stability of open pit slope

机译:露天矿边坡稳定性预测的贝叶斯判别分析方法

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A method to forecast the stability of open pit slope by using the Bayes discriminant analysis theory is presented in this paper. The Bayes discriminant analysis theory was introduced firstly. Then considering the mining circumstances and geological conditions of open pit slope, six factors reflecting the stability of open pit slope, including the magnitude of unit weight, angle of internal friction, cohesion, slope angle, slope hight and pore pressure ratio, were selected to establish a BDA model. 33 samples of open pit slope were used as the training and forecasting samples. The prior probability of each collectivity was obtained according to the ratio of training samples and re-substitution method was also introduced to verify the stability of model. Compared with the support vector machine (SVM) method, the results show that this Bayes discriminant analysis model has excellent performance, high prediction accuracy and can be used in practical engineering.
机译:提出了一种利用贝叶斯判别分析理论预测露天矿边坡稳定性的方法。首先介绍了贝叶斯判别分析理论。然后,考虑露天矿山边坡的开采环境和地质条件,选择了反映露天矿山边坡稳定性的六个因素,包括单位重量大小,内摩擦角,内聚力,边坡角,边坡高和孔隙水压力比。建立一个BDA模型。 33个露天矿边坡样本被用作训练和预测样本。根据训练样本的比例获得每个集合的先验概率,并引入重替代法来验证模型的稳定性。与支持向量机(SVM)方法相比,结果表明该贝叶斯判别分析模型具有良好的性能,较高的预测精度,可用于实际工程中。

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