基于主成分分析-Logistic模型的边坡稳定性分析

Slope Stability Analysis Based on Principal Components Analysis-Logistic Model

提出一种基于PCA-Logistic模型算法,并对边坡的稳定性进行预测分析。先用PCA对样本数据进行处理,有效控制稳定性因素的计算数量。选出边坡重度、内聚力、内摩擦角、边坡角4个主成成分进行Logistic建模分析。得出其Logistic回归模型,并运用于边坡稳定性实例分析,与用Logistic回归模型确定权重的模糊综合法进行对比。研究结果表明:PCA-Logistic模型能有效地处理样本数据,减少不必要的分析过程,计算得出的边坡稳定性状态与实际情况相符;并与Logistic回归模型确定权重的模糊综合法进行对比,验证其计算结果的准确性,高效性。该方法可在工程地质、采矿工程、经济等众多领域应用与推广。

A PCA-Logistic model algorithm is proposed,and the stability of the slope is predicted and analyzed.The PCA method is used to preprocess the sample data and control the calculation of the stability factors.The four principal components of rock weight,rock cohesion,internal friction angle and slope angle are selected for Logistic modeling analysis.The Logistic regression model is applied to slope stability analysis,and compared with the fuzzy comprehensive method of Logistic regression model.The results show that the PCA-Logistic model can effectively deal with the sample data and reduce the unnecessary analysis process.The calculated slope stability state is in accordance with the actual situation,and it is compared with the fuzzy synthesis method of Logistic regression model.The accuracy and efficiency of the method can be applied and popularized in many fields such as engineering geology,mining engineering,economy and so on.