一种边坡稳定性多参量辨识模型及应用

为实现对边坡稳定性高效、快速和准确评价，考虑影响边坡稳定性多种参量指标，其参量指标间存在一定多元共线性，易致边坡稳定性评价存在一定偏差。本研究通过引入一种主元分析（PCA）对影响边坡稳定性指标变量去相关和降维化，通过提取3个主元综合得分对边坡稳定性进行评价，经主元分析后的各指标相互独立，能够较好满足径向基神经网络（RBFNN）中高斯分布要求。在主元分析基础上，建立边坡稳定性评价RBFNN模型，并将其应用到我国32组典型边坡实测数据中，仿真结果表明：6种不同学习情况下，主元分析-RBFNN模型的误判率分别为6.25%、6.25%、6.25%、9.38%、15.62%和15.62%，同时对模型可能存在一些问题进行了讨论和检验。说明主元分析-RBFNN模型可为边坡稳定性评价提供一种新思路。

A Multi-parameter Identification Model for Slope Stability and Application

In order to achieve efficient, rapid and accurate evaluation of slope stability, multiple evaluation indicators that affect slope stability are considered. There is a certain degree of multivariate collinearity among the indicator variables, which may cause overlap of data and information, leading to certain errors in slope stability evaluation. . In this study, a principal component analysis (PCA) was introduced to decorrelate and reduce the dimensions of the slope stability-related variable data, and three comprehensive indicators were extracted to comprehensively evaluate the slope stability. After the principal component analysis, the indicators were mutually Independent, can better meet the Gaussian distribution requirements in the Radial Basis Neural Network (RBFNN). On the basis of principal component analysis, the slope stability evaluation RBFNN model is established and applied to 32 sets of typical slope measured data in China. The simulation results show that the principal component analysis-RBFNN model is wrong in 6 different learning situations. The judgment rates were 6.25%, 6.25%, 6.25%, 9.38%, 15.62%, and 15.62%, respectively. At the same time, the evaluation results were discussed and tested. It shows that the principal component analysis-RBFNN model can provide a new idea for slope stability evaluation