Application of factor analysis and probabilistic neural network model on evaluation of the slope stability
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摘要: 边坡稳定性分析是一个复杂的系统工程问题，其评价直接影响边坡工程的安全性与经济性。为了实现对边坡稳定性的快速、高效和准确评价，需要考虑边坡稳定性多种评价指标，但指标间或多或少存在一定的相关性，从而导致参量信息重叠。文章提出一种因子分析方法对边坡稳定性相关指标数据进行降维处理，提取3个综合指标对边坡稳定性进行总体评价。因子分析后的指标彼此独立，能够满足概率神经网络（PNN）样本层中采用高斯函数作径向基函数的要求。在因子分析的基础上，建立边坡稳定性评价的PNN模型，将其应用于39个典型的边坡稳定性评价。预测结果表明：5种不同的训练和测试样本个数下PNN模型仍具有良好的预测效果，其正判率分别为100%、94.87%、94.87%、84.62%和84.62%，说明因子分析与PNN模型结合可为岩土工程中边坡稳定性评价提供了一种很好的思路。Abstract: Slope stability analysis is a complex problem concerning system engineering, and slope stability evaluation directly affects the safety and economical efficiency of slope engineering. In order to realize rapid, efficient and accurate evaluation of slope stability, multiple evaluation indexes should be considered which exist more or less correlations which lead to the overlapping of parameter information. An improved factor analysis method is proposed to reduce the dimensionality of the slope stability index data. Three new indexes are extracted to conduct an overall evaluation of slope stability. The indexes undergoing factor analysis are independent of each other and can meet the requirement of adopting the Gaussian function as the radial basis function in the sample layer of PNN. On the basis of factor analysis, the PNN model for slope stability evaluation is established, which is applied to 39 typical slope stability evaluations. The predicting results show that the PNN model still presents a favorable predictive effect under 5 different training and test samples, and the correct judging ratio is 100%, 94.87%, 94.87%, 84.62% and 84.62%, respectively, which verify the evaluation results of factor analysis on slope stability and indicate that the combination of factor analysis and PNN model can provide a good thinking for slope stability evaluation in geotechnical engineering.
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