关键词: 边坡稳定, 基因表达式程序设计, 主成分分析, 预测, AIC信息准则, 模型选择

Abstract:

A novel gene expression programming (GEP) algorithm based on principal component analysis (PCA) is proposed and applied to predict slope stability. The PCA technology is utilized to preprocess the sample data and to reduce the input of prediction model, which thus improves the input factors and eliminates the correlation among the inputs. Then new sample data are input into gene expression to construct the prediction model. Applying the prediction model to predict the safety factors of 11 slope samples after learning other 71 samples, satisfactory result is obtained. Reserving the criteria that model judged by error value, Akaike’s information criterion (AIC)[12] is introduced; v-SVR algorithm, GA-BP network algorithm and PCA-GEP algorithm are compared respectively. The experimental results show the PCA-GEP algorithm is more accurate than v-SVR algorithm and GA-BP network algorithm. The engineering example indicates that the method is reasonable and feasible; and it provides a new idea for slope assessment. It can acquire slope safety factors quickly and accurately and evaluate the slope stability. It provides the decision basis for selecting economical slope design scheme.

Key words: slope stability, gene expression programming, principal component analysis, prediction, Akaike’s information criterion (AIC), model selection