基于最小二乘支持向量机的黄土湿陷性预测挖掘

运用数据挖掘技术进行了黄土湿陷性的预测挖掘，挖掘模型采用最小二乘支持向量机。建模过程中用主成份分析法进行数据的预处理，以剔除指标间的相关性，消除多指标信息冗余对挖掘模型的影响，并引入粒子群优化算法进行模型反演分析，确定最优参数。针对实际工程数据进行的预测挖掘表明：黄土的电阻率、剪切波速与土的结构特性、含水率、密度等指标密切相关，可较为全面地反映影响黄土湿陷性的因素；以电阻率、剪切波速及土层埋深作为模型的预测变量就可定量预测黄土的湿陷性；用所建模型和预测变量来预测黄土的湿陷性是可行的。

Prediction of loess collapsibility by using data mining based on least squares support vector machines

The data mining techniques are used to predict loess collapsibility in geotechnical engineering; and the mining model is constructed by using the least squares support vector machines. Using principal component analysis method, data of model is preprocessed to remove the correlation among the indicators and to eliminate the impact of multi-index redundant information on the mining model, and the model inverse analysis is administered by introducing particle swarm optimization algorithm to determine the optimal parameters. The forecast mining for the actual project data shows that loess resistivity and shear wave velocity is closely related to the soil indicators such as the soil structural properties, water content, density etc, they can be more comprehensively reflect the factors of impact of loess collapsibility. Using loess resistivity, shear wave velocity and soil depth as predicting variables of the model can quantitative predict the loess collapsibility; the proposed model is effective.