Application Of Genetic Arithmetic And Support Vector Machine In Prediction Of Compression Index Of Clay

The compression index is an important soil property that is essential to many geotechnical designs. As the determination of the compression index from consolidation tests is relatively time-consuming. Support Vector Machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. Considering the fact that parameters in SVM model are difficult to be decided, a genetic SVM was presented in which the parameters in SVM method are optimized by Genetic Algorithm (GA). Taking plasticity index, water content, void ration and density of soil as primary influence factors, the prediction model of compression index based on GA-SVM approach was obtained. The results of this study showed that the GA-SVM approach has the potential to be a practical tool for predicting compression index of soil.