Correlations between physical and mechanical property indexes of Shanghai soil based on support vector machine

Abstract

Correlation analysis of physical and mechanical indexes of Shanghai soil was carried out. Using the support vector machine algorithm, the authors constructed a correlation analysis model of soil plasticity index, liquidity index and compressibility coefficient based on the soil indoor test data obtained from several engineering sites. Then the model parameters were optimized by combining the error indexes. Comparing the results of support vector machine model with those of traditional linear and polynomial fitting methods, it was shown that the prediction results of the model are basically consistent with the actual results, and another advantage of model is that it can carry out deeper mining from more data to improve its robustness. Considering the engineering properties of different category soils are quite different, the authors further analyzed the performance and applicability of model, and established the relationship curve between the forecast bias of each testing sample and its physical indexes. The results indicate that the error of medium compressible soil is smaller than high compressible soil, and the model is more stable and accurate, which could provide a reference for the research of soil compressibility in Shanghai.

Keywords

• support vector machine
• correlation analysis
• compressibility coefficient
• liquidity index
• plasticity index