Neuronet-Based Soil Chemical Stabilization Model

Adding chemical agent to stabilize problematic highway subgrade soil is a common engineering practice in the United States. Due to the fact that theoretical accomplishments in soil chemical stabilization lag far behind the engineering practice, laboratory testing, which is expensive and time-consuming, is almost always necessary to determine the effectiveness of the soil stabilizer in enhancing engineering properties of the soil. Over the years, large amount of valuable data from laboratory tests on stabilizing different soils with different chemical stabilizers was accumulated in the literature. Efforts to extract the relationships and associations from the existing test data in order to provide guidance for new soil chemical stabilization cases were carried out for many years, however, due to the technology (statistic regression) limitations, reliable models are still not available. In this paper, Neuronet (NN) approach to study soil chemical stabilization was introduced. NN model to predict the unconfmed compression strength (UCS) of the stabilized soil was built based on the experimental data from stabilizing three representative Kansas embankment soils with five chemical stabilizers. The results showed that the trained NN model could precisely predict the UCS of stabilized soil. Furthermore, NN model enables us to study the significance of each input factor, thus providing a powerful tool for optimizing the mixture and construction design.