Bagging and Multilayer Perceptron Hybrid Intelligence Models Predicting the Swelling Potential of Soil

Abstract

Seasonal variations of the moisture content of fine-grained soils may result in the accumulation of significant volumetric strains, which may affect the stability of geotechnical infrastructure. Predicting the swelling potential of soil may therefore be crucial to geotechnical infrastructure integrity. This research presents a series of machine learning models namely Gaussian Process (GP), Multilayer Perceptron (MLP), and Bagging-MLP neural network models that developed for the prediction of the soil’s swelling potential. A data driven approach based was based on site specific data from residual soils in the Mong Cai-Van Don expressway in Vietnam. The analysis involved simple soil index indicators i.e the particle size distribution, Atterberg limits, optimum dry density, in order to determne the swelling potential of the soil. The experimental database was then used to train and develop Bagging and Multilayer Perceptron Hybrid Intelligence models for the prediction of the soil’s swelling potential. The results show that the model performance in this area of geotechnical engineering performed with the highest prediction accuracy obtained using the Bagging-MLP model. The results of the study show promising steps towards a data centric approach in order to support geotechnical design.