A hybrid computational intelligence approach for predicting soil shear strength for urban housing construction: a case study at Vinhomes Imperia project Hai Phong city (Vietnam)

摘要

This research proposes an alternative for estimating shear strength of soil based on a hybridization of Support Vector Regression (SVR) and Particle Swarm Optimization (PSO). SVR is used as a function approximation method for making prediction of the soil shear strength based on a set of twelve variables including sample depth, sand content, loam content clay content, moisture content, wet density, dry density, void ratio, liquid limit, plastic limit, plastic index, and liquid index. The hybrid framework, named as PSO-SVR, relies on PSO, as a metaheuristic, to optimize the training phase of the employed function approximator. A data set consisting of 443 soil samples associated with the experimental results of shear strength has been collected from a housing project in Vietnam. This data set is then used to train and verify the performance of the PSO-SVR model specifically constructed for shear strength estimation. The hybrid model has achieved a good modeling outcome with Root Mean Square Error (RMSE) = 0.038, Mean Absolute Percentage Error (MAPE) = 9.701%, and Coefficient of Determination (R~2) = 0.888. Hence, the PSO-SVR model can be a potential alternative to be participated in the design phase of high-rise housing projects.