A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil. (19th November 2021) - Pham, Binh Thai; Nguyen, Manh Duc; Al-Ansari, Nadhir; Tran, Quoc Anh; Ho, Lanh Si; Le, Hiep Van; Prakash, Indra
Abstract : Determination of the permeability coefficient ( K ) of soil is considered as one of the essential steps to assess infiltration, runoff, groundwater, and drainage in the design process of the construction projects. In this study, three cost-effective algorithms, namely, artificial neural network (ANN), support vector machine (SVM), and random forest (RF), which are well-known as advanced machine learning techniques, were used to predict the permeability coefficient ( K ) of soil (10 −9  cm/s), based on a set of simple six input parameters such as natural water content w (%), void ratio ( e ), specific density (g/cm 3 ), liquid limit (LL) (%), plastic limit (PL) (%), and clay content (%). For this, a total of 84 soil samples data collected from the detailed design stage investigations of Da Nang-Quang Ngai national road project in Vietnam was used to generate training (70%) and testing (30%) datasets for building and validating the models. Statistical error indicators such as RMSE and MAE and correlation coefficient ( R ) were used to evaluate and compare performance of the models. The results show that all the three models performed well ( R  > 0.8) for the prediction of permeability coefficient of soil, but the RF model (RMSE = 0.0084, MAE = 0.0049, and R  = 0.851) is more efficient compared with the other two models, namely, ANN (RMSE = 0.001, MAE = 0.005, and R   =  0.845) and SVM (RMSE = 0.0098, MAE = 0.0064, and R  = 0.844). Thus, it can be concluded that the RF model can be used for accurate estimation of the permeability coefficient ( K ) of the soil.