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/cm3), 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.
Highlights
Binh Thai Pham,1,2 Manh Duc Nguyen,3 Nadhir Al-Ansari,4 Quoc Anh Tran,5 Lanh Si Ho,1,2 Hiep Van Le,1 and Indra Prakash 6
E liquid limit is from 18.9% to 88.93%, the plastic limit is from 12.2% to 54.8%, and the clay content is from 5.7% to 64%
We evaluated the importance of the input parameter by using the Relief F technique for the six input parameters including the water content, void ratio, specific density, liquid limit, plastic limit, and clay content (Table 2). e clay content was found to be the less important variables of the permeability with the weight value of merely 0.025. e weights of the other index parameters including plastic limit, liquid limit, and specific density are 0.0753, 0.0762, and 0.0877, respectively
Summary
Binh Thai Pham ,1,2 Manh Duc Nguyen, Nadhir Al-Ansari ,4 Quoc Anh Tran, Lanh Si Ho ,1,2 Hiep Van Le, and Indra Prakash 6. In geotechnical point of view, the soil permeability depends on many factors such as the soil density, water content, void ratio, mineralogy, soil structures, and others. Us, many authors have tried to establish empirical relationships between influencing factors with the permeability coefficient [3,4,5]. Ere are several direct relationships between grain size and the permeability coefficient of soil. Mathematical Problems in Engineering indicated that the permeability is proportional to the square of the effective grain size for the sand with uniform particles. Other authors proposed a regression that considers porosity, percentage of clay, and sand particle to estimate the permeability of soil [7]. Some other authors predicted soil permeability based on bulk density and grain-size particle and shape of the particle [8, 9]. The permeability of soil is strongly dependent on the particle size distribution; it is not applicable for a wide range of soil [1, 10]. e study indicated that these empirical relationships have certain limitations as well as uncertainties
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