A Comparative Study of Soft Computing Models for Prediction of Permeability Coefficient of Soil

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.

Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

“…As previously stated, soil permeability is greatly influenced by particle size distribution; nevertheless, this is not true for all soils [9,14]. These empirical relationships include limitations and uncertainties, according to Pham et al [1] study.…”

“…One of the most essential variables governing soil's fluid-flow characteristics is its permeability. The importance of determining the soil permeability coefficient is widely acknowledged, and is affected by a variety of parameters, including mineralogy, soil density, soil structures, water content, void ratio, and others [1]. Ganjidoost et al [2] reported that three category factors remarkably affect the soil permeability coefficient, namely, permeable soil parameters (density, clay content, viscosity etc.…”

“…Correlating other soil engineering parameters using an empirical equation to estimate "k" may not be correct [5,6,27]. Therefore, the aims of this study are (1) to develop new improved prediction models based on the Gaussian process regression (GPR) on Da Nang-Quang Ngai expressway development-project-site soil by using six soil parameters, such as liquid limit LL (%), plastic limit PL (%), clay content CC (%), void ratio e, natural water content w (%), and specific density γ (g/cm 3 ) as inputs; (2) to divide data into training and testing datasets with due attention to statistical aspects such as the minimum, maximum, mean and standard deviation of the datasets. The splitting of the datasets is performed to find out the predictive ability and generalization performance of developed models and later helps in better evaluating them; (3) to compare the proposed models to the reference models used in the published literature; and (4) to investigate the importance and impact of each input parameter on the soil permeability coefficient.…”

Hybrid Model: Teaching Learning-Based Optimization of Artificial Neural Network (TLBO-ANN) for the Prediction of Soil Permeability Coefficient

“…e results of this study are compared with the results of the studies of Pham et al [12,17]. ey have used the following ML models: M5P, GP, ANN, SVM, and RF to predict the k-value with the same 06 types of parameters as inputs at the Da Nang-Quang Ngai Expressway project (Table 2).…”

“…erefore, artificial intelligence (AI) or machine learning (ML) methods have been developed in recent decades to accurately predict the k-value of the soil and to reduce cost and time using limited geotechnical parameters. Such methods include artificial neural network (ANN) [11][12][13][14], adaptive neural fuzzy system (ANFIS) [15,16], and hybrid optimization models of genetic algorithms with adaptive neural fuzzy inference system (GA-ANFIS) [15], support vector machine (SVM), random forest (RF) [12], M5P, and Gaussian process (GP) [17].…”