A Comparison of Gaussian Process and M5P for Prediction of Soil Permeability Coefficient

Abstract

The permeability coefficient (k) of soil is one of the most important parameters affecting soil characteristics such as shear strength or settlement. Thus, determining soil permeability coefficient is very crucial; however, a field test for determining this parameter is difficult, time-consuming, and expensive. In this study, soft computing methods, namely, M5P and Gaussian process (GP), for estimating the permeability coefficient were constructed and compared. The results of this paper indicate that the two soft computing algorithms functioned well in predicting k. These two methods gave high accuracy of prediction capability. The determination coefficient of M5P (R2 = 0.766) was higher than that (R2 = 0.700) of GP. This implies that the M5P model is more reliable estimation than the GP model in predicting soils’ permeability coefficient (k). This proves that applying these machine learning techniques can provide an alternative for predicting basic soil parameters, including the permeability coefficient of soil.

Novel Approach to Predicting Soil Permeability Coefficient Using Gaussian Process Regression

“…The dataset comprises of 84 soil samples obtained from detailed design state investigations of the Da Nang-Quang Ngai expressway development project near Da Nang, central Vietnam (Figure 1) and is reported in the research work of Pham et al [28] (see Appendix A for complete dataset). Further details about the collection, testing and type of soils can be found in the Pham et al [28] reference.…”

“…The dataset comprises of 84 soil samples obtained from detailed design state investigations of the Da Nang-Quang Ngai expressway development project near Da Nang, central Vietnam (Figure 1) and is reported in the research work of Pham et al [28] (see Appendix A for complete dataset). Further details about the collection, testing and type of soils can be found in the Pham et al [28] reference. Previous studies show that the coefficient of soil permeability is a function of the liquid limit LL (%), plastic limit PL (%), clay content CC (%), void ratio e, natural water content w (%), and specific density γ (g/cm 3 ) [1,28].…”

“…Further details about the collection, testing and type of soils can be found in the Pham et al [28] reference. Previous studies show that the coefficient of soil permeability is a function of the liquid limit LL (%), plastic limit PL (%), clay content CC (%), void ratio e, natural water content w (%), and specific density γ (g/cm 3 ) [1,28]. It has been widely accepted, among researchers, that the input factors selected by Pham et al [1,28] constitute a complete and suitable set to estimate "k".…”

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].…”