Predicting Subgrade Resistance Value of Hydrated Lime-Activated Rice Husk Ash-Treated Expansive Soil: A Comparison between M5P, Support Vector Machine, and Gaussian Process Regression Algorithms

Abstract

Resistance value (R-value) is one of the basic subgrade stiffness characterizations that express a material’s resistance to deformation. In this paper, artificial intelligence (AI)-based models—especially M5P, support vector machine (SVM), and Gaussian process regression (GPR) algorithms—are built for R-value evaluation that meets the high precision and rapidity requirements in highway engineering. The dataset of this study comprises seven parameters: hydrated lime-activated rice husk ash, liquid limit, plastic limit, plasticity index, optimum moisture content, and maximum dry density. The available data are divided into three parts: training set (70%), test set (15%), and validation set (15%). The output (i.e., R-value) of the developed models is evaluated using the performance measures coefficient of determination (R2), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator (ρ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R2, MAE, RSE, RMSE, RRMSE, and ρ equal to 0.9996, 0.0258, 0.0032, 0.0012, 0.0012, and 0.0006, respectively, in the validation phase), very closely followed by SVM, and M5P. The application used for the aforementioned approaches for predicting the R-value is also compared with the recently developed artificial neural network model in the literature. The analysis of performance measures for the R-value dataset demonstrates that all the AI-based models achieved comparatively better and reliable results and thus should be encouraged in further research. Sensitivity analysis suggests that all the input parameters have a significant influence on the output, with maximum dry density being the highest.

Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression

“…Researchers have used a different percentage of the available data as the training set for different problems. For instance, Ahmad et al 34 used 70% for training and remaining 30% was equally divided into testing and validation sets. In this study, training dataset contains 85 (70%) observations while testing and validation comprises of 18 (15%) observations each.…”

“…The mathematical expressions are given in Eqs. ( 4 )–( 9 ) 30 , 34 , 48 – 57 . where and are the n th measured and predicted output of the sample, respectively.…”