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

Mahmood Ahmad, Badr T. Alsulami, Ramez A. Al-Mansob, Saerahany Legori Ibrahim, Suraparb Keawsawasvong, Ali Majdi and Feezan Ahmad
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Mahmood Ahmad: Department of Civil Engineering, University of Engineering and Technology Peshawar (Bannu Campus), Bannu 28100, Pakistan
Badr T. Alsulami: Department of Civil Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah 24382, Saudi Arabia
Ramez A. Al-Mansob: Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak 50728, Selangor, Malaysia
Saerahany Legori Ibrahim: Department of Civil Engineering, Faculty of Engineering, International Islamic University Malaysia, Jalan Gombak 50728, Selangor, Malaysia
Suraparb Keawsawasvong: Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani 12120, Thailand
Ali Majdi: Department of Building and Construction Techniques Engineering, Al-Mustaqbal University College, Hilla 51001, Iraq
Feezan Ahmad: State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China

Mathematics, 2022, vol. 10, issue 19, 1-19

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 (R 2 ), mean absolute error (MAE), relative squared error (RSE), root mean square error (RMSE), relative root mean square error (RRMSE), performance indicator ( ? ), and visual framework (Taylor diagram). GPR is concluded to be the best performing model (R 2 , MAE, RSE, RMSE, RRMSE, and ? 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.

Keywords: resistance value; M5P; support vector machine; Gaussian process regression; expansive soil; subgrade (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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