Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Abstract

Soil compaction and related parameters play a pivotal role in the material selection process for earth constructions. Because of time limitations and attention to completion resources, the need to develop models for predicting compaction properties is felt more than ever (i.e., maximum dry unit weight (γ_dmax) and optimum moisture content (ω_opt)) using easily measured index properties. The main purpose of this study is to evaluate the usefulness of conventional SVR, ensembled (additive regression (AD-SVR)), and hybrid SVR (imperialist competitive algorithm (ICA-SVR)) models for predicting the maximum dry unit weight (γ_dmax) and optimum water content (ω_opt) related to proctor compaction test of lateritic soils, where the used dataset was collected from published literature from specific sites. Results depict that all developed models have acceptable efficiency in predicting the γ_dmax with R^2 larger than 0.844 and 0.719, and the ω_opt with R^2 larger than 0.732 and 0.714 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. The results of all SVR models were better than the literature, in which the hybrid ICA-SVR model outperformed other models by considering performance criteria. Overall, the hybrid ICA-SVR algorithm could get the most suitable results than others for estimating both γ_dmax and ω_opt. The ICA-SVR model outperforms other approaches and demonstrates the optimization algorithm's capacity throughout the optimization process.

Prediction of Soil Compaction Parameters Using Machine Learning Models

“…Hasnat et al [24] applied Support Vector Machine (SVM) to develop a model for predicting the compaction parameters of soil. Zhu et al [25] used the Support Vector Machine (SVM), Additive Regression Support Vector Machine (AD-SVM), and Imperialist Competitive Algorithm Support Vector Machine (ICA-SVM) to predict the compaction parameters of lateritic soils, and the comparison showed that the ICA-SVM algorithm outperformed the other two algorithms. Sinha et al [16] used Artificial Neural Network (ANN) to develop a predictive model for compaction parameters and permeability of soils.…”