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.