Machine learning-based prediction and performance study of transparent soil properties

Abstract

An indispensable process of geotechnical modeling with transparent soils involves analyzing images and soil property simulations. This study proposes an objective framework for quantitative analysis of the influential mechanism of three key factors, namely, different aggregate proportions (DAP), solvent ratio (SR), and solute solution ratio (SSR) on transparent soils' transparency and shear strength. 125 groups of transparent soil samples considering these three factors were prepared to investigate their impact on transparency and shear strength through Elastic Net regression. Spearman correlation analysis was performed for transparency and shear strength. Furthermore, by comparing the performance of XGBoost, GBDT, Random Forest, and SVR after hyperparameter tuning in predicting transparency and shear strength, XGBoost proved to be the optimal machine learning model with the lowest MSE of 0.0048 and 0.0306 and was innovatively adopted to analyze how various factors affect the transparency and shear strength, thus enhancing the interpretability of machine learning. A ranking system, according to the importance scores of XGBoost, shows that SSR was the most important factor affecting both shear strength and transparency of transparent soils, with importance scores being 0.45 and 0.57, respectively. Our study may shed light on the preparation and performance study of transparent soils.