Impact resistance of rammed earth: Comparative analysis of cement and alkali-activated slag stabilizers with machine learning predictions

Abstract

This study investigates the impact resistance of rammed earth (RE) materials, unstabilized and stabilized with cement and alkali-activated slag (AAS). Seven RE mix designs were developed, including one unstabilized reference mixture, four cement-stabilized mixtures containing 2.5 wt.%–10 wt.% ordinary Portland cement (Type II), and two AAS-stabilized mixtures incorporating 7.5 wt.% and 10 wt.% ground-granulated blast-furnace slag activated by an alkaline solution of sodium silicate and sodium hydroxide (Na₂SiO₃:NaOH = 2:1 by mass, 12 mol/L NaOH, activator-to-slag ratio = 0.8). All mixes were evaluated for their impact resistance, and physical and mechanical properties. Microstructural analyses were conducted using scanning electron microscopy and X-ray diffraction to characterize the internal structure of the materials. The results demonstrate that unstabilized RE exhibits poor impact resistance, failing upon the initial impact during drop-weight tests. Incorporating stabilizers significantly enhances impact resistance, with AAS-stabilized mixtures outperforming cement-stabilized counterparts. Specifically, AAS mixtures showed a 35% improvement in impact resistance compared to cement-stabilized mixtures with the same binder content. Machine learning models, support vector regression, random forest, extreme gradient boosting (XGB) and light gradient boosting, have demonstrated the ability to predict impact resistance based on mechanical properties, including compressive strength, tensile strength, elastic modulus, toughness, density, and water absorption. XGB, in particular, achieved a high coefficient of determination (R²) of 0.99 for training data and 0.95 for test data, indicating a strong correlation between these mechanical properties and impact resistance. The study underscores the potential of AAS as a viable alternative to cement in stabilizing RE, contributing to the development of more sustainable construction practices.

Graphical abstract

Keywords

impact resistance / RE / AAS / cement / sustainable construction / machine learning

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