Assessing the Porosity-Binder Ratio and Machine Learning Models for Predicting the Strength and Durability of Soil-Cement-Glass Powder Geomaterial

This study evaluates the mechanical behavior and durability of a silty soil stabilized with Portland cement and recycled ground glass powder (<i>GGP</i>). The porosity-cement index (η/C_iv) was applied to predict unconfined compressive strength (qu), splitting tensile strength (qt), and accumulated mass loss (<i>ALM</i>) under wetting-drying cycles. Mixtures were prepared with cement contents of 3%, 6%, and 9%, GGP contents of 5%, 15%, and 30%, and dry unit weights of 13.5, 14.5, and 15.5 kN/m³, and were cured for 7, 28, and 90 days. The experimental program consisted of a large dataset, comprising 486 mechanical tests (unconfined compressive and splitting tensile strength) and 81 durability tests, providing a robust basis for both empirical modeling and machine learning analysis. The results confirmed a strong power-law relationship between η/<i>C</i>_iv and both qu and qt, achieving high coefficients of determination (<i>R</i>² > 0.98). The strength coefficient (A) increased consistently with curing time and <i>GGP</i> addition, indicating enhanced pozzolanic reactivity and matrix densification. After 90 days, qu increased by over 250% and qt by nearly 700%. Durability tests revealed exponential reductions in <i>ALM</i> with higher density and binder content, achieving values below 0.5% for the densest mixtures, which contained 30% <i>GGP</i>. These findings validate the η/<i>C</i>_iv index as an effective predictor of strength and durability in soil-cement-<i>GGP</i> geomaterials, establishing a solid basis for future integration with machine learning models. The implementation of twenty-eight machine learning presets for predicting qu, qt, and <i>ALM</i> demonstrated that the Matern 5/2 Gaussian Process Regression and the trilayered neural network are the most suitable algorithms, achieving R² values higher than 0.987 in both the validation and testing stages.