This study evaluates the mechanical behavior and durability of a silty soil stabilized with Portland cement and recycled ground glass powder (
GGP). The porosity–cement index (η/C
iv) was applied to predict unconfined compressive strength (
), splitting tensile strength (
), and accumulated mass loss (
ALM) 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/m
3, 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/
Civ and both
and
, achieving high coefficients of determination (
R2 > 0.98). The strength coefficient (A) increased consistently with curing time and
GGP addition, indicating enhanced pozzolanic reactivity and matrix densification. After 90 days,
increased by over 250% and
by nearly 700%. Durability tests revealed exponential reductions in
ALM with higher density and binder content, achieving values below 0.5% for the densest mixtures, which contained 30%
GGP. These findings validate the η/
Civ index as an effective predictor of strength and durability in soil–cement–
GGP geomaterials, establishing a solid basis for future integration with machine learning models. The implementation of twenty-eight machine learning presets for predicting
,
, and
ALM demonstrated that the Matern 5/2 Gaussian Process Regression and the trilayered neural network are the most suitable algorithms, achieving R
2 values higher than 0.987 in both the validation and testing stages.
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