Integrating the Porosity/Binder Index and Machine Learning Approaches for Simulating the Strength and Stiffness of...

This study evaluates the mechanical performance and predictive modeling of fine-grained soils stabilized with crushed aggregate residue (CAR) or crushed limestone waste (CLW) and Portland cement by integrating the porosity-binder index (η/Civ) and Machine Learning (ML) techniques. Laboratory testing included unconfined compressive strength (q_u) and small-strain shear modulus (G_o) measurements on mixtures containing 15% and 30% CAR and 3% and 6% cement, compacted at dry unit weights between 1.69 and 1.81 g·cm^-3 and cured for 7 and 28 days. Results revealed that strength and stiffness increased significantly with both cement and CAR contents. The mixture with 30% CAR and 6% cement exhibited the highest mechanical performance at 28 days (q_u = 1550 kPa and G_o = 6790 MPa). When mixtures are compared within the same curing period, the role of CAR and cement becomes evident. At 28 days, increasing CAR from 15% to 30% led to a moderate rise in q_u (from 1390 to 1550 kPa) and G_o (from 6220 to 6790 MPa). Likewise, at 7 days, increasing cement from 3% to 6% at 15% CAR produced significant gains in q_u (207 to 693 kPa) and G_o (2090 to 4120 MPa). The porosity-binder index showed strong correlations with q_u (R² = 0.94) and G_o (R² = 0.92). The ML models further improved accuracy, achieving R² values of 0.99 for q_u and 0.97 for G_o. Although the index already performed well, the additional gain provided by ML is meaningful because it reduces prediction errors and better captures nonlinear interactions among mixture variables. This results in more reliable estimates for mix design, confirming that the combined use of η/Civ and ML offers a robust framework for predicting the behavior of soil-cement-CAR mixtures.