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

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 (qu) and small-strain shear modulus (Go) 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 (qu = 1550 kPa and Go = 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 qu (from 1390 to 1550 kPa) and Go (from 6220 to 6790 MPa). Likewise, at 7 days, increasing cement from 3% to 6% at 15% CAR produced significant gains in qu (207 to 693 kPa) and Go (2090 to 4120 MPa). The porosity–binder index showed strong correlations with qu (R2 = 0.94) and Go (R2 = 0.92). The ML models further improved accuracy, achieving R2 values of 0.99 for qu and 0.97 for Go. 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.