Comparative Evaluation of Lime–NaCl Catalyzed and Xanthan Gum–Fiber Reinforced Soil Stabilization: Experimental and Machine Learning Assessment of Strength and Stiffness

The sustainable stabilization of clayey soils has become a critical strategy for improving their mechanical performance while reducing environmental impact. This study compares two distinct stabilization systems applied to the same low-plasticity clay (CL) from Cartagena de Indias, Colombia: (i) lime catalyzed with sodium chloride (NaCl) and (ii) xanthan gum (XG) reinforced with polypropylene fibers (PPF). A series of laboratory tests was performed to evaluate the unconfined compressive strength (qu) and small-strain stiffness (Go) of both systems under controlled compaction and curing conditions. The lime–NaCl system demonstrated accelerated early-age strength and stiffness development, reaching qu values above 2.5 MPa and Go exceeding 10 GPa after 28 days of curing, mainly attributed to enhanced pozzolanic reactions catalyzed by NaCl. Conversely, the XG–PPF blends exhibited progressive improvements in mechanical performance, achieving notable gains after 90 days due to the polymeric bonding of XG and the fiber–matrix reinforcement that enhanced ductility and post-peak behavior. When normalized through the porosity–binder index, both systems exhibited power-law trends, with the lime–NaCl mixtures displaying higher exponents indicative of cementation-controlled behavior, while the XG–PPF mixtures showed lower exponents consistent with interparticle bonding and network formation. These results highlight the complementary mechanisms of chemical and biopolymeric stabilization, providing insights into the selection of sustainable binders tailored to specific design requirements in tropical clays. This research demonstrated that the implementation of machine learning models enhanced the fitting accuracy of the two soil stabilization methods when compared with traditional mathematical regression models commonly used in geotechnical engineering. Among the tested approaches, the neural network and Gaussian process regression models exhibited the best performance, achieving R2 values ranging from 0.917 to 0.980 during the validation stage.