Using Machine Learning Models and Numerical Algorithms for Estimating the Strength of Cemented Silt Through Porosity-Binder Index

One of the recent challenges in problematic-soil improvement is the proper definition of mix design parameters, particularly cement dosage, curing time, and compaction density. Although the porosity–cement ratio has been widely used to assess the strength behaviour of cemented soils, traditional empirical approaches often fail to capture the complex nonlinear interactions among soil structure, binder content, and curing conditions. In parallel, existing machine learning (ML) algorithms have shown significant potential for predicting the behaviour of geotechnical materials; however, most previous studies rely exclusively on data-driven models without incorporating physically meaningful parameters associated with cementation mechanisms. In this context, this study proposes a predictive framework for estimating the strength of silt stabilised with three types of hydraulic cement by integrating the porosity–cement index with ML algorithms and numerical optimisation methods. Initially, the mechanical response was correlated using the porosity–cement ratio with an exponent of 0.40. Subsequently, ML techniques were applied using cement type, cement content, soil content, dry density, and curing time as input variables. During the optimisation process, the exponent was refined to 0.423, remaining close to the conventionally obtained value. The results demonstrate that integrating the porosity–cement index with machine learning provides a reliable and physically consistent framework for predicting the strength behaviour of cemented silts, enabling a more rational and efficient mix design methodology for ground improvement applications.