# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 1 Introduction
[22] demonstrated that the void/cement ratio, rather than the traditional water/cement ratio, provides the most appropriate parameter for assessing UCS in unsaturated soil-cement mixtures. [PERSON] and [PERSON] [26] successfully developed multiple regression models incorporating eight independent parameters, achieving an R\({}^{2}\) of 0.96 for UCS prediction. [PERSON] et al. [27] evaluated cement-treated marine clay across a broad range of mix ratios and curing periods, establishing power function correlations for cement and water content effects, and developing a comprehensive strength-prediction model that incorporates clay type through the plasticity index. [PERSON] et al. [28] investigated the time-dependent development of strength properties, establishing practical correlations for predicting UCS and resilient modulus as functions of curing time and basic soil properties. [PERSON] et al. [29] employed evolutionary polynomial regression (EPR) to develop predictive models for both the UCS and Young's modulus of lime and cement stabilized clayeve subgrade soil, achieving R\({}^{2}\) values of 0.95-0.96 for cement-stabilized specimens. [PERSON] and [PERSON] [30] advanced this work by calibrating numerical relationships to both backcast and forecast mechanical properties of chemically stabilized soils, achieving prediction accuracies within 89-99% of actual UCS values. While these empirical models provide valuable insights into the fundamental relationships governing strength development, they often exhibit limited predictive capability when applied beyond their development datasets. The inherent complexity of soil-cement interactions, characterized by non-linear relationships and interdependencies among multiple variables, frequently exceeds the modeling capacity of traditional empirical approaches [12; 24; 31]. This limitation has prompted the geotechnical community to explore more sophisticated computational methods, particularly machine learning (ML) techniques, which offer the potential to capture these complex, multi-dimensional relationships more effectively than conventional empirical models. The adoption of machine learning approaches in geotechnical engineering has provided powerful new tools for capturing the complex, multi-dimensional relationships in soil behavior that traditional empirical models struggle to represent. While the fundamental mechanisms of soil-cement interaction remain unchanged, ML techniques offer unprecedented accuracy in predicting engineering properties by learning directly from experimental data patterns. [PERSON] et al. [32] presented a comprehensive review of artificial neural networks (ANNs) in predicting the geomechanical properties of stabilized clays, highlighting their robust handling of large, complex datasets and their superior accuracy for non-linear modeling tasks. This foundational work established that ML techniques could consistently outperform traditional regression models by capturing intricate patterns in data without requiring predetermined functional forms. The successful application of machine learning in geotechnical engineering has encouraged researchers to explore a diverse range of algorithms for UCS prediction. Among neural network applications, [PERSON] and [PERSON] [33] investigated the viability of ANN models for predicting the UCS of geopolymer-stabilized clays, demonstrating improved accuracy over multivariable regression and providing useful sensitivity analysis tools. [PERSON] et al. [34] established that ANNs can consistently outperform traditional regression models for UCS prediction, while [PERSON] and [PERSON] [35] and [PERSON] et al. [36] each demonstrated that ANN models achieve high predictive accuracy and practical utility for stabilized and rammed earth mixes, facilitating the design of optimized and sustainable soil-cement compositions. [PERSON] et al. [37] compared Gradient Boosting, ANNs, and support vector machines (SVMs) for UCS prediction in cement-stabilized soils, identifying ANNs as delivering the highest accuracy.