# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 1 Introduction  
Contemporary urbanization and industrialization have profoundly impacted the field of geotechnical engineering, introducing new and complex challenges for infrastructuredevelopment [1; 2; 3]. As cities continue to expand, sites with naturally favorable soil conditions are becoming increasingly rare. Engineers are therefore often required to design and build on marginal or weak soils whose engineering properties must be improved to make them suitable for civil engineering applications [4]. This reality has transformed ground improvement techniques from occasional solutions into essential components of contemporary geotechnical practice. Among the various ground improvement technologies available, e.g., mechanical and thermal stabilization, grouting, freezing, drainage, preloading etc., chemical additive-based stabilization has emerged as one of the most versatile and cost-effective approaches for treating fine-grained soils with inadequate engineering properties [5; 6; 7]. One widely adopted method of chemical stabilization consists in mixing soil with Portland cement and water to produce soil-cement, a composite material whose properties are tailored by adjusting the proportions of its constituents [8; 9; 10]. The resulting mixture is subsequently compacted to achieve the required density and engineering performance. Cement stabilization offers distinct advantages that explain its widespread adoption in geotechnical practice. First, it demonstrates remarkable versatility, effectively treating diverse soil types from soft clays to granular materials [6; 8; 11]. Second, cement-treated soils exhibit rapid strength development, with significant improvements observable within days of treatment, enabling accelerated construction schedules [10; 12; 13; 14]. Third, the long-term durability of cement-stabilized soils has been well documented, with many projects showing sustained performance over decades [5; 15; 16]. Fourth, compared to alternative ground improvement methods, cement stabilization often provides superior cost-effectiveness, particularly for large-area treatments [8]. These advantages have established cement stabilization as a preferred solution for applications ranging from pavement subgrades to embankment foundations and slope stabilization projects [17; 18; 19; 20; 21]. However, despite these well-documented advantages and widespread applications, the field still lacks a universally accepted dosage methodology based on rational criteria. Unlike concrete technology, where the water/cement ratio serves as a fundamental predictor of target strength [22], soil-cement design currently relies on empirical approaches. The soil-cement ratio is typically determined through extensive laboratory testing to identify the minimum cement content required to achieve desired target properties, often unconfined compressive strength (UCS) [9; 11; 15; 17; 23]. This empirical, trial-and-error process likely reflects the complex behavior of soil-cement, which is influenced by numerous interacting factors including soil physicochemical properties, mineralogy, organic content, cement type and dosage, as well as porosity and moisture at compaction [14; 24]. Given that laboratory testing for cement-stabilized soil design is often time-consuming and resource-intensive, researchers have long sought more efficient alternatives for predicting UCS and other engineering properties. This need for efficiency has become increasingly urgent with growing environmental concerns regarding the substantial carbon footprint of cement production, which accounts for approximately 8% of global CO\({}_{2}\) emissions [25], emphasizing the importance of optimized dosage strategies that minimize both material consumption and ecological impact. These efforts have resulted in numerous empirical relationships attempting to correlate UCS with various parameters such as cement content, water-to-cement ratio, curing time, soil plasticity indices, and compaction characteristics. [PERSON] et al.