## 1 Introduction  
Geotechnical engineers frequently encounter challenges when dealing with high-plasticity clay soils, known for their poor mechanical properties, including low strength,high compressibility, and pronounced volume changes [1]. These characteristics can lead to significant issues in construction and infrastructure projects, such as excessive settlement, instability, and reduced bearing capacity [2]. Soil stabilization techniques have become increasingly important in geotechnical engineering practice to address these challenges. Among various stabilization methods, lime and cement as admixtures have gained widespread attention due to their effectiveness in improving soil properties [3]. Line-cement stabilization can enhance the strength, reduce compressibility, and increase the durability of problematic soils [2]. However, determining the optimal proportions of lime and cement for specific soil types remains a complex task, often requiring extensive laboratory testing and field trials. Soil stabilization techniques play a crucial role in sustainable infrastructure development. By minimizing the need for costly soil replacement or deep foundations, line-cement stabilization contributes to resource conservation and environmental protection [4]. The study's findings provide insights into the eco-friendly use of lime and cement, promoting efficient soil utilization. The traditional approach to soil stabilization has relied predominantly on univariate analysis methods that examine relationships between individual parameters while holding others constant. Such approaches, however, fail to capture the multidimensional interactions characteristic of chemically stabilized soil systems [3]. The inherent complexity of these interactions necessitates more sophisticated multivariate analytical frameworks capable of elucidating the interdependencies among soil properties and their collective influence on mechanical performance. PCA and PCR offer powerful statistical tools to address these analytical challenges. PCA reduces the dimensionality of multivariate datasets by transforming potentially correlated variables into uncorrelated principal components that capture the essential variance structure of the original data [5]. PCR extends this transformation by utilizing these principal components as predictors in regression modeling, effectively addressing multicollinearity issues while providing insights into the underlying causal mechanisms [6]. These techniques have been successfully applied in various geotechnical applications, including landslide susceptibility assessment and soil classification, but their application to chemical soil stabilization remains limited. The application of PCR for sensitivity and causality analysis in this study represents a methodological advancement in understanding soil-stabilizer interactions. These analyses bridge traditional empirical approaches with data-driven modeling, enabling the optimization of stabilizer dosages while minimizing experimental trial-and-error. By isolating causal relationships from correlated variables, this approach advances the design of sustainable soil improvement strategies, ensuring resource efficiency and alignment with infrastructure durability requirements. The findings establish PCR as a versatile tool for decoding complex soil-chemical interactions, with broader applicability to geotechnical systems involving multifactorial variables. The integration of artificial intelligence (AI) into dynamic mechanical geotechnical applications has enabled sophisticated modeling of time-dependent soil behavior, nonlinear interactions, and complex multivariate systems [7]. Recent studies highlight AI's capacity to address challenges in predicting and optimizing soil stabilization, consolidation, and strength progression under varying environmental and loading conditions [8].