## 1 Introduction
Dynamic mechanical behavior, for example, research by [PERSON], [PERSON] [9] applied long short-term memory (LSTM) networks to predict UCS in GGBS-stabilized soils, capturing temporal lag effects and hydration kinetics with 95% accuracy. Similarly, functional PCA (FPCA) was used to analyze seasonal temperature effects on frozen soil strength, revealing time-dependent relationships between porosity and compressibility [10]. The current study's PCR framework corroborates these findings, showing that lime-cement interactions reduce void ratio and porosity over 28 days, with compressibility parameters dominating variance at later curing stages. This mirrors trends in slag-cement systems, where prolonged curing enhances microstructural densification. [PERSON], [PERSON] [11] applied convolutional neural networks (CNNs) to SEM images of cement-stabilized clays, correlating pore geometry with compressibility modulus improvements [12]. Topological data analysis (TDA) mapped persistent homology features in GGBS-treated soils, linking microstructural stability to pre-consolidation stress [13]. These studies highlight the need for integrating imaging data into AI models, a direction the current work's PCR framework could expand by incorporating XRD or SEM-derived metrics to enhance predictive granularity. AI models increasingly incorporate uncertainty quantification (UQ) to address variability in geotechnical systems. Polynomial chaos expansions (PCE) and Bayesian networks have been used to model moisture-induced UCS variability, explaining 32% of predictive variance in lime-treated soils [14]. While nonlinear models like ANNs report higher accuracy, they obscure mechanistic pathways. PCR, however, explicitly traces relationships between stabilizer content and UCS, as seen in the current study's identification of PC1's inverse correlation with void ratio and porosity. This interpretability is critical for translating lab findings to field applications, a challenge noted in studies using \"black-box\". Multiscale modeling frameworks integrating macro- and micro-level data are gaining traction. [PERSON] et al. developed physics-informed neural networks (PINNs) to simulate lime-cement stabilization, embedding [PERSON]'s consolidation theory into ML architectures to enhance accuracy [15]. Despite significant advancements, conventional analytical approaches have generally failed to elucidate the underlying causal mechanisms governing strength development in stabilized soils. The complex interdependencies among void ratio, porosity, compression characteristics, and strength parameters necessitate more sophisticated analytical frameworks capable of extracting meaningful patterns from multidimensional datasets. This study aims to investigate the stabilization of high-plasticity clay soil using various combinations of lime (0-30%) and cement (0-8%) to optimize consolidation and strength properties. Through the integration of experimental testing and multivariate statistical analysis, we seek to: (1) characterize the effects of lime-cement combinations on key geotechnical parameters including void ratio, porosity, compression index, pre-consolidation stress, and unconfined compressive strength (UCS); (2) employ PCA to identify the principal components that capture the essential variance in these parameters; and (3) quantify the relative influence of soil properties on UCS through PCR sensitivity and causality analyses. Optimal stabilization (15% lime + 6% cement) yielded UCS increase, void ratio reduction, and higher pre-consolidation stress and compressibility modulus. PCR identified compressibility parameters (void ratio, porosity, compression index) as explaining 46% of UCS variance.