Performance Prediction and Optimization of High-Plasticity Clay Lime–Cement Stabilization Based on Principal Component Analysis and Principal Component Regression

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High-plasticity clay soils pose significant challenges in geotechnical engineering due to their poor mechanical properties, such as low strength and high compressibility. Lime–cement stabilization offers a sustainable solution, but optimizing additive proportions requires advanced analytical approaches to decipher complex soil-stabilizer interactions. This study investigates the stabilization of high-plasticity clay soil (CH) sourced from Kano, Nigeria, using lime (0–30%) and cement (0–8%) for thirty (30) sample combinations to optimize consolidation and strength properties. Geotechnical laboratory tests (consolidation and UCS) were evaluated per ASTM standards. Multivariate analysis integrated principal component analysis (PCA) with regression modeling (PCR) for sensitivity and causality assessment. Optimal stabilization (15% lime + 6% cement) significantly improved soil properties: void ratio reduced by 58% (0.60→0.25), porosity by 49.5% (0.38→0.19), UCS increased by 222.5% to 2670 kPa (28 days), preconsolidation stress by 206% (355.63→1088.92 kPa), and compressibility modulus by 16% (7048→10,474.28 kPa). PCR sensitivity analysis attributed 46% of UCS variance to PC1 (compressibility parameters: void ratio, porosity, compression index; β = 0.72). PCR Causality analysis shows improvment with curing (R2: 68.7% at 7 days→83.0% at 28 days; RMSE: 11.2→7.8 kPa). PCR establishes compressibility reduction as the dominant causal mechanism for strength gain, providing a robust framework for dosage optimization beyond empirical approaches.