Dimensionality analysis in assessing the unconfined strength of lime-treated soil using machine learning approaches

Expansive soils pose significant challenges due to their tendency to swell when wet and shrink when dry, causing ground instability. These volumetric changes can lead to structural damage, including foundation cracks, uneven floors, and compromised infrastructure. Addressing these issues requires proper soil evaluation and the implementation of stabilization techniques to ensure long-term safety and durability. The high degree of expansive, problematic soil is stabilized by cement, bitumen, lime, etc. This investigation predicts the unconfined compressive strength (UCS) of lime-treated soil using decision tree (DT), ensemble tree (ET), gaussian process regression (GPR), support vector machine (SVM), and multilinear regression (MLR). This research investigates the impact of dimensionality on the computational approaches. The variance accounted for (VAF), correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and performance index (PI) metrics have computed the model's performance. The comparison reveals that model ET5 has predicted UCS with an excellent performance in testing (RMSE = 368.06 kPa, R = 0.9640, VAF = 91.60, PI = 1.8077) and validation (RMSE = 508.41 kPa, R = 0.9165, VAF = 83.89, PI = 1.6337) phase. Also, model ET5 has achieved better score (total = 90), area over the curve (testing = 8.98E-04, validation = 1.56E-03), computational cost (testing = 0.1772s, validation = 0.1551 s), uncertainty rank (= 1), and overfitting (testing = 2.32, validation = 2.80), presenting model ET5 as an optimal performance model. The dimensionality analysis reveals that simple models like MLR, SVM, GPR, and DT struggle with high-dimensional data (case 5). Still, the ET5 model achieves high performance and reliable prediction with consistency, compaction and soil physical parameters. Conversely, the effect of multicollinearity has been observed on the performance of the MLR, SVM, and DT models.