Compressive strength of biopolymer-stabilized residual granitic soil using polybutylene succinate and xanthan gum: A mechanical-microstructural study

The biodegradable polybutylene succinate (PBS) material offers a sustainable solution for a circular economy to address the global issue of marine plastic waste. Its cross-linkage with non-biodegradable xanthan gum (XG) biopolymer to ameliorate residual granitic soil (RGS) in arid and semiarid regions can significantly mitigate soil erosion. This study investigates the enhancement of RGS by cross-linking the PBS and XG biopolymers. Employing a multitude of geotechnical tests (liquid limit, linear shrinkage, specific gravity, compaction, and UCS tests) at 3 d, 28 d, and 90 d of steam-curing at a controlled temperature of 16 °C, the outcomes were validated through scanning electron microscopy (SEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) analyses. In addition, a comprehensive experimental database of 150 tests and nine parameters from the current study was utilized to model the UCS_90-d (i.e. unconfined compressive strength after 90 d of curing) of the PBS-XG-treated RGS mixtures by deploying the random forest (RF) and eXtreme Gradient Boost (XGBoost) methods. The results found that the two biopolymers significantly improve the mechanical properties of RGS, with optimal UCS achieved at specific dosages (0.4PBS, 1.5XG, and 0.2PBS+1.5XG dosage levels) and curing times. The UCS of PBS-XG-treated RGS showed up to a 57% increase after 90 d of curing. Furthermore, SEM and FTIR analyses revealed the formation of stronger microstructures and chemical bonds, respectively, whereas BET analysis indicated that pore volume and diameter are critical in affecting UCS. The proposed RF model outperformed XGBoost in predictive accuracy and generalization, demonstrating robustness and versatility. Moreover, SHAP values highlighted the significant impact of input parameters on UCS_90-d, with curing time and specific material properties being key determinants. The study concludes with the proposal of a novel PyCharm intuitive graphical user interface as a “UCS Prediction App” for engineers and practitioners to forecast the UCS_90-d of granitic residual soil.