The rheological and physical properties of asphalt binders modified with aluminium oxide (Al₂O₃) nanoparticles were investigated. Al₂O₃ nanoparticles were added to the base asphalt binder at concentrations of 3, 5, and 7 wt.%. Superpave binder tests were conducted to evaluate the characteristics of the nano-Al₂O₃-modified binders. Rotational viscosity tests and a dynamic shear rheometer were used to analyse the rheological and physical properties of the modified binders, and Fourier transform infrared spectroscopy technique to observe changes in the structure of modified binders compared to the original. The penetration and ductility values decreased with added Al₂O₃ as well as temperature susceptibility. The phase angle δ is reduced with 5 wt.% or less Al₂O₃ added to the binder. The complex shear modulus G* significantly increased with the addition of up to 5 wt.% Al₂O₃. Results recognise 5 wt.% as the optimum level. The performance of nano-Al₂O₃-modified binders was enhanced regarding their resistance to both thermal rutting at high temperatures and fatigue cracking at low temperatures. Furthermore, statistical analysis of penetration, softening point, complex shear modulus, and phase angle results, using both one- and two-way ANOVA tests, show a significant difference level, having remarkable impacts on the dependent variables.

This study examines the potential of the soft computing technique—namely, Gaussian process regression (GPR), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. The inputs of the model are width of footing (B), depth of footing (D), footing geometry (L/B), unit weight of sand (γ), and internal friction angle (ϕ). The results of the present model were compared with those obtained by two theoretical approaches reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of UBC (q_u). This study shows that the developed GPR is a robust model for the q_u prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter.

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K‐nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R²), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R² = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R² = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R² = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R² = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R² = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.