The environmental impacts of cement manufacturing are becoming a real-time issue that requires attention. This paper investigates the mechanical and physical properties of mortars with finely ground sand as a substitute for cement. The experimental program consisted of three silica sands with a Blaine Specific Surface (BSS) area of 459 m²/kg, 497 m²/kg, and 543 m²/kg and four substitution ratios of 10%, 20%, 30%, and 40%. A total of 12 mixtures have been prepared and tested for comparison to the reference mortar. The pozzolanic effect of the sand was evaluated using thermogravimetric analysis (TGA). The results revealed that the fineness variation from 459 m²/kg to 543 m²/kg resulted in an increase of 20% and 30% in water absorption and compressive strength, respectively. However, increasing the substitution ratio from 10% to 40% led to a 40% decrease in mechanical strength and a 25% increase in water absorption. The statistical analysis of the results demonstrated that both factors under study influenced compressive strength and water absorption. The ANalysis of VAriance (ANOVA) confirmed that the proposed regression equations predict the experimental results. Further studies will investigate both the technical and environmental performances of cement mortars with finely ground silica sand.

This study investigates the prediction of immediate settlement (Uz) in soft clay improved with Deep Soil Mixing (DSM) columns under heavy aircraft loading. Two key design parameters were considered: column spacing (2.25 m to 3.75 m) and column length (6 m to 20 m), with both rectangular and triangular arrangements analyzed. The datasets obtained from numerical simulations were modeled using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN), with model calibration and validation performed through k-fold cross-validation. The statistical analysis revealed that both approaches achieved excellent predictive capability, with R² values exceeding 0.999. For the rectangular arrangement, RSM yielded slightly lower errors (RMSE = 0.0636 cm, MAE = 0.0553 cm) compared to ANN (RMSE = 0.0828 cm, MAE = 0.0682 cm), suggesting that a second-order polynomial approximation can effectively describe the settlement response in this configuration. Conversely, for the triangular arrangement, ANN clearly outperformed RSM, reducing RMSE from 0.0725 cm to 0.0265 cm and MAE from 0.0615 cm to 0.0111 cm, thereby capturing the nonlinear stress redistribution associated with isotropic column layouts more effectively. Observed–predicted plots confirmed the high predictive accuracy of both methods, with ANN showing superior generalization in triangular grids. Overall, the findings highlight that RSM remains a robust and computationally efficient tool for rectangular layouts with relatively linear responses. In contrast, ANN provides enhanced accuracy for triangular configurations where nonlinear interactions dominate, making it particularly suitable for DSM design optimization in airport pavement foundations.