Offshore Wind Turbines (OWT) with increasingly higher energy output are being developed to meet energy demands, posing greater challenges for their foundation design. Several foundation types are used to support these turbines, with monopiles (MPs) accounting for 80% of the installed capacity. In this study, three-dimensional (3D) nonlinear finite element models (FEM) were employed to investigate the behaviour of a monopile foundation supporting a 5MW wind turbine subjected to lateral loading. The results indicate that the MP behaviour depends on the pile length to diameter (L/D) ratio and the soil shear strength. Inspection of the bending-moment profiles at the lateral ultimate capacity indicated that the monopiles can behave in a flexible manner, even with low L/D ratios. The L/D ratio affected the MP normalized lateral ultimate capacity to varying degrees, and the biggest effect was for soft clays, amounting to an approximately five-fold increase for L/D values of 3.33 to 13.33. Lesser effects were found for stiff clays.

Deploying higher-capacity offshore wind turbines to meet the growing energy demand poses a significant challenge in designing their foundations. Monopiles currently constitute 80% of the foundation installations for these turbines. This study utilizes a nonlinear three-dimensional (3D) finite element model to explore the behavior of monopiles underpinning a five megawatt wind turbine under horizontal loads. The findings reveal that the performance of monopiles is influenced by the strength of the soil and the ratio of pile depth of embedment to diameter (L/D). Examination of flexural bending profiles at/close to failure loads demonstrates the flexible behavior of monopiles, even with a low L/D ratio. The L/D ratio exhibits varying degrees of impact on the normalized ultimate lateral capacity of monopiles, with a notable effect observed in soft clays, resulting in an increase of up to five times for L/D ratios ranging from 3.33 to 13.33. Stiff clays show comparatively lesser effects. Under serviceability loading, an increase in the L/D ratio leads to a 3–6% rise in the maximum flexural moment of monopiles, while the maximum shear force experiences a decrease of 20–30%. Furthermore, a significant reduction, up to five-fold, in the maximum tower tip displacements and rotations at the mudline is observed with an increasing L/D ratio. However, this reduction is more pronounced for higher foundation rigidity.

Mine tailings are the byproduct of mining activities, which need to be disposed of once the minerals in the ore are extracted. They can be disposed of in either dry or wet forms. The latter is most common, with the tailings being disposed of in the form of slurry inside retention structures. The retention structure may be a natural or manmade dam, with a predominant use of the upstream method due to its cost-effectiveness. This study analyzes the stability of an upstream tailings dam considering its staged construction. A two-dimensional nonlinear finite element model was developed using the program Plaxis 2-D to investigate the potential for stabilizing the tailings dam by using emulsified polymer and a mixture composed of cement kiln dust (CKD) and re-cycled gypsum (B). The numerical model demonstrated that utilizing a CKD: B mix increased the overall stability of the tailings impoundment above the conventional 1.5 safety factor requirements and indicated its usefulness in constructing robust dams whilst still being environmentally friendly.

Offshore Wind Turbines (OWT) with increasingly higher energy output are being developed to meet energy demand, posing challenges for their foundation design. Several foundation types are used to support these turbines with monopiles (MP) accounting for 80% of the installed capacity. In this study, three-dimensional (3D) nonlinear finite element models (FEM) were employed to investigate the behaviour of monopile foundation supporting a 5MW wind turbine when subjected to lateral loading. Results indicate that the MP behavior depending on the pile length to diameter (L/D) ratio and the soil shear strength. Inspection of bending moment profiles at the lateral ultimate capacity indicated the monopiles can behave in flexible manner even with low L/D ratio. The L/D ratio affected the MP normalized lateral ultimate capacity at varying degrees and the biggest effect was for soft clays, amounting to around 5 folds increase for L/D values of 3.33 to 13.33. Lesser effects were found for stiff clays.

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.