Owing to the potential negative impacts of climatic changes and the grand Ethiopian renaissance dam, water scarcity has become an urgent issue. Therefore, the Egyptian Ministry of Water Resources and Irrigation has started a national project of the lining and rehabilitation of canals, to reduce seepage losses and for efficient water resource management. This study presents a new approach for assessing three different lining and crack techniques for the Ismailia canal, the largest end of the river Nile, Egypt. A 2-D steady state seep/w numerical model was developed for the Ismailia canal section, in the stretch at 28.00–49.00 km. The amount of seepage was significantly dependent on the hydraulic characteristics of the liner material. The extraction from aquifers via wells also had a considerable impact on the seepage rate from the unlined canals; however, a lesser effect was present in the case of lined canals. The concrete liner revealed the highest efficiency, followed by the geomembrane liner, and then the bentonite liner; with almost 99%, 96%, and 54%, respectively, without extraction, and decreasing by 4% for bentonite and geomembrane liners during extraction; however, the concrete lining efficiency did not change considerably. Nevertheless, the efficiency dramatically decreased to 25%, regardless of the lining technique, in the case of deterioration of the liner material. The double effect of both deterioration of the liner material and extraction from the aquifer showed a 16% efficiency, irrespective of the utilized lining technique.

Cutoff walls are widely used to limit seepage, piping, and the uplift under hydraulic structures. Therefore, this study focused on a numerical investigation of the hydraulic performance of cutoff walls beneath hydraulic structures during both static and dynamic conditions, considering location and inclination angle influences. The results confirmed that placing the cutoff wall at the upstream heel was more effective in reducing uplift pressure compared to other placements during static conditions. The inclination angles for the different placements of the cutoff wall had a significant impact on the total uplift pressure, exit hydraulic gradient, and seepage discharge during both static and dynamic states. The earthquakes had a noticeable effect on uplift pressure, seepage discharge, and exit hydraulic gradient. During static conditions, the inclination angle of 90° was the most effective angle for decreasing seepage discharge, irrespective of the cutoff wall position. During an earthquake, the seepage discharge values were high regardless of the inclination angle. In the case of placing a cutoff wall at the upstream heel, the maximum seepage discharge value occurred at an inclination angle of 45°. This study provided insights into the behavior of cutoff walls under different conditions and can inform the design and construction of such structures for effective seepage control. The experimental feed-forward neural network (FFNN) was also successfully built. According to the following criteria (uplift pressure, seepage, and exit hydraulic gradient), the hydraulic performance of cutoff walls beneath hydraulic structures under static conditions can be examined. The FFNN can make predictions with root mean square errors (RMSE) of 0.0697, 0.0021, and 0.0059, respectively, and R² values of 1.00, 0.9994, and 0.9997.

This study aims to develop a novel ensemble modeling approach that integrates artificial neural networks with finite element analysis to optimize the stability of hydraulic structures, particularly through the design of cutoff wall configurations. The research investigates the effects of varying cutoff wall positions and inclination angles on key parameters such as uplift pressure, seepage discharge, and exit gradient. Numerical simulations were performed using Geostudio SEEP/W to analyze seepage patterns across multiple configurations. The proposed methodology combines a Feed-Forward Neural Network (FFNN), XGBoost Regressor, and Support Vector Machine (SVM) with a Genetic Algorithm (GA) to create a predictive optimization framework. The findings reveal that the optimal cutoff wall inclination angle for minimizing both uplift pressure and exit gradient is 165° across all positions, while for seepage discharge, the optimal angle varies by position, ranging from 60° to 120° and increasing incrementally by 15° from upstream to downstream. The ensemble model demonstrated robust predictive performance across 5-fold cross-validation trials, achieving mean R-squared values of 0.99 ± 0.01 for uplift pressure, 0.94 ± 0.02 for seepage discharge, and 0.97 ± 0.01 for exit gradient. The small standard deviations indicate consistent performance across different data partitions, validating model stability and generalizability. The Genetic Algorithm results closely aligned with the numerical model outputs, validating the robustness of the proposed framework. This study introduces a significant improvement over traditional analytical methods by providing an integrated approach that enhances the safety and efficiency of hydraulic infrastructure design, particularly under complex conditions where conventional techniques may fall short.

Because of climate change, the erection of dams through transboundary rivers, and the shortage of water resources, countries worldwide are facing several challenges. Unconventional water resources such as recycling wastewater are urgently needed to overcome the demand–supply gap. The bioreactor is considered an effective means of removing contaminants and enhancing the self-purification on open waterways. This study numerically investigates the hydraulic impacts of an installed bioreactor in an open channel. Three-dimensional numerical models were developed based on computational fluid dynamics (CFD) to simulate different arrangements of the gravel bioreactor installed in the open channel. The relative heading up increased as Froude’s number increased, regardless of the number of bio-filter segments. The heading up values decreased significantly when dividing the bio-filter into several segments with the same total length. The relative heading up decreased linearly as the distance between the bio-filter segments increased for a constant bio-filter length. On the other hand, the relative heading up increased linearly as the length of the bio-filter segments increased for a constant inter-distance of the bio-filter segments. For simultaneous changes in both the length of the bio-filter segments and their inter-distance, the relative heading up increased nonlinearly with the product of both the length and inter-distance. This study confirmed that the greater the inclination angles of the bio-filter’s face, the greater the heading up. When considering the base case with the inclination angle of 45°, the increasing ratios of the relative heading up were 14.5%, 16.1%, and 19.7%, corresponding to the angles 60°, 75°, and 90°, respectively.

Understanding and accurately predicting the outflow hydrograph from embankment dam breaches is essential for managing the associated flood hazard and improving emergency preparedness. This work simulates the breaching process using a high-resolution 3D computational fluid dynamics (CFD) model, a critical natural hazard for earth-fill dams under overtopping conditions. The model was validated against the experimental data, showing high accuracy in predicting breach development and failure timing. A parametric analysis was performed to assess the influence of the initial breach geometry on erosion dynamics. The results indicated a high sensitivity, while increasing the breach width by 5% led to an average 11% increase in the erosion rate, and decreasing the depth by 5% caused an average 16.5% rise. To enhance predictive capabilities for this hazard, a multilayer neural network (MLNN) was trained on the CFD-generated dataset. The network utilized breach geometry and time as inputs to forecast the peak outflow and erosion rate, achieving excellent accuracy (RMSE = 0.019, R² = 0.99). This integrated modeling strategy combines data-driven learning with physics-based simulation and demonstrates its effectiveness for laboratory-scale dam breach modeling. This approach is a step toward more efficient surrogate-based tools for flood risk analysis, though its extension to full-scale dams and varied material properties requires additional validation and scaling analyses beyond the scope of this work.