JWSS - Journal of Water and Soil Science

One of the key challenges in the design of side weirs is enhancing discharge efficiency, which is defined as the dimensionless ratio of the flow rate over the weir to the total incoming discharge. This study investigates the hydraulic performance of a converging side weir equipped with flow-guiding side plates. A three-dimensional numerical model using FLOW-3D software was employed to simulate flow conditions in the presence of guide plates with varying angles, relative lengths (defined as the ratio of plate length to the upstream channel width), and installation positions, to identify hydraulically optimal configurations. Following validation of the model against experimental data, 28 different scenarios were evaluated. The results demonstrated that under proper conditions, the installation of side guide plates can significantly improve discharge efficiency. Among all cases, the configuration with a 60° deflecting angle and a relative length of 0.2, installed at the upstream location (X₁) of the weir, yielded the best performance, increasing efficiency from a baseline of 62% to 82%. Analysis of the velocity field further revealed that the formation of a low-velocity zone behind the plate plays a critical role in directing the flow toward the weir. Overall, the use of side guide plates presents a simple, low-cost, and effective solution for enhancing the hydraulic performance of converging side weirs without requiring structural redesign.

Scour is a major challenge in river engineering, as it causes bridge failures during flood events and leads to significant economic losses. This study aims to estimate the normalized scour depth (Dse/Dp) around pile groups by examining relevant hydrodynamic and geometric parameters. A dataset comprising 299 laboratory measurements collected from various sources was assembled and divided into training and testing subsets. As machine learning inputs, several models were employed, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and a meta-ensemble learning model (Stacking). Hyperparameter tuning was performed using the Grid search method to achieve optimal regression performance. Model performance evaluation indicated that the ANN and SVR models achieved coefficients of determination of R² = 0.87 and R² = 0.91, respectively. The XGBoost model outperformed these approaches, yielding R² = 0.94 with an RMSE of approximately 0.28. Ultimately, the stacking ensemble model, by integrating the outputs of the base learners, demonstrated the highest predictive accuracy with R² = 0.96 and an RMSE of 0.11, representing an improvement of approximately 15% compared to ANN and 7% compared to XGBoost. Overall, the findings highlight that ensemble machine learning models—particularly the Stacking approach—provide a robust and efficient framework for predicting scour depth around pile groups and for capturing the complex flow behaviors in hydraulic systems.