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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble

Mohamed Kamel Elshaarawy1, Martina Zeleňáková2, Asaad M Armanuos3

  • 1Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta, 34517, Egypt. melshaarawy@horus.edu.eg.

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Summary
This summary is machine-generated.

Machine learning models, including CatBoost, accurately predict hydraulic structure performance using inclined cutoff walls. The CatBoost model excels in forecasting uplift force, hydraulic gradient, and seepage discharge, aiding practical engineering applications.

Keywords:
Cutoff wallsFeature importance analysisHydraulic structure designMachine learning modelsSeepage discharge predictionUplift force control

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Area of Science:

  • Geotechnical Engineering
  • Computational Fluid Dynamics
  • Machine Learning Applications

Background:

  • Hydraulic structures require effective seepage control to prevent uplift forces and ensure stability.
  • Inclined double cutoff walls are a common method for managing seepage, but their performance analysis can be complex.
  • Traditional analytical methods may not fully capture the intricate relationships between design parameters and seepage control effectiveness.

Purpose of the Study:

  • To evaluate the effectiveness of five machine learning models in predicting the performance of inclined double cutoff walls under hydraulic structures.
  • To identify the most influential design parameters affecting uplift force, hydraulic gradient, and seepage discharge.
  • To develop a practical tool for engineers to optimize cutoff wall design and predict seepage-related outcomes.

Main Methods:

  • Utilized a dataset of 630 samples from previous studies, incorporating parameters like relative cutoff wall distance (L/B), inclination angle ratio (θ2/θ1), depth ratio (d2/d1), and relative cutoff depth (d2/D).
  • Employed five machine learning models: Random Forest (RF), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost).
  • Performed hyperparameter optimization using Bayesian Optimization (BO) with five-fold cross-validation and analyzed feature importance using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP).

Main Results:

  • The CatBoost model demonstrated superior predictive accuracy, achieving R² values above 0.95 for uplift force (U/Uo), 0.93 for hydraulic gradient (iR/iRo), and 0.97 for seepage discharge (q/qo), with low RMSE.
  • Feature importance analysis indicated that L/B significantly influences U/Uo and iR/iRo, while d2/D is critical for q/qo.
  • Partial Dependence Plots revealed specific relationships: a positive linear trend between L/B and U/Uo, a V-shaped impact of d2/d1 on iR/iRo and q/qo, and complex nonlinear effects of θ2/θ1.

Conclusions:

  • Machine learning, particularly CatBoost, offers a robust and accurate approach for analyzing the performance of inclined double cutoff walls.
  • The study provides valuable insights into the influence of key design parameters, enabling more informed engineering decisions.
  • An interactive Graphical User Interface (GUI) was developed, facilitating the practical application of these predictive models in real-world hydraulic engineering projects.