Recent Advances in Stability and Seepage Analysis of Earth Dams: A Review Leveraging Numerical Methods and Computational Intelligence

Ensuring the safety and long-term performance of earth dams is paramount due to their critical role in water management and the potential consequences of failure. Dams are susceptible to stability issues and internal erosion driven by complex hydraulic conditions, including seepage and fluctuating water levels. Traditional analytical methods often fall short in capturing the full complexity of these interactions and optimizing mitigation measures. This review fills a notable gap in the existing literature by holistically integrating numerical modeling (FEM, Limit Equilibrium) and artificial intelligence (ANN, GA) techniques to provide a comprehensive assessment of earth dam behavior. This review synthesizes recent research applying advanced computational techniques, including numerical methods like the Finite Element Method (FEM) and Limit Equilibrium methods, alongside computational intelligence approaches such as Artificial Neural Networks (ANN) and Genetic Algorithms (GA), to analyze the stability and seepage characteristics of earth dams and evaluate the effectiveness of seepage control measures. Drawing insights from several recent studies, this article examines the influence of rapid drawdown on dam slope stability, the impact of fissured soil orientation, and critically, the effectiveness of various seepage control measures (cutoff walls, horizontal drains, pipe drains, injections) and their optimal configurations, including combined approaches. It highlights the capabilities of numerical modeling for detailed analysis and optimization and the potential of AI and hybrid methods for improved prediction and parameter identification in complex, nonlinear scenarios. The findings underscore the importance of integrating diverse computational tools for robust design, risk assessment, and monitoring of earth dams.  

Keywords: Earth dams; Stability assessment; Seepage mitigation; Finite Element Method; Neural networks; Genetic algorithms.

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