Optimization of Earth Dam Cross-Sections Using the Max–Min Ant System and Artificial Neural Networks with Real Case Studies

The identification of non-circular critical slip surfaces in slopes using metaheuristic algorithms remains a frontier challenge in geotechnical engineering. Such approaches are particularly effective for assessing the stability of heterogeneous slopes, including earth dams. This study introduces ODACO, a comprehensive program developed to determine the optimum cross-section of earth dams with berms. The program employs the Max–Min Ant System (MMAS), one of the most robust variants of the ant colony optimization algorithm. For each candidate cross-section, the critical slip surface is first identified using MMAS. Among the stability-compliant alternatives, the configuration with the most efficient shell geometry is then selected. The optimization process is conducted automatically across all loading conditions, incorporating slope stability criteria and operational constraints. To ensure that the optimized cross-section satisfies seismic performance requirements, an artificial neural network (ANN) model is applied to rapidly and reliably predict seismic responses. These ANN-based predictions provide an efficient alternative to computationally intensive dynamic analyses. The proposed framework highlights the potential of optimization-driven approaches to replace conventional trial-and-error design methods, enabling more economical, reliable, and practical earth dam configurations.