Genetic algorithm-optimized back propagation neural network for the characterization of backward erosion piping channels

The use of levees is one of the most prevalent and effective strategies for flood protection. However, owing to the ageing of levees, inconsistent reinforcement efforts, and complex geological conditions, hazards such as piping frequently arise during flood seasons, which lead to significant and often irreparable damage. This study investigates backward erosion piping (BEP) in the foundations of double-structured levees via a back-propagation (BP) neural network optimized by a genetic algorithm (GA). The primary contributions of this study include: 1) the construction of a training dataset through numerical simulations of BEP in heterogeneous aquifers and validation of the dataset against laboratory sandbox piping tests to verify its reliability; 2) the extraction of head H and permeability coefficient K data from Groups II, III, and IV in the BEP laboratory tests, augmentation of the dataset, and optimization of the GA–BP model to characterize test results in Group I, where the results demonstrate that the optimized model more accurately characterizes areas where the K≤1.0 cm/s; and 3) the use of the optimized GA-BP model to characterizes the development of a BEP channel. The results indicate that the model accurately captures the general trends. However, minor discrepancies remain in the characterized channel location and size compared with the actual conditions. In conclusion, this study offers an effective tool for characterizing BEP and demonstrates the potential of the GA–BP network model for practical applications in this field.