The performance of neural-evolutionary optimization approaches' efficiency in forecasting belled piles' pullout capacity

The primary objective of this study is to predict the pullout capacity of belled piles by developing and evaluating various hybrid modeling techniques, including Evolution Strategy (ES), Moth Flame Optimizer (MFO), Grasshopper Optimization Algorithm (GOA), and League Championship Algorithm (LCA). Each hybrid model combines an artificial neural network (ANN) with an optimization algorithm, trained using a hybrid learning approach that incorporates back-propagationand least squares estimation, implemented in MATLAB. A total of 36 samples were used, with 25 designated for training and 11 for testing. The performance of each model was assessed using statistical metrics, namely the coefficient of determination (R²) and root mean square error (RMSE). Among the models, MFO-ANN demonstrated the highest predictive accuracy, followed by LCA-ANN, ES-ANN, and GOA-ANN, respectively. The results confirm the robustness and reliability of the ANN-based hybrid models in estimating pullout capacity.