Using meta-heuristic algorithms with multi-layer perceptron for prediction of ultimate bearing capacity

This study presents an innovative approach to predicting the Ultimate Bearing Capacity (Qu) in civil engineering, integrating a Multi-layer Perceptron (MLP) neural network using the Artificial Rabbits Optimization (ARO) and Improved Manta-Ray Foraging Optimizer (IMRFO) methods. The MLP effectively captures complex, non-linear relationships within geotechnical datasets, while the IMRFO and ARO algorithms fine-tune the MLP's parameters for optimal performance. Validation results demonstrate the efficacy of this method, with the MLP+IMRFO (MLIM) model achieving an R2 value of 0.996 and a MARE of 0.113, highlighting its potential for enhancing predictive accuracy in civil engineering applications. This combined methodology offers a state-of-the-art solution for Qu forecasting, contributing to safer and more efficient construction practices.