Analysis of metaheuristic‑optimized ann for predicting surface strip footing bearing capacity in layered cohesionless soils using limit equilibrium analysis

Abstract

Accurate prediction of shallow-foundation bearing capacity in layered cohesionless soils remains challenging because footing geometry and the strength contrast between soil layers interact nonlinearly. This study developed and compared four hybrid multilayer perceptron models optimized using biogeography-based optimization, the league championship algorithm, sunflower optimization, and whale optimization. The models used footing width, upper-layer thickness, upper-layer friction angle, and lower-layer friction angle as predictors. A complete factorial database of 2,304 limit-equilibrium analyses was generated for surface strip footings under zero surcharge and divided into 1,613 training, 346 validation, and 345 untouched test cases. Population sizes and model configurations were selected exclusively from validation performance and convergence behavior, while repeated 80/20 holdout analysis within the training set was used to assess stability. Among the finalized models, BBO-MLP provided the strongest overall agreement, achieving a test-set correlation of 0.9834, a standard-deviation ratio of 1.002, and a centered RMSE of 130.19 kPa. SHAP analysis across all 2,304 cases consistently ranked footing width as the dominant predictor, followed by lower-layer friction angle, upper-layer friction angle, and upper-layer thickness.