Bearing Capacity Prediction for Spatially Variable Clay with Rotated Anisotropy Using ANN-Driven Stochastic Modeling

Bearing Capacity Prediction for Spatially Variable Clay with Rotated Anisotropy Using ANN-Driven Stochastic Modeling

Shallow foundations are commonly used in geotechnical engineering, requiring accurate estimation of soil strength, particularly for undrained random clay with rotated anisotropy. This study employs a random adaptive stochastic analysis to assess the bearing capacity and failure probability of strip footings on such soils, considering key parameters including undrained shear strength (Su), its coefficient of variation (COVSu), spatial correlation lengths (CLx and CLy), and rotation angle (β). Results include the mean bearing capacity factor (μNran), probability of failure (PoF), and failure mechanisms influenced by COVSu and CLy. Four hybrid ANN-based soft computing techniques, namely Ant Lion Optimizer (ALO), Imperialist Competitive Algorithm (ICA), Shuffled Complex Evolution Algorithm (SCE), and Teaching-Learning-Based Optimization (TLBO), were developed and evaluated using various performance metrics (R², MAE, RMSE, VAF, IOS, and RSR), as well as convergence curves, regression plot, Taylor diagram, ranking of model performance, and relative impact. Among them, the ANN-SCE model demonstrated superior performance, achieving R² values of 0.9980 (training) and 0.9905 (testing), with consistently low errors across all metrics. These findings provide a robust framework for predicting foundation behavior under spatially variable soil conditions and highlight the potential of hybrid AI models in enhancing geotechnical reliability and design efficiency.