Application of six neural network-based solutions on bearing capacity of shallow footing on double-layer soils

Many of the recent investigations in the field of geotechnical engineering focused on the bearing capacity theories of multilayered soil. A number of factors affect the bearing capacity of the soil, such as soil properties, applied overburden stress, soil layer thickness beneath the footing, and type of design analysis. An extensive number of finite element model (FEM) simulation was performed on a prototype slope with various abovementioned terms. Furthermore, several non-linear artificial intelligence (AI) models are developed, and the best possible neural network system is presented. The data set is from 3443 measured full-scale finite element modeling (FEM) results of a circular shallow footing analysis placed on layered cohesionless soil. The result is used for both training (75% selected randomly) and testing (25% selected randomly) the models. The results from the predicted models are evaluated and compared using different statistical indices (R2 and RMSE) and the most accurate model BBO (R2=0.9481, RMSE=4.71878 for training and R2=0.94355, RMSE=5.1338 for testing) and TLBO (R2=0.948, RMSE=4.70822 for training and R2=0.94341, RMSE=5.13991 for testing) are presented as a simple, applicable formula.