April 18, 2024

Hossein Moayedi

Academic rank:
Education: Ph.D
Faculty: Faculty of Engineering


Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods
Type Article
machine learning; soft computing; shallow foundation; bearing capacity; finite element method.
Researchers Hossein Moayedi، Sajad Hayati


This paper presents the results of an investigation into several non-linear machine learning and soft computing-based models, namely, feedforward neural network (FFNN), radial basis neural network (RBNN), general regression neural network (GRNN), support vector machine (SVM), tree regression fitting model (TREE) and adaptive neuro-fuzzy inference system (ANFIS). The data sets are from extensive finite element modelling (FEM) of a shallow strip footing located near a homogeneous sandy slope. The FEM outputs are used for training and testing the models. Furthermore, the predicted and calculated models are compared and evaluated using different statistical indices and the most accurate model is presented as a simple formula. After model evaluation process the most accurate model is proposed to estimate the solution. The predicted results are compared with the FEM data, and a good agreement is obtained representing good reliability for FFNN (R2=0.9233 for training and 0.9095 for testing) solutions in this study. Moreover, the soft computing model is presented as a simple formula and excellent agreement is obtained representing a high degree of reliability for the proposed model.