An Artificial Neural Network Approach for Prediction of Bearing Capacity of Spread Foundations in Sand

Abstract

This study highlights the application of Back-Propagation (BP) feed forward Artificial Neural Network (ANN) as a tool for predicting bearing capacity of spread foundations in cohesionless soils. For network construction, a database of 75 recorded cases of full-scale axial compression load test on spread foundations in cohesionless soils was compiled from literatures. The database presents information about footing length (L), footing width (B), embedded depth of the footing (Df), average vertical effective stress of the soil at B/2 below footing (s΄), friction angle of the soil (f) and the ultimate axial bearing capacity (Qu). The last parameter was set as the desired output in the ANN model, while the rest were used as input of the ANN predictive model of bearing capacity. The prediction performance of ANN model was compared to that of Multi-Linear Regression analysis. Findings show that the proposed ANN model is a suitable tool for predicting bearing capacity of spread foundations. Coefficient of determination R2 equals to 0.98, strongly indicates that the ANN model exhibits a high degree of accuracy in predicting the axial bearing capacity of spread foundation. Using sensitivity analysis, it is concluded that the geometrical properties of the spread foundations (B and L) are the most influential parameters in the proposed predictive model of Qu.