Prediction of ultimate bearing capacity eccentrically loaded rectangular foundations using ANN

Extensive laboratory model tests were conducted on a rectangular embedded foundations resting over homogeneous sand bed and subjected to eccentric load to determine the ultimate bearing capacity. The depth of embedment varies from 0 to B with an increment of 0.5B; where B is the width of foundation and the eccentricity ratio (e/B) was varied from 0 to 0.15 with increments of 0.05. Based on the laboratory model test results, a neural network model has been developed to estimate the reduction factor (RF). The reduction factor can be used to estimate the ultimate bearing capacity of an eccentrically loaded foundation from the ultimate bearing capacity of a centrally loaded foundation. A thorough sensitivity analysis has been carried out to determine the important parameters affecting the reduction factor. Importance was given on the construction of neural interpretation diagram, and based on this diagram, whether direct or inverse relationships exist between the input and output parameters was determined. The results from artificial neural network (ANN) were compared with the laboratory model test results and the agreement is good