Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN, part I

Abstract

Extensive laboratory model tests were conducted on a strip foundation lying over sand bed subjected to an eccentrically inclined load to determine the ultimate bearing capacity. Based on the model test results, a neural network model was developed to predict the reduction factor. This reduction factor (RF) is the ratio of the ultimate bearing capacity of the foundation subjected to an eccentrically inclined load to the ultimate bearing capacity of the foundation subjected to a centric vertical load. Different sensitivity analysis was carried out to evaluate the parameters affecting the reduction factor. Emphasis is given on the construction of neural interpretation diagram, based on the weights of the developed neural network model, to find out direct or inverse effect of input parameters on the output. A prediction model equation is established using the trained weights of the neural network model. The predictions from artificial neural network (ANN), and those from two other approaches, were compared with the laboratory model test results. The ANN model results found to be more accurate and well matched with other results.