Prediction of ultimate bearing capacity of eccentrically inclined loaded strip footing by ANN: Part II

Abstract

Laboratory model tests were conducted on a strip footing resting over dry sand bed subjected to eccentrically inclined load in reinforced condition to determine the ultimate bearing capacity. Eccentrically inclined load on a strip footing can be referred to as partially compensated when the line of load application at the base of the footing is inclined toward the centerline of the foundation or reinforced when the line of load application is inclined away from the centerline. Based on the model load test results, a neural network model was developed to predict the reduction factor that will be used in computing the ultimate bearing capacity of an eccentrically inclined loaded strip footing. This reduction factor (RF) is the ratio of the ultimate bearing capacity of the footing subjected to an eccentrically inclined load to the ultimate bearing capacity of the footing subjected to a centric vertical load. A thorough sensitivity analysis was carried out to evaluate the parameters affecting the reduction factor. Based on the weights of the developed neural network model, a neural interpretation diagram is developed to find out whether the input parameters have direct or inverse effect on the output. A prediction model equation is established using the trained weights of the neural network model. The results were compared with the developed empirical equation for the reduction factor (Patra et al., 2012b). The ANN model results were found to be more accurate than the regression equation proposed by Patra et al. (2012b) based on the laboratory model test data and the predictability was reasonably good.