Author
Abstract
Abstract:This study explores the potential of back propagation neural networks (BPNN) computingparadigm to predict the ultimate bearing capacity of shallow foundations on cohesionlesssoils. The data from 97 load tests on footings (with sizes corresponding to those of realfootings and smaller sized model footings) were used to train and validate the model. Fiveparameters are considered to have the most significant impact on the magnitude ofultimate bearing capacity of shallow foundations on cohesionless soil and are thus used asthe model inputs. These include the width of the footing, depth of embedment, length towidth ratio, dry or submerge unit weight and angle of internal friction of the soil. Themodel output is the ultimate bearing capacity. Performance of the model wascomprehensively evaluated. The values of the performance evaluation measures such ascoefficient of correlation, root mean square error, mean absolute error reveal that themodel can be effectively used for the bearing capacity prediction. BPNN model iscompared with the values predicted by most commonly used bearing capacity theories.The results indicate that the model perform better than the theoretical methods.KEYWORDS: Ultimate bearing capacity; Shallow foundations; cohesionless soil; backpropagation neural network (BPNN); prediction
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