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|Title:||Prediction of pile behavior using artificial neural networks based on standard penetration test data|
|Citation:||Proceedings of the International Association for Computer Methods and Advances in Geomechanics, 2011: pp.564-569|
|Conference Name:||International Conference of the International Association for Computer Methods and Advances in Geomechanics (13th : 2011 : Melbourne, Australia)|
|F. Pooya Nejad, M.B. Jaksa|
|Abstract:||This paper presents an artifi cial neural network (ANN) model for the prediction of non-linear behavior of vertically loaded piles based on the results of standard penetration test (SPT) data. The geotechnical literature has in-cluded many methods, both theoretical and experimental, to predict pile behavior. Most of the available methods simplify the problem by incorporating several assumptions associated with the factors that affect pile behavior. With respect to the design of pile foundations, accurate prediction of pile behavior is necessary to ensure appropriate structural and serviceability performance. Approximately, 1,000 data sets, obtained from the published literature, are used to develop the ANN model. In addition, the paper discusses the choice of input and internal network parameters which were examined to obtain the optimum model. Finally, the paper proposes a series of charts for predicting pile behavior that will be useful for pile design.|
|Rights:||Copyright status unknown|
|Appears in Collections:||Aurora harvest|
Civil and Environmental Engineering publications
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