Pipe pile setup: Database and prediction model using artificial neural
network

Bashar Tarawneh,
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doi:10.1016/j.sandf.2013.06.011
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Open Access funded by Japanese Geotechnical Society
Under an Elsevier user license
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Abstract
Over the last few years, artificial neural networks (ANNs) have been applied to many geotechnical
engineering problems with some degree of success. With respect to the design of pile foundations, the
ability to accurately predict pile setup may lead to more economical pile design, resulting in a reduction in
pile length, pile section, and size of driving equipment. In this paper, an ANN model was developed for
predicting pipe pile setup using 104 data points, obtained from the published literature and the author's
own files. In addition, the paper discusses the choice of input and internal network parameters which
were examined to obtain the optimum ANN model.
Finally, the paper compares the predictions obtained by the ANN with those given by a number of
empirical formulas. It is demonstrated that the ANN model satisfactorily predicts the measured pipe pile
setup and significantly outperforms the examined empirical formulas.
Keywords

Pile foundation;

Pile setup;

Artificial neural networks