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. © 2013 The Japanese Geotechnical Society. Production and hosting by Elsevier B.V. All rights reserved.
Tarawneh, B. (2013). Pipe pile setup: Database and prediction model using artificial neural network. Soils and Foundations, 53(4), 607–615. https://doi.org/10.1016/j.sandf.2013.06.011