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dc.contributor.authorAlzo’ubi, AK
dc.contributor.authorIbrahim, Farid
dc.identifier.citationAlzo’ubi, A. K., & Ibrahim, F. (2019). Predicting loading–unloading pile static load test curves by using artificial neural networks. Geotechnical and Geological Engineering, 37(3), 1311-1330.en_US
dc.description.abstractIn the United Arab Emirates, Continuous Flight Auger piles are the most widely used type of deep foundation. To test the pile behavior, the static load test is routinely conducted in the field by increasing the dead load while monitoring the displacement. Although the test is reliable, it is expensive to conduct. This test is usually conducted in the UAE to verify the pile capacity and displacement as the load increase and decreases in two cycles. The artificial neural network approach was used to build a model that can predict a complete static load pile test. In this paper, it was shown that by incorporating the pile configuration, soil properties, and groundwater table in one artificial neural network model, the static load test can be predicted with confidence. Six thousand field data points were used to train and validate the model. Three complete independent field tests (not included in the training stage) were used to test the model ability to predict the behavior of the pile during loading and unloading cycles. The results show excellent agreement between the actual and predicted curves in two loading–unloading cycles. The authors believe that based on this approach and the presented results of this research, the model is able to predict the entire pile load test results from start to end. The suggested approach is an excellent tool to reduce the cost associated with such expensive tests or to predict pile’s performance ahead of the actual test.en_US
dc.publisherSpringer International Publishingen_US
dc.subjectStatic load testen_US
dc.subjectContinuous flight augeren_US
dc.subjectArtificial neural networken_US
dc.titlePredicting Loading–Unloading Pile Static Load Test Curves by Using Artificial Neural Networksen_US
dc.title.alternativeJournal Articleen_US

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