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dc.contributor.authorAlzo'ubi, Abdel Kareem
dc.identifier.citationAlzo’ubi, A. K., & Ibrahim, F. (2018). Towards building a neural network model for predicting pile static load test curves. In MATEC Web of Conferences (Vol. 149, p. 02031). EDP Sciences.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 behaviour, 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. In this paper we will utilize the Artificial Neural Network approach to build a model that can predict a complete Static Load Pile test. We will show that by integrating the pile configuration, soil properties, and ground water table in one artificial neural network model, the Static Load Test can be predicted with confidence. We believe that based on this approach, the model is able to predict the entire pile load test 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.publisherEDP Sciencesen_US
dc.subjectUnited Arab Emiratesen_US
dc.subjectArtificial neural networken_US
dc.subjectThe Static Loaden_US
dc.titleTowards building a neural network model for predicting pile static load test curvesen_US
dc.title.alternativejournal Articalen_US

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