Abstract
In 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.
Highlights
Deep foundations such as piles, are the part of a structure used to carry and transfer loads of the superstructure to the bearing ground located at some depth below ground surface
Based on the above discussion, it is safe to assume that predicting the pile capacity by means of artificial neural networks can be achieved through training the model on the available data
As we can see in the above mentioned studies, no serious attempt was made to predict the entire static load test; in this paper we demonstrate that static load test can be reasonably predicted by an artificial neural network with enough training data
Summary
Deep foundations such as piles, are the part of a structure used to carry and transfer loads of the superstructure to the bearing ground located at some depth below ground surface. As a result of the economic boom that has been happening in the United Arab Emirates, construction industry flourished and huge amount of new projects has to be built as fast as possible, keeping in mind the safety of structures such as high rise buildings, malls, and residential developments With this in mind, engineers relied heavily on the Continuous Flight Auger (CFA) piles (see Figure 1) to speed up the construction process, and over design these piles to avoid failure. Based on the above discussion, it is safe to assume that predicting the pile capacity by means of artificial neural networks can be achieved through training the model on the available data This approach would allow judging new proposed pile design ahead of the field tests. The calculated load capacities are not as precise, so designs based solely on analytic methods must be correspondingly more conservative and the final design is more expensive
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