Predicting the Entire Static Load Test by Using Generalized Regression Neural Network in the United Arab Emirates

Abstract

In the UAE, continuous flight auger piles (CFA) are the most commonly used type of foundations. To minimize the risk of failure, of these CFA piles, mandatory expensive field tests need to be performed and the most important one is the Static Pile Load Test (SPLT). This paper proposes using a General Regression Neural Network (GRNN) to predict the pile performance ahead of any test. Thousands of loading points in over one hundred projects from Dubai, Abu Dhabi, and Al Ain cities are used to develop a GRNN capable of predicting SPLT curves with reasonable accuracy. The friction angle, unconfined compressive strength, depth, soil type, groundwater table, pile’s diameter, and pile’s length are the parameters that are input to predict the load–displacement curves of the SPLT. This approach can complement conventional SPLT and provide engineers with sufficient insight into the pile performance ahead of the actual test.