Application of Artificial Neural Network for Diagnosing Pile Integrity Based on Low Strain Dynamic Testing

摘要

The artificial neural network (ANN) models are presented for diagnosing pile in this paper based on the pile integrity test (PIT) also known as low strain dynamic test. The back-propagation learning algorithm is employed to train the network for extracting knowledge from training examples. There are fifty-three input neurons in the network including the PIT response and pile length, crosssectional area and wave velocity. In order to obtain the pile condition in quantity, the novel technique is proposed containing two back-propagation ANn models. The first is to identify the defect patters while the second to investigate the exact degree of pile defect by computing the change of equivalent cross-sectional area. Training and testing data were drawn from response records of actual piles. The results from the testing phase indicate that the presented method is successful.