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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Application of Artificial Neural Network for Diagnosing Pile Integrity Based on Low Strain Dynamic Testing
verfasst von : Canhui Zhang, Jianlin Zhang
Erschienen in: Computational Structural Engineering
Verlag: Springer Netherlands
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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, cross-sectional 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.