Abstract
The low strain test method is the prevalent method for integrity testing of cast in situ foundation piles. The automated interpretation of sonic echo traces from this test may prove beneficial to industry through standardisation of the test method and a reduction in the time spent analysing data. In this research the generalisation and feature extraction strengths of artificial neural networks have been exploited for test data interpretation. This study involved the use of a multilayer perceptron network considered most suitable for this heteroassociative function approximation task. The network was trained using numerically generated data. Field data from two sites confirmed that the network identified, located and quantified changes in pile diameter as well as detecting pile lengths to within 5% of their design values.
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