Neural Network Application in Prediction of Axial Bearing Capacity of Driven Piles

This paper presents the application of the Artificial Neural Network (ANN) for prediction of axial capacity of a driven pile by adopting data collected from several projects in Indonesia and Malaysia. As many as 300 data were selected for this study. In this study, ANN was set and trained to predict the axial bearing capacity from high strain dynamic testing, i.e. Pile Driving Analyzer (PDA) data. A system was developed by a computerized intelligent system for predicting the total pile capacity for various pile characteristics and hammer energy. The results show that the neural network models give a good prediction of axial bearing capacity of piles if both stress wave data and properties of both driven pile and driving system are considered in the input data. Verification of the model indicates that the numbers of data are not always related to the quality of the prediction.