Artificial Neural Network Model for Prediction of Bearing Capacity of Driven Pile
Abstract: This paper presents
the development of ANN model for prediction of axial capacity of a driven pile
based on Pile Driving Analyzer (PDA) test data. As many as 300 sets of high
quality test data from dynamic load test performed at several construction
projects in Indonesia and Malaysia were selected for this study.Input
considered in the model-ing are pile characteristics (diameter, length as well
as compression and tension capacity), pile set, and hammer characteristics (ram
weight, drop height, and energy transferred).An ANN model (named: ANN-HM) was
developed in this study using a computerized intelligent system for predicting
the total pile capacity as well as shaft resistance and end bearing capacity
for various pile and hammer characteristics.
The results show that the ANN-HM serves as a reliable prediction tool to
predict the resistance of the driven pile with coefficient of correlation (R)
values close to 0.9 and mean squared error (MSE) less than 1% after 15,000
number of iteration process.
Author: Harnedi Maizir, Nurly
Gofar, Khairul Anuar Kassim
Journal Code: jptsipilgg150001
