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 modeling 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.

Keywords

• :Axial capacity
• Shaft resistance
• End bearing
• PDA