Artificial neural network modelling of driven pile capacity in Metro Manila - Buensuceso, Benjamin B. III
The compressive axial load capacity of driven piles is most likely to be the governing factor in the piles' design, making its accurate prediction an important consideration. This paper presents the use of Artificial Neural Networks (ANNs) for the prediction of the ultimate compressive axial load capacity of 155 driven piles in the coastal Metro Manila Region. A database of the characteristics and capacities of these piles are also presented. Various characteristics of the driven piles and the characteristics of pile driving were then used as inputs into an ANN to predict their ultimate compressive axial load capacity. The ANNs were trained with capacity values obtained from High Strain Dynamic Testing, more specifically the Pile Driving Analyzer method, a method shown to give a reasonable lower bound of the ultimate load capacity. The neuralnet library in R was used to build and train the ANNs, and the NeuralNetTools library, also in R, was used to perform a sensitivity analysis on the ANNs to determine the most important parameters in determining the ultimate compressive axial load capacity of the piles. An ANN built with 1 hidden layer and 8 nodes outperformed the Engineering News Formula and the Hiley Method, two conventional pile driving formulas, by both coefficients of correlation and root mean squared error. A sensitivity analysis performed on the ANN found that the number of blows at the end-of-drive, the number of days between driving, the cross-sectional area of the pile and the total penetration of the pile are the most important parameters in determining the load capacity through the ANN.