Prediction of axial capacity of piles driven in non-cohesive soils based on neural networks approach

Abstract

This paper presents an application of two advanced approaches, Artificial Neural Networks (ANN) and Princi­pal Component Analysis (PCA) in predicting the axial pile capacity. The combination of these two approaches allowed the development of an ANN model that provides more accurate axial capacity predictions. The model makes use of Back-Propagation Multi-Layer Perceptron (BPMLP) with Bayesian Regularization (BR), and it is established through the incorporation of approximately 415 data sets obtained from data published in the literature for a wide range of un-cemented soils and pile configurations. The compiled database includes, respectively 247 and 168 loading tests on large-and low-displacement driven piles. The contributions of the soil above and below pile toe to the pile base resistance are pre-evaluated using separate finite element (FE) analyses. The assessment of the predictive performance of the new method against a number of traditional SPT-based approaches indicates that the developed model has attractive capabili­ties and advantages that render it a promising tool. To facilitate its use, the developed model is translated into simple design equations based on statistical approaches.

Keywords

• neural networks
• principal components analysis
• failure zone
• ultimate capacity
• pile load tests
• FE