renato alves ferreira - Universidade Estadual de Feira de Santana

ABSTRACT
Considering the contribution of Artificial Neural Networks (ANN) in several areas of
civil engineering, in determination the behavior of various phenomena, this study aims to
verify the feasibility of using ANN in predicting the load capacity of two distinct types of
piles: in concrete drive spiked and continuous flight auger excavated. These networks will
relate the geometrical properties of the piles (length and cross section) and the values of the
SPT (standard penetration test) of the tip and lateral of the pile, with the lateral load capacity,
tip and total deep foundations. With this information and the results obtained in static and
dynamic load tests performed on the entire national territory, available in publications, articles
from academic and available by specialist companies, was organized organize a database of
test load continuous flight auger excavated and for in concrete drive. With these data
organized, the ANN were trained through these, prediction equations were obtained, capable
of deduce the ability of lateral support, tip and full. The values of load capacity obtained
through the technique of ANN presented higher accuracy than traditional methods of Aoki &
Veloso and Décourt & Quaresma, which demonstrates the feasibility of using ANN with a
tool for predicting the load capacity of deep foundations.
KEYWORDS : Neural Networks – Load tests – Deep Foundations