Modelo de previsão de recalques em estacas hélice contínua, metálicas e escavadas através de redes neurais artificiais

Abstract

This work presents a method for pile settlement prediction using an artificial neural network(ANN) known as perceptron. Results from 199 SPT and static load tests carried out on continuous flight auger,steel and bored piles were used in the model development. The ANN model allows the prediction ofsettlements as a function of the pile type and geometry (diameter and length), the stratigraphy andcharacteristics of soils defined by SPT test results, and the applied load. The architecture that presented thebest performance was A10:14:8:4:2:1, with a correlation coefficient of 0.94. The results were compared tothose obtained in previous works and by other authors that also developed settlement prediction models usingANNs. This comparison has shown that the proposed model is able to predict settlements more accurately