Estimative of shaft and tip bearing capacities of single piles using multilayer perceptrons

Abstract

There are an increasing number of studies that use the artificial neural networks (ANN) as a prediction tool in the field of foundations with satisfactory results. In this paper, multilayer perceptrons are used to develop prediction models for the shaft and tip bearing capacities of single piles based on a supervised training using the error back propagation algorithm. Results from static load tests carried out on 95 instrumented single piles executed in different regions of Brazil were used in the ANN modelling. The prediction models of shaft and tip bearing capacities of single piles were obtained portraying indicated in the validation phase determination coefficients equal to 95% and 99%, respectively. To demonstrate their applicability and efficiency, such models were used to estimate the bearing capacity of single piles unused in the models’ development, as well as groups of two and three piles. The results demonstrated that the neuron models were much closer to the values of the bearing capacities measured in single pile tests and groups of piles, than the estimated results using semi-empirical methods. As a result of overestimating the predicted bearing capacities in relation to the results of the load tests, it is recommended to use models applying reduction factors of 0.88 for single piles, and 0.75 for groups of up to three piles.