Previsão de recalques em fundações profundas utilizando redes neurais artificiais do tipo Perceptron

• Ano de defesa: 2013
• Autor(a) principal: Amancio, Luciana Barbosa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Palavras-chave em Português: Geotecnia; Fundações (Engenharia); Redes neurais (Computação)
• Link de acesso: http://www.repositorio.ufc.br/handle/riufc/7980

The settlement deep foundations preview of stakes continuous helix, metallic dug and stuck is the aim of this study. The settlement is a vertical down dislocation a foundation shows when it undergoes a determined charge. The settlements assessment in deep foundations can be done using several methods as, for instance, the numerical and the theoretical ones. Different variables influence the settlements occurred in foundations of the stake kind which can be detached, among them, the characteristics of resistance and deformation of the involved material, the stratigraphy of the foundation ground and the geometry of the foundation’s structural element manifesting, thus, a multi-diverse and high-complex problem. An alternative to a more realistic assessment of the settlements in deep foundations consists in the application of the artificial neural networks, models that work analogically in the human brain which have been recently contributing to the resolution of complex problems in different areas of Civil Engineering. In this research, multi-marked neural networks were used, fed ahead (perceptron multi-layer) to develop a preview model of settlements in stakes, since a managed training which uses the error back propagation algorithm. To the development of the model, SPT experiments and static charge tests’ results were collected and, with the help of QNET 2000 program, several neural network models were tested and validated. After the analysis and comparison of the different configurations’ results, it was verified that the artificial neural networks were able to understand the deep foundations behavior, continuous helix, metallic dug and stuck kind concerned to the influence of entrance variables considered to the settlements assessment. Furthermore, the results obtained by the developed model allow, through other factors, the definition of work charges and limit charges on the stake. The architecture of this model is formed by 6 knots in the entrance layer, 20 neurons distributed in 3 hidden layers and 1 neuron in the exit layer, corresponding to the measured settlements to the stake. The change process of the synaptic heights, in the model’s validation stage, with 4 million iterations, resulted in the bigger correlation coefficient between the assessed and the measured settlements (0.89), which is satisfactory regarding the preview of a complex phenomenon.