Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / Estimar o recalque em estacas à um problema muito complexo, incerto e ainda nÃo totalmente compreendido, devido Ãs muitas incertezas associadas aos fatores que afetam a magnitude desta deformaÃÃo. As RNA sÃo ferramentas que funcionam analogamente ao cÃrebro humano, e sua unidade principal, o neurÃnio artificial, trabalha de maneira semelhante ao neurÃnio biolÃgico. Esta ferramenta alternativa vem sendo aplicada com sucesso em muitos problemas de engenharia geotÃcnica, podendo, portanto ser utilizadas como uma ferramentas alternativas para avaliar recalques em estacas isoladas. Nessa pesquisa as RNA utilizadas foram do tipo perceptron de mÃltiplas camadas, empregando um treinamento supervisionado utilizando o algoritmo de retropropagaÃÃo do erro. O modelo desenvolvido relaciona o recalque em estacas isoladas com as propriedades geomÃtricas das estacas (diÃmetro e comprimento), a estratigrafia e as caracterÃsticas de compacidade, ou consistÃncia dos solos por meio dos resultados obtidos nos ensaios SPT, e a carga atuante, obtidas em provas de carga realizadas em estacas hÃlice contÃnua, cravada metÃlica e escavada. O conjunto de aprendizagem foi composto por 1947 exemplos de entrada e saÃda. Com auxilio do programa QNET2000 foram treinadas e validadas vÃrias arquiteturas de redes neurais. ApÃs comparar o desempenho da curva carga x recalque elaborada com os recalques estimados pelo modelo proposto com a curva carga x recalque resultante da prova de carga estÃtica e com a curva carga x recalque gerada pelo emprego do programa comercial baseado em elementos finitos tridimensionais PLAXIS 3D Foundation, constatou-se que as RNA foram capazes de entender o comportamento das fundaÃÃes profundas do tipo estacas hÃlice contÃnua, escavada e cravada metÃlica, possibilitando dentre outras coisas, a definiÃÃo das cargas de trabalho e cargas limites nas estacas. / Predicting the settlement in deep foundation is a very complex, uncertain and not yet fully understood, due to the many uncertainties associated with factors that affect the magnitude of this deformation. Artificial Neural Network (ANN) is a tool that works similarly to the human brain, its main unit, the artificial neuron, works in a similar way to the biological neuron. This alternative tool has been successfully applied in many geotechnical engineering problems and can therefore be used as an alternative tool to evaluate the behavior of settlement in isolated piles. In this paper, the ANN used were the multilayer perceptron type, employing a supervised training that uses the error back propagation algorithm. The model developed relates settlement in isolated piles with the type and the geometrical properties of the piles (diameter and length), the stratigraphy and characteristics of compactness or consistency of soils by means of the SPT tests results, and the load applied, obtained in static pile load tests performed in continuous helix, steel driven and excavated pile types. The data set used to model consisted of 1.947 samples of input and output. QNET 2000 was the program used to assist the training and validation of various architectures of neural networks. The architecture formed by 10 nodes in the input layer, 28 neurons distributed in 4 intermediate layers and one neuron in the output layer, corresponding to the measured discharge for cutting (A10: 14:8:4:2:1) was the one that showed the best performance, with the correlation coefficient between the estimated settlements and settlements measured during the validation phase of 0.94, such value can be considered satisfactory when considering the prediction of a complex phenomenon. After comparing the performance of the applied load x settlement estimated by model proposed curve with the applied load x settlement measured in static pile load test curve and the applied load x settlement estimated by an elasto-plastic model thru numerical simulation, it was found that the ANN were able to understand the behavior of deep foundations of continuous helix, steel driven and excavated piles type, allowing among other things, the definition of workloads and load limits at the pile.
| Identifer | oai:union.ndltd.org:IBICT/oai:www.teses.ufc.br:8366 |
| Date | 01 August 2014 |
| Creators | Mariana Vela Silveira |
| Contributors | Silvrano Adonias Dantas Neto, Francisco de Assis de Souza Filho, MaurÃcio Martines Sales |
| Publisher | Universidade Federal do CearÃ, Programa de PÃs-GraduaÃÃo em Engenharia Civil, UFC, BR |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Format | application/pdf |
| Source | reponame:Biblioteca Digital de Teses e Dissertações da UFC, instname:Universidade Federal do Ceará, instacron:UFC |
| Rights | info:eu-repo/semantics/openAccess |
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