O presente trabalho faz um estudo da representação da protensão em estruturas de barras através de um Conjunto de Cargas Concentradas Equivalentes para determinação dos esforços solicitantes e dos deslocamentos, gerados pela protensão. O trabalho aborda a conceituação de protensão, forças de desvio e perdas imediatas de protensão. Na sequência discute-se alguns métodos para determinação de esforços de protensão, inclusive para o caso de peças hiperestáticas, como por exemplo o método dos esforços solicitantes iniciais e o da carga distribuída equivalente. A seguir discute-se o algoritmo em estudo - Conjunto de Cargas Concentradas Equivalentes, CCCE (também conhecido como Método da Força Variável), suas vantagens e aplicações. Na parte final compara-se, através de exemplos, a aplicabilidade e precisão do CCCE com alguns dos métodos mais tradicionais citados anteriormente assim como as vantagens e desvantagens de cada um deles. / This work is a study about the representation of the prestressing through a CELG (Concentrated Equivalent Loads Group) in order to determine the internal forces and displacements in prestressed structures, due to prestressing. This study considers the concept of prestressing, deviation forces and immediate loss of prestressing. Furthermore some alternative methods to determine forces of prestressing are discussed including the case of hiperestatic structures e.g. initial forces and equivalent distributed loads. Next, the studied algorithm is discussed - CELG, (also known as Variable Force Method), its advantages and uses. Finally the use and precision of CELG is compared to some of the most traditional methods quoted beforehand and also its advantages and disadvantages.

O presente trabalho faz um estudo da representação da protensão em estruturas de barras através de um Conjunto de Cargas Concentradas Equivalentes para determinação dos esforços solicitantes e dos deslocamentos, gerados pela protensão. O trabalho aborda a conceituação de protensão, forças de desvio e perdas imediatas de protensão. Na sequência discute-se alguns métodos para determinação de esforços de protensão, inclusive para o caso de peças hiperestáticas, como por exemplo o método dos esforços solicitantes iniciais e o da carga distribuída equivalente. A seguir discute-se o algoritmo em estudo - Conjunto de Cargas Concentradas Equivalentes, CCCE (também conhecido como Método da Força Variável), suas vantagens e aplicações. Na parte final compara-se, através de exemplos, a aplicabilidade e precisão do CCCE com alguns dos métodos mais tradicionais citados anteriormente assim como as vantagens e desvantagens de cada um deles. / This work is a study about the representation of the prestressing through a CELG (Concentrated Equivalent Loads Group) in order to determine the internal forces and displacements in prestressed structures, due to prestressing. This study considers the concept of prestressing, deviation forces and immediate loss of prestressing. Furthermore some alternative methods to determine forces of prestressing are discussed including the case of hiperestatic structures e.g. initial forces and equivalent distributed loads. Next, the studied algorithm is discussed - CELG, (also known as Variable Force Method), its advantages and uses. Finally the use and precision of CELG is compared to some of the most traditional methods quoted beforehand and also its advantages and disadvantages.

In recent years, machine learning applications have gained great attention in the wind power industry. Among these, artificial neural networks have been utilized to predict the fatigue loads of wind turbine components such as rotor blades. However, the limited number of contributions and differences in the used databases give rise to several questions which this study has aimed to answer. Therefore, in this study, 5-min SCADA data from the Lillgrund wind farm has been used to train two feed-forward neural networks to predict the fatigue loads at the blade root in flapwise and edgewise directions in the shape of damage equivalent loads.The contribution of different features to the model’s performance is evaluated. In the absence of met mast measurements, mesoscale NEWA data are utilized to present the free flow condition. Also, the effect of wake condition on the model’s accuracy is examined. Besides, the generalization ability of the model trained on data points from one or multiple turbines on other turbines within the farm is investigated. The results show that the best accuracy was achieved for a model with 34 features, 5 hidden layers with 100 neurons in each hidden layer for the flapwise direction. For the edgewise direction, the best model has 54 features, 6 hidden layers, and 125 neurons in each hidden layer.For a model trained and tested on the same turbine, mean absolute percentage errors (MAPE) of 0.78% and 9.31% are achieved for the flapwise and edgewise directions, respectively. The seen difference is argued to be a result of not having enough data points throughout the range of edgewise moments. The use of NEWA data has been shown to improve the model’s accuracy by 10% for MAPE values, relatively. Training the model under different wake conditions did not improve the model showing that the wake effects are captured through the input features to some extent. Generalization of the model trained on data points from one turbine resulted in poor results in the flapwise direction. It was shown that using data points from multiple turbines can improve the model’s accuracy to predict loading on other turbines.