Dynamic stiffness of the piled foundation of a onshore wind turbine

One of today's biggest concerns is the increase of energetic needs, especially in the developed countries. Among various clean energies, wind energy is one of the technologies that assume greater importance on the sustainable development of humanity. Despite wind turbines had been developed and studied over the years, there are phenomena that haven't been yet fully understood. This work studies the soil-structure interaction that occurs on a wind turbine's foundation composed by a group of piles that is under dynamic loads caused by wind. This problem assumes special importance when the foundation is implemented on locations where safety criteria are very demanding, like the case of a foundation mounted on a dike. To the phenomenon of interaction between two piles and the soil between them it's given the name of pile-soil-pile interaction. It is known that such behavior is frequency dependent, and therefore, on this work evaluation of relevant frequencies for the intended analysis is held. During the development of this thesis, two methods were selected in order to assess pile-soil-pile interaction, being one of analytical nature and the other of numerical origin. The analytical solution was recently developed and its called Generalized pile-soil-pile theory, while for the numerical method the commercial nite element software PLAXIS 3D was used. A study of applicability of the numerical method is also done comparing the given solution by the nite element methods with a rigorous solution widely accepted by the majority of the authors.

Human activity recognition with accelerometry: novel time and frequency features

Human Activity Recognition systems require objective and reliable methods that can be used in the daily routine and must offer consistent results according with the performed activities. These systems are under development and offer objective and personalized support for several applications such as the healthcare area. This thesis aims to create a framework for human activities recognition based on accelerometry signals. Some new features and techniques inspired in the audio recognition methodology are introduced in this work, namely Log Scale Power Bandwidth and the Markov Models application. The Forward Feature Selection was adopted as the feature selection algorithm in order to improve the clustering performances and limit the computational demands. This method selects the most suitable set of features for activities recognition in accelerometry from a 423th dimensional feature vector. Several Machine Learning algorithms were applied to the used accelerometry databases – FCHA and PAMAP databases - and these showed promising results in activities recognition. The developed algorithm set constitutes a mighty contribution for the development of reliable evaluation methods of movement disorders for diagnosis and treatment applications.