Navegando por Assunto "Monitoramento de integridade estrutural"

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Acesso aberto (Open Access)
Desenvolvimento de sensores baseados em fibra óptica para monitoramento estrutural
(Universidade Federal do Pará, 2016-04-01) FERNANDES, Cindy Stella; GIRALDI, Maria Thereza Miranda Rocco; http://lattes.cnpq.br/0270661833822671; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
The research and development of systems for measuring physical and mechanical parameters of structural health, allows the early detection of collapses, deterioration process and other natural and / or caused by man factors. In this case, real-time measurements of these parameters are crucial for identification, localization and quantification of damages, and also improve the maintenance and safety of such structures. Curvature and vibration measurements are important to structural monitoring due to their relationship with the dynamic responses of engineering structures. In this thesis, core diameter mismatch structures are proposed and experimentally investigated for curvature and vibration sensing. Among the analyzes, two settings exhibited improved performance, one approach uses a structure formed by splicing an uncoated short section of multimode fiber between two standard single mode fibers (SMS), combined to an optical fiber mirror at its end, and the other approach is made by the sandwich of one single mode fiber section between two short multimode fiber sections, spliced between two standard single mode fibers (SMSMS). The SMS device is analyzed through experimental measurements and numerical simulations. In the curvature analysis, the proposed SMS sensor generates destructive interference patterns whether it is bent, varying only the attenuation of the optical signal without wavelength shifts. Numerical modeling is performed using the finite difference beam propagation method by means of the BeamProp 9.0 software of Rsoft™ company. In the experimental vibration analysis, the SMSMS sensor is formed by two MMF sections that act as coupler and re-coupler of core-cladding modes, and the SMF section in the middle acts as the "arm" of interference. So the cladding modes that propagate in the SMF middle section become sensitive to the applied frequencies. The SMSMS vibration sensor proved to be suitable to monitor very low frequencies such as 0.1 Hz. The proposed sensors configurations present several interesting features, such as easy fabrication, low-cost, high-efficiency, and high sensitivity. Although the manufacturing process of the structures is not very precise, which affects its reproducibility, such sensors very useful in a wide range of applications, such as structural health monitoring.
Acesso aberto (Open Access)
Output-only methods for damage identification in structural health monitoring
(Universidade Federal do Pará, 2017-04-27) SANTOS, Adam Dreyton Ferreira dos; FIGUEIREDO, Elói João Faria; http://lattes.cnpq.br/2315380423001185; COSTA, João Crisóstomo Weyl Albuquerque; http://lattes.cnpq.br/9622051867672434
In the structural health monitoring (SHM) field, vibration-based damage identification has become a crucial research area due to its potential to be applied in real-world engineering structures. Assuming that the vibration signals can be measured by employing different types of monitoring systems, when one applies appropriate data treatment, damage-sensitive features can be extracted and used to assess early and progressive structural damage. However, real-world structures are subjected to regular changes in operational and environmental conditions (e.g., temperature, relative humidity, traffic loading and so on) which impose difficulties to identify structural damage as these changes influence different features in a distinguish manner. In this thesis by papers, to overcome this drawback, novel output-only methods are proposed for detecting and quantifying damage on structures under unmeasured operational and environmental influences. The methods are based on the machine learning and artificial intelligence fields and can be classified as kernel- and cluster-based techniques. When the novel methods are compared to the state-of-the-art ones, the results demonstrated that the former ones have better damage detection performance in terms of false-positive (ranging between 3.65.4%) and false-negative (ranging between 0-2.6%) indications of damage, suggesting their applicability for real-world SHM solutions. If the proposed methods are compared to each other, the cluster-based ones, namely the global expectation-maximization approaches based on memetic algorithms, proved to be the best techniques to learn the normal structural condition, without loss of information or sensitivity to the initial parameters, and to detect damage (total errors equal to 4.4%).