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%).
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