Engineering structures have played an important role into societies across
the years. A suitable management of such structures requires automated structural
health monitoring (SHM) approaches to derive the actual condition of the system.
Unfortunately, normal variations in structure dynamics, caused by operational and
environmental conditions, can mask the existence of damage. In SHM, data normalization
is referred as the process of filtering normal effects to provide a proper
evaluation of structural health condition. In this context, the approaches based on
principal component analysis and clustering have been successfully employed to
model the normal condition, even when severe effects of varying factors impose difficulties
to the damage detection. However, these traditional approaches imposes
serious limitations to deployment in real-world monitoring campaigns, mainly due
to the constraints related to data distribution and model parameters, as well as
data normalization problems. This work aims to apply deep neural networks and
propose a novel agglomerative cluster-based approach for data normalization and
damage detection in an effort to overcome the limitations imposed by traditional
methods. Regarding deep networks, the employment of new training algorithms
provide models with high generalization capabilities, able to learn, at same time,
linear and nonlinear influences. On the other hand, the novel cluster-based approach
does not require any input parameter, as well as none data distribution assumptions
are made, allowing its enforcement on a wide range of applications. The superiority
of the proposed approaches over state-of-the-art ones is attested on standard
data sets from monitoring systems installed on two bridges: the Z-24 Bridge and
the Tamar Bridge. Both techniques revealed to have better data normalization and
classification performance than the alternative ones in terms of false-positive and
false-negative indications of damage, suggesting their applicability for real-world
structural health monitoring scenarios.
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