Abstract
Vibration-based methods for damage localizations often rely on a damage feature defined in terms of changes of modal shapes and localize damage by detecting shape irregularities that are ascribed to local loss of stiffness. In this paper, the performance of several algorithms for damage localization is investigated and the results are compared in terms of the gain of information they provide, which is measured by the relative information entropy also known as Kullback–Leibner (KL) divergence. This parameter is a measure of the difference between two probability distributions and can quantify the information gain achieved using different statistical models. In this paper the relative entropy is used to compare the gain of information obtained using different damage-sensitive indicators retrieved from simulated structural health monitoring data. The investigation is carried out using structural responses simulated using the finite element model of a real bridge permanently monitored by the Italian Seismic Observatory of Structures.
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