Sensors networks for the health monitoring of structural systems ought to be designed toudrender both accurate estimations of the relevant mechanical parameters and an affordableudexperimental setup. Therefore, the number, type and location of the sensors have to be chosen soudthat the uncertainties related to the estimated health are minimized. Several deterministic methodsudbased on the sensitivity of measures with respect to the parameters to be tuned are widely used.udDespite their low computational cost, these methods do not take into account the uncertaintiesudrelated to the measurement process. In former studies, a method based on the maximization of theudinformation associated with the available measurements has been proposed and the use ofudapproximate solutions has been extensively discussed. Here we propose a robust numericaludprocedure to solve the optimization problem: in order to reduce the computational cost of theudoverall procedure, Polynomial Chaos Expansion and a stochastic optimization method areudemployed. The method is applied to a flexible plate. First of all, we investigate how the informationudchanges with the number of sensors; then we analyze the effect of choosing different types of sensorsud(with their relevant accuracy) on the information provided by the structural health monitoringudsystem.
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