307 shared publications
Sport Business Management, Sheffield Hallam University, Sheffield, UK
107 shared publications
TECHDYN Engineering, Via A. Rendano 18, I-00199 Rome, Italy
100 shared publications
Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Zürich, Switzerland
75 shared publications
Dipartimento di Ingegneria Civile ed Ambientale, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy
56 shared publications
Department of Economics and Management, University of Florence, Florence, Italy
(1970 - 2018)
Sensors networks for the health monitoring of structural systems have to be designed to achieve both accurate estimations of the relevant mechanical parameters and low cost of the experimental equipment. Therefore, the number, type and location of the sensors have to be chosen so that the uncertainties related to the estimated health are minimized. Several deterministic methods based on the sensitivity of measures with respect to the parameters to be tuned are widely used; despite their low computational cost, these methods do not take into account the uncertainties related to the measurement process.
In former studies, a method based on the maximization of the information associated with the available measurements has been proposed and the use of approximate solutions has been extensively discussed. Here we propose a robust numerical procedure to solve the optimization problem: in order to reduce the computational cost of the overall procedure, Polynomial Chaos Expansion and a stochastic optimization method are employed.
The method is applied to a flexible plate. First of all, we investigate how the information changes with the number of sensors; then we analyze the effect of choosing different types of sensors (with their relevant accuracy) on the information provided by the structural health monitoring system.