CORDIS OpenAire Datacite Sygma UnpayWall Multidisciplinary Digital Publishing Institute DOAJ Crossref Research Collection Microsoft Academic Graph RE.PUBLIC@POLIMI Research Publications at Politecnico di Milano Europe PubMed Central PubMed Central European Union Open Data Portal http://www.mdpi.com/1424-8220/18/7/2174/pdf https://re.public.polimi.it/bitstream/11311/1085132/1/Sensors_2018b.pdf https://doi.org/10.3929/ethz-b-000278733 Sensors Open Access 2018-07-06 2018-01-01 2019-04-30 2018-07-01 <p>Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost￢ﾀﾓbenefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information￢ﾀﾓcost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.</p> Stochastic Processes structural health monitoring structural health monitoring; Bayesian inference; cost–benefit analysis; stochastic optimization; information theory; Bayesian experimental design; surrogate modeling; model order reduction Chemical technology Cost-Benefit Analysis Bayesian inference Bayesian experimental design Uncertainty Bayes Theorem TP1-1185 02 engineering and technology stochastic optimization Bayesian experimental design; Bayesian inference; Benefit analysis; Cost; Information theory; Model order reduction; Stochastic optimization; Structural health monitoring; Surrogate modeling; Algorithms; Monte Carlo Method; Nonlinear Dynamics; Stochastic Processes; Uncertainty; Bayes Theorem; Cost-Benefit Analysis; Analytical Chemistry; Atomic and Molecular Physics, and Optics; Biochemistry; Instrumentation; Electrical and Electronic Engineering Article surrogate modeling 0201 civil engineering Nonlinear Dynamics model order reduction cost–benefit analysis Monte Carlo Method Algorithms information theory Giovanni Capellari, Eleni Chatzi, Stefano Mariani, 2018-07-06 journalpaper <p>Structural health monitoring (SHM) allows the acquisition of information on the structural integrity of any mechanical system by processing data, measured through a set of sensors, in order to estimate relevant mechanical parameters and indicators of performance. Herein we present a method to perform the cost￢ﾀﾓbenefit optimization of a sensor network by defining the density, type, and positioning of the sensors to be deployed. The effectiveness (benefit) of an SHM system may be quantified by means of information theory, namely through the expected Shannon information gain provided by the measured data, which allows the inherent uncertainties of the experimental process (i.e., those associated with the prediction error and the parameters to be estimated) to be accounted for. In order to evaluate the computationally expensive Monte Carlo estimator of the objective function, a framework comprising surrogate models (polynomial chaos expansion), model order reduction methods (principal component analysis), and stochastic optimization methods is introduced. Two optimization strategies are proposed: the maximization of the information provided by the measured data, given the technological, identifiability, and budgetary constraints; and the maximization of the information￢ﾀﾓcost ratio. The application of the framework to a large-scale structural problem, the Pirelli tower in Milan, is presented, and the two comprehensive optimization methods are compared.</p> Cost–benefit optimization of structural health monitoring sensor networks 10.3929/ethz-b-000278733