Entropy-Based Optimal Sensor Location for Structural Model Updating

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Bayesian Updating of Structural Models and Reliability using Markov Chain Monte Carlo Simulation (2002, background): “…~Beck and Katafygiotis 1998; Engelund and Sorensen 1998; Geyskens et al. 1998; Katafygiotis and Beck 1998; Katafygiotis et al. 1998; Singhal and Kiremidjian 1998; Zheng and Ellingwood 1998; Enright and Frangopol 1999; Papadimitriou et al. 2000; Papadimitriou et al. 2001; Vanik et al. 2000!.”
The effect of prediction error correlation on optimal sensor placement in structural dynamics (2012, methodology): “Since the information entropy gives the amount of useful information contained in the measured data, the optimal sensor configuration is selected as the one that minimizes the information entropy [14].”
Optimal sensor placement methodology for parametric identification of structural systems (2004, background): “An important advantage of the information entropy measure is that it allows us to make comparisons between sensor configurations involving a different number of sensors in each configuration [14,16].”
Pareto optimal sensor locations for structural identification (2005, methodology): “The information entropy [13] is used as the optimality criterion for optimizing the sensor configuration for a single model class.”