A Bayesian statistical methodology is presented for optimally locating the sensors in a structure for the purpose of extracting the most information about the model parameters which can be used in model updating and in damage detection and localization. This statistical approach properly handles the unavoidable uncertainties in the measured data as well as the uncertainties in the mathematical model used to represent the structural behavior. The optimality criterion for the sensor locations is based on information entropy which is a measure of the uncertainty in the model parameters. The uncertainty in these parameters is computed by the Bayesian statistical methodology and then the entropy measure is minimized over the set of possible sensor configurations using a genetic algorithm. Results presented illustrate how both the minimum entropy of the parameters and the optimal sensor configuration depend on the location of sensors, number of sensors, number and type of contributing modes and the structural parameterization (substructuring) scheme used.
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