This paper presents an approach for model identifiability that builds upon recent research into measurement data interpretation. The objective of this approach is to determine probabilistically to what degree the number of models able to explain a measured behaviour can be reduced in comparison to the initial solution space. The procedure is intended to be used prior to obtaining measurements from full-scale testing. The new methodology evaluates the probability of occurrence of two performance indices; the expected number of candidate models and the expected parameter range. It allows users, prior to taking measurements, to determine whether or not performing tests is likely to be useful. Since it does not require any intervention on the structure, this method may be used for a fraction of the cost required for full-scale testing. These features are illustrated through a case study, the Langensand Bridge (Switzerland). The methodology is the basis for a new generation of sensor placement techniques that determine to what extent particular sensor and load configurations are useful.