Predicting the usefulness of monitoring for identifying the behavior of structures

Structures can be better understood through structural identification. This is where measurement data are used to improve modeling of structural behavior. During structural identification, uncertainties may limit the extent of such understanding. The objective of this paper is to determine probabilistically to what degree measurements are useful for structural identification. The procedure is intended to be used prior to monitoring. The new methodology evaluates the probability of occurrence of two performance indices; the expected number of candidate models and the expected prediction ranges. Since it does not require intervention on the structure, the method can be used to support prioritization of decisions related to full-scale testing. These features are illustrated through the study of the Langensand Bridge (Switzerland). In this example, the methodology shows that increases in modeling uncertainties hinders the usefulness of measurements for identifying model parameter values. The predictive capability of the method proposed is verified by agreement with observations made during a recent identification exercise. Quantifying the expected identifiability is able to support infrastructure decision-making such as determining whether or not certain types of structural monitoring are useful.

Mots clés

Expected identifiability, System identification, Bridge monitoring, Modelupdating, Residual minimization, Errors, Uncertainties, Correlation