ABSTRACT
A systematic approach to interpretation of measurement data employs methodologies developed in the field of system identification (Ljung, 1999). System identification involves determining the state of a system and values of system parameters through comparisons of predicted and observed responses. Since measurements are indirect, the use of models is necessary to estimate system parameters. Model-free interpretation of data (Posenato et al., 2008), while identifying anomalies, may not accurately estimate parameters. Even though design models are the most appropriate for designing and analyzing the structure prior to construction, they often cannot be used for system identification. Models that support diagnostic activities such as data interpretation must provide accurate estimations of the real behaviour of existing structures. The current work is a combination of model-based reasoning concepts from computer science (De Kleer and Williams, 1987) and traditional model updating techniques used in engineering (Ljung, 1999).
