Structural identification of existing structures is a subject of increasing interest in the civil-engineering community because of its potential to use measurement data to enhance asset-management decision making. An important structuralidentification application is residual-capacity assessment of earthquake-damaged structures. Known to be potentially slow and subjective, current assessment practices rely mostly on expert-conducted visual inspection. Structural-identification techniques can help overcome these shortcomings through improving estimates of residual capacity. Physics-based models are needed to predict structural behavior under future loading. For earthquake-engineering simulations, a large variety of prediction models and techniques exists. While engineers often prefer simplified behavior models for assessment, data-interpretation applications usually involve detailed model classes. Neither choice is appropriate for all situations. To improve upon current practice, this paper contains a proposal for a more rational a-priori model-class selection, based on availability of several sources of information. While, knowledge of the earthquake signal is identified as a main criterion to select model classes and analysis tools, the number of measured frequencies that could be inferred from measurements and the amount of building and material information are other criteria that help select an appropriate model