Measurements are increasingly used to augment traditional assessments of structural state. Measurements of deflections, rotations, and strain provide indications of damages as well as changes in the values of parameters such as Young's modulus. Finite-element model-updating methods have been developed in the 1990s for identifying structural state from measurements. Currently most methods aim to determine the values of stiffness coefficients that result in measured responses. In the present work, a model calibration method identifies causes of structural behavior such as support conditions and material properties. Static measurement data are employed for model calibration. A case study of the Lutrive Bridge in Switzerland illustrates the methodology. Candidate models whose responses reasonably match measured data are identified. These models are then examined to determine whether the calibrated values are physically possible. Such examinations lead to either model rejection or further measurements.

System identification methodologies are useful for identifying characteristics of structural systems using measurement data. However, incorrect systems might be identified when many combinations of system characteristics result in the same predicted responses at measured locations. The reliability of identification is affected by a number of factors that most previous work has overlooked. This paper presents a system identification methodology that explicitly treats factors that affect the success of identification. Rather than simply determining parametric values, this methodology also involves identification of model characteristics including boundary conditions. Due to inevitable modeling errors, models that provide absolute minimum differences between predictions and measurements are rarely correct models. In such situations, the challenge is to define a population of candidate models that result in such differences being below threshold values that are determined by the magnitude of modeling errors. ...