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Mechanics-based dynamic models are commonly used in the design and performance assessment of structural systems, and their accuracy can be improved by integrating models with measured data. This paper provides an overview of hierarchical Bayesian model updating which has been recently developed for probabilistic integration of models with measured data, while accounting for different sources of uncertainties and modeling errors. The proposed hierarchical Bayesian framework allows one to explicitly account for pertinent sources of variability such as ambient temperatures and/or excitation amplitudes, as well as modeling errors, and therefore yields more realistic predictions. The paper reports observations from applications of hierarchical approach to three full-scale civil structural systems, namely (1) a footbridge, (2) a 10-story reinforced concrete (RC) building, and (3) a damaged 2-story RC building. The first application highlights the capability of accounting for temperature effects within the hierarchical framework, while the second application underlines the effects of considering bias for prediction error. Finally, the third application considers the effects of excitation amplitude on structural response. The findings underline the importance and capabilities of the hierarchical Bayesian framework for structural identification. Discussions of its advantages and performance over classical deterministic and Bayesian model updating methods are provided. 

This paper discusses the dynamic tests of a two‐story infilled reinforced concrete (RC) frame building using an eccentric‐mass shaker. The building, located in El Centro, CA, was substantially damaged prior to the tests due to the seismic activity in the area. During the testing sequence, five infill walls were removed to introduce additional damage states and to investigate the changes in the dynamic properties and the nonlinear response of the building to the induced excitations. The accelerations and displacements of the structure under the forced and ambient vibrations were recorded through an array of sensors, while lidar scans were obtained to document the damage. The test data provide insight into the nonlinear response of an actual building and the change of its resonant frequencies and operational shapes due to varying damage levels and changes of the excitation amplitude, frequency, and orientation.