  Bayesian model updating facilitates the calibration of analytical models
based on observations and the quantification of uncertainties in model
parameters such as stiffness and mass. This process significantly enhances
damage assessment and response predictions in existing civil structures.
Predominantly, current methods employ modal properties identified from
acceleration measurements to evaluate the likelihood of the model parameters.
This modal analysis-based likelihood generally involves a prior assumption
regarding the mass parameters. In civil structures, accurate determination of
mass parameters proves challenging owing to the significant uncertainty and
time-varying nature of imposed loads. The resulting inaccuracy potentially
introduces biases while estimating the stiffness parameters, which affects the
assessment of structural response and associated damage. Addressing this issue,
the present study introduces a stress-resultant-based approach for Bayesian
model updating independent of mass assumptions. This approach utilizes system
identification on strain and acceleration measurements to establish the
relationship between nodal displacements and elemental stress resultants.
Employing static analysis to depict this relationship aids in assessing the
likelihood of stiffness parameters. Integrating this static-analysis-based
likelihood with a modal-analysis-based likelihood facilitates the simultaneous
estimation of mass and stiffness parameters. The proposed approach was
validated using numerical examples on a planar frame and experimental studies
on a full-scale moment-resisting steel frame structure.
