Stress-Resultant-Based Approach to Mass-Assumption-Free Bayesian Model Updating of Frame Structures
arxiv(2024)
Abstract
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, accurately determining mass
parameters proves challenging owing to the 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.
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