Effects of prediction error bias on model calibration and response prediction of a 10-story building

This paper investigates the application of Hierarchical Bayesian model updating to be used for probabilistic model calibration and response prediction of civil structures. In this updating framework the misfit between the identified modal parameters and the corresponding parameters of the finite element (FE) model is considered as a Gaussian distribution with unknown parameters. For response prediction, both the structural parameters of the FE model and the parameters of the misfit error functions are considered. The focus of this paper is to (1) evaluate the performance of the proposed framework in predicting the structural modal parameters at a state that the FE model is not calibrated (extrapolation from the model), and (2) study the effects of prediction error bias on the accuracy of the predicted values. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state were identified from ambient vibration data using the NExT-ERA system identification method. The identified modal parameters are used to calibrate parameters of the initial FE model as well as the misfit error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after wall removal. These data are not used to calibrate the model; they are only used to validate the predicted results. The model updating framework of this paper is applied to estimate the modal parameters of the building after removal of the six walls. Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests.