Post-earthquake reliability assessment of segmental column structures based on nonlinear model updating

In this study, a Bayesian based nonlinear model updating approach is developed to enhance the post-earthquake reliability assessment of posttensioned segmental column structures. According to the proposed procedure, dy-namic global sensitivity analysis (DGSA) approach is firstly performed to analyze the sensitivity of the model outputs to the model parameters, which aims to select the appropriate parameters for model updating, and then, the likelihood function of Bayesian estimation is constructed based on the residual errors between the measured and simulated dynamic responses of segmental column structures. Since the decomposed structural dynamic responses are conducted for model updating, the simulated error variance parameters are also considered as unknown. To simultaneously estimate the unknown model parameters and variance parameters, the maximum likelihood estimation approach is employed in this study. Then, the Cram-Rao lower bound (CRLB) analysis method is applied to estimate uncertainty of the identified model parameters that caused by measurement noise. Once nonlinear model updating is completed, the earthquake-induced failure probability of segmental column structure can be evaluated by performing reliability analysis. In numerical simulations, the feasibility of the proposed reliability assessment approach is validated by using a planar posttensioned segmental column model subjected to seismic loads. Furthermore, a scaled posttensioned segmental column structure subjected to bi-directional earthquake exactions is applied to verify the effectiveness of the proposed approach. Both numeri-cal and experimental results have shown that the proposed strategy is accurate and effective for nonlinear model updating of segmental column structures, and the updated results are capable of using to evaluate the failure probability of such type of structures subjected to strong external excitations.