ABSTRACT
This paper presents a probabilistic structural identification (SI) framework using the time-dependent deflection of prestressed concrete bridges (PSCBs). The framework involves a stochastic volatile model (SVM) that considers the full ranges of the unobserved state variables affecting the time-dependent deflection of PSCBs, namely, structural rigidity, creep, shrinkage, prestress level and dead load level, along with the volatility parameters associated with the deterioration path of structural rigidity, which is modeled via the Wiener process. Exploiting the latent structure of the SVM, a cyclic Markov chain Monte Carlo sampler is proposed to draw samples from the joint posterior distribution of the unobserved state variables and volatility parameters. In an illustrative example, deflection measurements of an existing bridge are used as the target information for SI. The updated model can reveal the accumulated damage of the case bridge over the monitoring period.
