Hierarchical Bayesian model updating of a long-span arch bridge considering temperature and traffic loads

Framework of the proposed hierarchical Bayesian FEMU method. [Display omitted] •Effects of operational loads are considered in the hierarchical Bayesian FEMU.•Temperature-stiffness relationship and vehicle load estimation are established.•A two-step MCMC sampling and RSM are proposed to accelerate updating process.•Validated on a long-span arch bridge using two-year monitoring data.•An indicator is defined to assess structural states under operational loads. Excluding the variations of structural dynamic characteristics due to operational loads in finite element model updating (FEMU) is essential for response prediction and structural condition assessment. However, existing FEMU techniques for long-span bridges usually directly use modal parameters in the objective function without considering the effects of various operational loads, resulting in high variations of the updated model. To this end, this paper proposes a hierarchical Bayesian FEMU framework considering the effects of operational loads, including temperature and traffic loads. A linear temperature-elastic modulus relationship and a vehicle load estimation method based on weigh-in-motion (WIM) data are established to quantitatively consider the operational loads’ effects on structural dynamic properties. In addition, the identified natural frequencies and measured expansion joint displacements are incorporated into the objective function to update structural static and dynamic properties simultaneously. A two-step Markov Chain Monte Carlo (MCMC) sampling method and a response surface surrogate model are proposed to accelerate the updating process. The proposed method is validated on a long-span arch bridge using two-year monitoring data. The updated model is then used to predict the structural static and dynamic responses by taking into account of operational loads, parameter uncertainties, and modelling errors, and the measured responses generally fall within the predicted 95% confidence interval. Finally, a structural state indicator, which has considered the effects of operational loads on structural properties, is defined to assess the structural condition and successfully detect the pavement replacement process on the bridge.