Bayesian Finite Element Model Updating of a Long-Span Suspension Bridge Utilizing Hybrid Monte Carlo Simulation and Kriging Predictor

Abstract

Bayesian model updating technique has been widely investigated and utilized in the field of finite element model (FEM) updating for its advantages in system uncertainty quantification. Most existing studies focus on numerical and experimental models. More studies on large-scale civil infrastructures based on field monitoring are still required. A case study on Bayesian FEM updating of the Runyang Suspension Bridge (RSB), a long-span suspension bridge with a main span of 1,490 m, is carried out in this paper. The Bayesian updating method is utilized to update the initial FEM of RSB, aiming to make the numerical modal properties match the field monitoring results. Two stochastic sampling algorithms, i.e., the Metropolis-Hastings (MH) algorithm and the Hybrid Monte Carlo (HMC) algorithm, are respectively investigated to show their advantages and limitations in Bayesian updating. Subsequently, based on the experimentalsamples generated by the Latin hypercube sampling algorithm, a Kriging predictor is established as a surrogate model to reduce the computational burden of model updating. Results show that the HMC algorithm could guarantee much higher acceptance rate of the sampled chain than the MH algorithm especially when the updating step size is large. In addition, combined with the Kriging predictor, Bayesian model updating method could serve as an effective and efficient tool to calibrate the FEM of large-scale civil infrastructures.