Experimental verification for load rating of steel truss bridge using an improved Hamiltonian Monte Carlo-based Bayesian model updating

The load rating of a steel truss bridge is experimentally identified in this study using an improved Bayesian model updating algorithm. The initial element model is sequentially updated to match the static and dynamic characteristics of the bridge. For this purpose, a modified version of the Hamiltonian Monte Carlo (HMC) simulation is adopted for closed-form candidate generation that helps in faster convergence compared to the Markov Chain Monte Carlo simulation. The updated model works as a digital twin of the original structure to predict its load-carrying capacity and performance under proof or design load. The proposed approach incorporates in-situ conditions in its formulation and helps to reduce the risk involved in bridge load testing at its full capacity. The rating factor for each member is estimated from the updated model, which also indicates the weak links and possible failure mechanism. The efficiency of the improved HMC-based algorithm is demonstrated using limited sensor data, which can be easily adopted for other existing bridges.