Finite element model updating method for continuous girder bridges using monitoring responses and traffic videos

Accurate finite element (FE) models play an essential role in the health monitoring of operational bridges. Static structural deflections caused by vehicles, which are used in traditional finite element model updating (FEMU) methods, are often procured from field tests, interrupting the traffic and limiting the test loading scenarios. This study proposes a FEMU method that directly applies the massive, multi-source structural and traffic data in the operation phase to update the FE model, effectively solving the defects above. We use the computer vision-based vehicle load identification technique to accurately locate and weigh vehicle loads and carry out static simulations in the FE model based on the identified vehicle loads. The proposed FEMU objective function is established using indices including dynamic structural characteristics and vehicle-induced static structural responses. The static error index in the objective function integrates the curve shape and extrema difference of theoretic and measured static responses. Finally, we deploy a parallel particle swarm optimization (PSO) algorithm to find the global optimal updated FE model. A continuous scale bridge model is employed in the experimental studies with four typical scenarios. Compared to the results from the initial FE model, the updated FE model provides significantly better results both in dynamic and static aspects in all scenarios, and the static error indexes reduce by 75% on average. The proposed method offers a practical approach to deploying the monitoring data for real-time FEMU, which considers dynamic and static features and provides a basis for damage detection, performance assessment, and management.

Automated summary

This study proposes a new method for updating FE models in real time by directly applying massive structural and traffic data, achieving good results. Experimental results show that the updated FE model significantly improves in both dynamic and static aspects, with an average 75% reduction in static error indexes.