Vibration-based structural health monitoring (SHM) for long-span bridges has become a dominant research topic in recent years. The Nam O Railway Bridge is a large-scale steel truss bridge located on the unique main rail track from the north to the south of Vietnam. An extensive vibration measurement campaign and model updating are extremely necessary to build a reliable model for health condition assessment and operational safety management of the bridge. The experimental measurements are carried out under ambient vibrations using piezoelectric sensors, and a finite element (FE) model is created in MATLAB to represent the physical behavior of the structure. By model updating, the discrepancies between the experimental and the numerical results are minimized. For the success of the model updating, the efficiency of the optimization algorithm is essential. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed to update the unknown model parameters. The result shows that PSO not only provides a better accuracy between the numerical model and measurements, but also reduces the computational cost compared to GA. This study focuses on the stiffness conditions of typical joints of truss structures. According to the results, the assumption of semi-rigid joints (using rotational springs) can most accurately represent the dynamic characteristics of the truss bridge considered.

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Data-driven structural health monitoring using feature fusion and hybrid deep learning. IEEE Transactions on Automation Science and Engineering .

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