Abstract
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.
Highlights
Structural health monitoring and safety assessments for long-span bridges have become a dominant research topic, and have received the special attention of infrastructure authorities in recent years
The result shows that Particle swarm optimization (PSO) provides a better accuracy between the numerical model and measurements, and reduces the computational cost compared to genetic algorithm (GA)
That means that the deviation between numerical model and measurement after model updating using PSO was lower than GA
Summary
Structural health monitoring and safety assessments for long-span bridges have become a dominant research topic, and have received the special attention of infrastructure authorities in recent years. If measured dynamic characteristics (such as natural frequencies, mode shapes, and modal damping ratio, etc.) are available, model updating can be applied to minimize the deviation between numerical prediction and experimental results. Updated a short-span steel railway bridge by applying a time-domain FE model updating approach based on in-situ dynamic displacements under ambient vibrations after train passage, instead of using modal parameters (natural frequency, mode shape, etc.). Regarding its application to bridges, Qin et al [21] combined the particle swarm optimization (PSO) algorithm with a surrogate model to update higher vibration modes for a continuous railway concrete bridge in Spain. Analyzes and selects the scenario that properly reflects the dynamic behavior This approach is applied by some other authors [26,27,28].
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