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Model Updating for Nam O Bridge Using Particle Swarm Optimization Algorithm and Genetic Algorithm

机译：基于粒子群优化算法和遗传算法的Nam O桥模型修正。

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摘要

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.
机译：近年来，基于振动的大跨度桥梁结构健康监测（SHM）已成为研究的主要课题。 Nam O铁路桥是大型的钢桁架桥，位于越南北部至南部独特的主要铁路轨道上。要为桥梁的健康状况评估和运营安全管理建立可靠的模型，就必须进行广泛的振动测量活动和模型更新。使用压电传感器在环境振动下进行实验测量，并在MATLAB中创建一个有限元（FE）模型来表示结构的物理行为。通过模型更新，可以将实验结果与数值结果之间的差异最小化。为了成功更新模型，优化算法的效率至关重要。采用粒子群算法（PSO）和遗传算法（GA）更新未知模型参数。结果表明，与遗传算法相比，粒子群优化算法不仅在数值模型和测量之间提供了更好的精度，而且降低了计算成本。这项研究的重点是桁架结构典型接头的刚度条件。根据结果​​，半刚性接头（使用旋转弹簧）的假设可以最准确地表示所考虑的桁架桥的动力特性。

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