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Mode identifiability of a cable-stayed bridge under different excitation conditions assessed with an improved algorithm based on stochastic subspace identification

机译:基于随机子空间识别的改进算法评估斜拉桥在不同激励条件下的模式识别

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Deficient modes that cannot be always identified from different sets of measurement data may exist in the application of operational modal analysis such as the stochastic subspace identification techniques in large-scale civil structures. Based on a recent work using the long-term ambient vibration measurements from an instrumented cable-stayed bridge under different wind excitation conditions, a benchmark problem is launched by taking the same bridge as a test bed to further intensify the exploration of mode identifiability. For systematically assessing this benchmark problem, a recently developed SSI algorithm based on an alternative stabilization diagram and a hierarchical sifting process is extended and applied in this research to investigate several sets of known and blind monitoring data. The evaluation of delicately selected cases clearly distinguishes the effect of traffic excitation on the identifiability of the targeted deficient mode from the effect of wind excitation. An additional upper limit for the vertical acceleration amplitude at deck, mainly induced by the passing traffic, is subsequently suggested to supplement the previously determined lower limit for the wind speed. Careful inspection on the shape vector of the deficient mode under different excitation conditions leads to the postulation that this mode is actually induced by the motion of the central tower. The analysis incorporating the tower measurements solidly verifies this postulation by yielding the prevailing components at the tower locations in the extended mode shape vector. Moreover, it is also confirmed that this mode can be stably identified under all the circumstances with the addition of tower measurements. An important lesson learned from this discovery is that the problem of mode identifiability usually comes from the lack of proper measurements at the right locations.
机译:在操作模态分析的应用中可能存在无法始终从不同的测量数据集中识别出的缺陷模式,例如大型土木结构中的随机子空间识别技术。基于最近在不同风激励条件下使用斜拉桥的长期环境振动测量结果的一项工作,通过将同一座桥作为测试台来提出基准问题,以进一步加强对模式识别性的探索。为了系统地评估此基准问题,扩展了基于替代稳定图和分层筛选过程的最新开发的SSI算法,并将其应用于本研究中以研究几套已知和盲目的监测数据。对精心挑选的案例的评估清楚地将交通激励的影响与目标激励模式的可识别性与风激励的影响区分开。随后建议主要由过往交通引起的甲板垂直加速度振幅的附加上限,以补充先前确定的风速下限。仔细检查在不同激励条件下的缺陷模式的形状矢量会导致这种模式实际上是由中心塔架的运动引起的。包含塔架测量值的分析通过在扩展模式形状矢量中的塔架位置产生主要分量,从而可靠地验证了这一假设。此外,还证实了通过添加塔架测量值,可以在所有情况下稳定地识别此模式。从这一发现中学到的重要教训是,模式可识别性的问题通常是由于在正确的位置缺少适当的测量结果而引起的。

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