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Identification of parametric variations of structures based on least squares estimation and adaptive tracking technique

机译:基于最小二乘估计和自适应跟踪技术的结构参数变化识别

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摘要

An important objective of health monitoring systems for civil infrastructures is to identify the state of the structure and to detect the damage when it occurs. System identification and damage detection, based on measured vibration data, have received considerable attention recently. Frequently, the damage of a structure may be reflected by a change of some parameters in structural elements, such as a degradation of the stiffness. Hence it is important to develop data analysis techniques that are capable of detecting the parametric changes of structural elements during a severe event, such as the earthquake. In this paper, we propose a new adaptive tracking technique, based on the least-squares estimation approach, to identify the time-varying structural parameters. In particular, the new technique proposed is capable of tracking the abrupt changes of system parameters from which the event and the severity of the structural damage may be detected. The proposed technique is applied to linear structures, including the Phase I ASCE structural health monitoring benchmark building, and a nonlinear elastic structure to demonstrate its performance and advantages. Simulation results demonstrate that the proposed technique is capable of tracking the parametric change of structures due to damages.
机译:用于民用基础设施的健康监控系统的重要目标是识别结构的状态并在发生损坏时进行检测。基于测得的振动数据的系统识别和损坏检测近来受到了相当大的关注。通常,结构的损坏可能会通过结构元素中某些参数的变化(例如刚度降低)来反映。因此,开发能够检测诸如地震之类的严重事件期间结构元素的参数变化的数据分析技术非常重要。在本文中,我们提出了一种基于最小二乘估计方法的自适应跟踪技术,以识别随时间变化的结构参数。特别是,提出的新技术能够跟踪系统参数的突然变化,从中可以检测事件和结构破坏的严重性。所提出的技术被应用于线性结构,包括第一阶段ASCE结构健康监测基准建设,以及非线性弹性结构,以证明其性能和优点。仿真结果表明,所提出的技术能够跟踪由于损伤引起的结构参数变化。

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