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Generalized algorithms for the identification of seismic ground excitations to building structures based on generalized Kalman filtering under unknown input

机译:基于未知输入识别基于广义Kalman滤波的地震地面激励识别的广义算法

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摘要

The exact information of seismic excitation and structural state is a prerequisite for structural seismic safety assessment and vibration control. When the seismic excitation to a structure is not measured, the seismic excitation can be identified as an inversed problem from measured structural responses. Although some relevant approaches have been developed, there are certain limitations or drawbacks in the existing approaches. To circumvent these problems, two generalized algorithms are proposed for the identification of seismic ground excitation to multi-story and tall buildings, respectively. When the seismic ground excitation to a structure is not measured, the data measured by a structural health monitoring system are structural absolute responses. So the structural motion equation in the absolute coordinate system is derived, in which the unknown seismic ground excitation is treated as unknown external force acting on the structure. First, the identification of unknown seismic excitations to multi-story building structures is studied. A generalized Kalman filtering under unknown input is proposed for the identification of structural state and unknown seismic excitation without the observation of structural absolute acceleration responses at the location of unknown external force. The derivation of the proposed generalized Kalman filtering under unknown input is based on the classical Kalman filter, but is more general than the existing identification approaches based on Kalman filter with unknown input in the deployments of accelerometers in the building structure. Then, it is extended to explore the identification of unknown seismic excitations to tall building structures. To avoid substructural identification from the top to bottom in a sequential manner, the motion equation in absolute coordinate system is reduced by modal expansion. Moreover, instead of the identification of unknown modal forces in previous approaches, the seismic excitation is directly identified without increasing the number of unknown forces. To demonstrate the proposed algorithms, numerical examples of identifying seismic excitations to a 6-story shear building and an 18-story tall building are investigated.
机译:地震激励和结构状态的确切信息是结构地震安全评估和振动控制的先决条件。当未测量结构的地震激发时,可以将地震激发识别为来自测量的结构响应的反向问题。虽然已经开发了一些相关方法,但现有方法中存在某些限制或缺点。为了规避这些问题,提出了两个广义算法,分别用于识别多层和高层建筑物的地震地面激发。当未测量结构的地震接地激发时,由结构健康监测系统测量的数据是结构绝对反应。因此,导出绝对坐标系中的结构运动方程,其中未知的地震地面激发被视为作用在结构上的未知外力。首先,研究了对多层建筑结构的未知地震激励的识别。提出了在未知输入下的广义卡尔曼滤波,用于识别结构状态和未知的地震激发,而不观察在未知的外力位置的结构绝对加速度响应。在未知输入下提出的广义Kalman滤波的推导基于经典的Kalman滤波器,但是比基于Kalman滤波器的现有识别方法更通用,其具有在建筑物结构中的加速度计的部署中的未知输入。然后,扩展到探索高层建筑结构的未知地震激励的识别。为了避免从顶部到底部以顺序方式识别,绝对坐标系中的运动方程被模态扩展减少。此外,代替在先前的方法中识别未知的模态力,而不是在不增加未知力的数量的情况下直接识别出地震激发。为了展示所提出的算法,研究了识别震动刺激到6层剪切建筑和18层高楼的数值例子。

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