Output‐only identification of civil structures is a challenging yet in escapable task, as input excitations are unmeasurable in many real‐world situations. This paper presents a new output‐only technique to jointly identify the model parameters of adjacent buildings from their recorded seismic responses. Classic output‐only identification techniques are based on various limiting assumptions about the unknown input signal (e.g., whiteness), which are often violated for earthquakes. While other advanced output‐only (e.g., blind) identification techniques have recently been developed, their applications can be limited due to their computational costs and sensor density requirements. The proposed method assumes that two adjacent buildings experience the same foundation input motion during an earthquake. Both systems are then identified by investigating the uncommon portions of their measured responses through a cross‐relation (CR) method. To relax the CR method's nominally restrictive identifiability criteria, mechanics‐based finite element (FE) models of the two buildings are utilized and updated, instead of direct identification of their impulse response functions (IRFs). An unscented Kalman filter (UKF) is employed for model updating, which also quantifies the estimation uncertainties. Having identified the buildings, their common input excitation is recovered through a stable deconvolution approach. The overall method is verified through a synthetic example, and identifiability conditions are investigated. While the proposed method is limited to linear‐elastic ranges of response, it is capable of handling limited measurements (e.g., single biaxial accelerometer for each building); therefore, it offers an ideal solution for identification of structural models of sparsely instrumented buildings along with their foundation input motions at regional scales.

中文翻译：

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稀疏地震响应记录中相邻建筑物的仅输出模型更新以及它们共同激发的识别

仅在输出中识别土木结构是一项具有挑战性但可逃避的任务，因为在许多实际情况下无法测量输入激励。本文提出了一种仅用于输出的新技术，可以从记录的地震响应中共同识别相邻建筑物的模型参数。经典的仅输出识别技术基于对未知输入信号（例如白度）的各种限制假设，地震常常违反这些假设。尽管最近开发了其他一些仅用于输出的高级识别技术，但由于其计算成本和传感器密度要求，它们的应用可能受到限制。所提出的方法假设在地震期间两座相邻建筑物经历相同的地基输入运动。然后，通过交叉关系（CR）方法调查所测量响应的不常见部分，从而识别这两个系统。为了放宽CR方法的名义上的限制性可识别性标准，将利用和更新两座建筑物的基于力学的有限元（FE）模型，而不是直接识别其冲激响应函数（IRF）。使用无味卡尔曼滤波器（UKF）进行模型更新，这也量化了估计的不确定性。识别建筑物后，可以通过稳定的反褶积方法恢复其共同的输入激励。通过一个综合实例验证了整体方法，并研究了可识别性条件。虽然建议的方法仅限于线性弹性响应范围，但它能够处理有限的测量值（例如，每个建筑物使用单个双轴加速度计）；因此，它为识别稀疏建筑的结构模型及其在区域范围内的地基输入运动提供了理想的解决方案。