After a strong earthquake, it is crucial to evaluate accurately the health of structures in order to decide whether they can continue to be used. Isolation techniques are well known for enhancing the seismic performance of structures; however, a large response displacement anticipated in the design will likely impact the expansion joints. The occurrence of any damage or impact involves a large disturbance in the system or measurement equations. The Kalman filter (KF) is effective and reliable under proper conditions, but a simple simulation may show disrupted stability conditions after a large disturbance, causing a temporary filter divergence. If the filter design cannot be rapidly adjusted, an overall filter divergence may occur, preventing an accurate evaluation of structural health. This study proposes a performance recovery strategy for the unscented KF (UKF). Rather than identifying optimal parameter estimates at the current instant, the filter meets the stability conditions and asymptotically approaches the true estimates. The measurement noise is adaptively adjusted to bound the true noise covariance. Once the filter divergence is identified based on the expected measurement residual error, the state covariance is adjusted by a covariance-matching technique to bound the true error covariance. After sufficient measurements are obtained, the state covariance is reduced to a low level, indicating filter convergence and a reliable estimation. The effectiveness of the proposed approach is numerically validated for an isolation bridge and building under several scenarios, and two existing UKF variants, which adaptively estimate the system and measurement noise covariances, are compared.

中文翻译：

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用于隔离结构参数估计的具有性能恢复策略的无迹卡尔曼滤波器

强烈地震过后，准确评估结构的健康状况以决定其是否可以继续使用至关重要。众所周知，隔离技术可以提高结构的抗震性能；但是，设计中预期的大响应位移可能会影响伸缩缝。任何损坏或影响的发生都涉及系统或测量方程中的大扰动。卡尔曼滤波器 (KF) 在适当的条件下是有效和可靠的，但简单的模拟可能会在大扰动后显示不稳定的条件，从而导致暂时的滤波器发散。如果不能快速调整过滤器设计，则可能会出现整体过滤器发散，从而无法准确评估结构健康状况。本研究提出了无味 KF (UKF) 的性能恢复策略。滤波器不是在当前时刻识别最优参数估计，而是满足稳定性条件并渐近地接近真实估计。测量噪声被自适应地调整以限制真正的噪声协方差。一旦基于预期的测量残差识别出滤波器散度，就通过协方差匹配技术调整状态协方差以限制真实误差协方差。在获得足够的测量值后，状态协方差降低到较低水平，表明滤波器收敛和可靠的估计。所提出方法的有效性在几种情况下对隔离桥和建筑物以及两个现有的 UKF 变体进行了数值验证，