A crucial step when identifying the modal signature of systems using growing order parametric methods consists in discriminating spurious modes from physical modes. In this paper, a three-stages clustering strategy is presented in a fuzzy framework for automating this selection process in the context of Input/Output and Output-Only identification. The novelty and strong point of the approach lies in the first stage where, after computation of single mode validation indicators, a modified fuzzy c-means clustering procedure is developed for performing a first partition. It is shown how the membership function obtained for the cluster of physical modes can be interpreted as a new synthetic modal indicator and helps with pole-splitting detection, outlier rejection and generally improves the final modal parameters estimation. The developed methodology does not involve any user-specified threshold and can be used for discriminating modes produced by any methodology consisting in fitting a growing order model to experimental data of any type. In this paper, accelerations measured during the SMART2013 shaking-table test campaign are processed using data-driven state-space identification algorithms. The automated selection process is used for tracking the modal signature of a trapezoidal shaped reinforced-concrete specimen using in turn stochastic and combined deterministic-stochastic algorithms, defining for the latter the movement of the shaking table as input. Variations in the modal signature are then correlated to the damage actually observed on the specimen and a comparison between Output-Only and Input/Output results is made in order to estimate the interaction between the specimen and the whole shaking table device.

使用增长阶参数方法识别系统的模式签名时的关键步骤在于将伪模式与物理模式区分开。在本文中，在模糊框架中提出了一种三阶段聚类策略，用于在输入/输出和仅输出识别的情况下自动执行此选择过程。该方法的新颖性和强项在于第一阶段，在此阶段中，在计算了单模验证指标之后，对模型进行了改进的模糊c均值开发了用于执行第一分区的群集过程。它显示了如何将为物理模式簇获得的隶属度函数解释为一种新的合成模态指标，并有助于极点分裂检测，离群值剔除并通常改善最终的模态参数估计。所开发的方法不涉及任何用户指定的阈值，并且可用于区分由将增长的订单模型拟合到任何类型的实验数据中的任何方法所产生的模式。在本文中，使用数据驱动的状态空间识别算法处理在SMART2013振动台测试活动中测得的加速度。自动选择过程用于通过使用随机和组合确定性-随机算法来跟踪梯形形状的钢筋混凝土标本的模态特征，并为后者定义振动台的运动作为输入。然后将模态特征的变化与实际在样品上观察到的损伤相关联，并比较“仅输出”和“输入/输出”结果，以估计样品与整个振动台设备之间的相互作用。