This paper proposes a novel technique that aims at detecting the effect of damage on structural frequency signals as “bad” outliers. To this end, a procedure is developed based on the Variational Mode Decomposition (VMD), Minimum Covariance Determinant (MCD), and Recurrent Neural Network (RNN) with Bi-directional Long-Short Term Memory (BiLSTM) cells. The VMD is first used in a pre-processing stage to denoise the signals and remove the seasonal patterns in them. Then, the proposed method seeks to learn the rules behind calculation of the Mahalanobis distances of the points from their distribution, using the parameters obtained from the MCD algorithm, through training an RNN on signals obtained from the inferior state of the structure (healthy state). It will be shown that, since the rule behind the effect of damage on the Mahalanobis distances has not been learnt by the trained RNN, the prediction errors of these values will increase significantly as soon as damage occurs using the data obtained from the posterior state of the structure (including damage). The performance of the proposed method is first tested on a numerical example and further validated through solving an experimental example of the Z24 bridge. Moreover, the proposed method is compared against a PCA-based method. The results demonstrate the superiority of the proposed method in long-term condition monitoring of civil infrastructures. The proposed method is an output-only condition monitoring method that requires only a couple of lowest structural natural frequency signals measured over a long-term monitoring of the structure. Therefore, it is recommended for cases when the measurements from the EOV are not available. Also the proposed method can be used along with other output-only or input-out methods to either improve or confirm the validity of their results.

本文提出了一种新技术，旨在将损坏对结构频率信号的影响检测为“坏”异常值。为此，基于变分模式分解 (VMD)、最小协方差行列式 (MCD) 和具有双向长短期记忆 (BiLSTM) 单元的循环神经网络 (RNN) 开发了一种程序。VMD 首先用于预处理阶段，以对信号进行去噪并去除其中的季节性模式。然后，所提出的方法试图通过使用从 MCD 算法获得的参数，通过对从结构的劣等状态（健康状态）获得的信号训练 RNN，来学习从其分布计算点的马氏距离背后的规则. 将表明，由于损伤对马氏距离的影响背后的规律还没有被训练的 RNN 学习到，使用从结构的后验状态（包括损伤）获得的数据一旦发生损伤，这些值的预测误差就会显着增加. 该方法的性能首先在一个数值例子上进行了测试，并通过解决 Z24 桥的一个实验例子进一步验证。此外，将所提出的方法与基于 PCA 的方法进行了比较。结果证明了所提出的方法在民用基础设施的长期状态监测中的优越性。所提出的方法是仅输出状态监测方法，它只需要在结构的长期监测中测量的几个最低结构固有频率信号。所以，当 EOV 的测量值不可用时，建议使用此方法。此外，所提出的方法可以与其他仅输出或输入输出方法一起使用，以改进或确认其结果的有效性。