Structural health monitoring under environmental and operational variations using MCD prediction error

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.