Bridge anomaly data identification method based on statistical feature mixture and data augmentation through forwarding difference

Identifying abnormal data in the structural health monitoring system is of vital importance for correctly evaluating the structural service status. For the monitored data of a long-span cable-stayed bridge, this paper proposed a method to identify abnormal data, primarily including data augmentation through forwarding difference, and statistical feature hybrid. The average prediction results of the test set showed that the proposed method can significantly improve the classification accuracy of anomaly data compared to directly training the original samples. Besides, the comparison results of the confusion matrix illustrated that the prediction results based on classifiers of random forest and decision tree were more robust, and using the former as the classifier can gain better recognition performance.

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