Abstract:
As an embedded multifunctional piezoelectric transducer, smart aggregate has the advantages of wide frequency band, fast response, and integration with concrete. To characterize the damage degree at different stages, it is necessary to use the wavelet decomposition method to process the collected signal. All time domain signals are converted into energy by n-order wavelet decomposition, and finally the damage factor is characterized by calculating the root mean square error. However, the process needs to consume a large amount of computing power for nearly hundreds of groups of signals. Additionally, the high frequency of signal acquisition slows down the calculation process and makes the entire monitoring process lag. To overcome the above problems, this paper proposes a semi-supervised monitoring method based on adversarial neural network and monitoring signal reconstruction, which can extract damage-sensitive features according to the difference between the reconstructed signal and the original health signal in the frequency domain and optimize the pseudo-clustering of fuzzy clustering. The tag propagates, thereby indirectly assessing the damage status of the structure by reading the monitoring signal. The advantage of the established structural state evaluation model is that through the logic of training and verification before the loss, and the rapid prediction after the loss, the time-consuming training and verification links are pre-positioned to ensure the efficiency of the later prediction and evaluation.