A method for reconstruction of structural health monitoring data using WGANGP with U-net generator

Data loss issue often occurs in structural health monitoring (SHM), which can undermine the reliability of structural condition diagnosis and prognosis. Neural networks are commonly employed for reconstruction of missing SHM data by modeling the correlation between intact and missing signals. The existing neural network methods use deeper architectures to capture complex data correlations. As network depth increases, the ability to preserve both low- and high-level features diminishes, and network training becomes challenging, which reduces data reconstruction accuracy. This study proposes a data reconstruction method that utilizes a Wasserstein generative adversarial network containing a gradient penalty term with a U-net generator. Multiple improvements are made to the generative adversarial network to enhance the reconstruction performance. First, the U-net is used as a generator, and the signal features at low and high levels are preserved based on the skip-connection technique. The U-net is pre-set with multiple layers to enhance the reconstruction accuracy. Second, a mean square error (MSE) term is added to the loss function. The MSE coefficient is proposed to balance adversarial training and reconstruction feature learning. Third, the Wasserstein distance is introduced to replace the cross-entropy loss function of traditional generative adversarial networks, avoiding gradient vanishing and exploding. The reconstruction performance of the proposed method is evaluated on a computational bridge model and Canton Tower, and the influence of reconstruction parameters on the accuracy of reconstruction results is discussed in detail. The reconstructed data by the proposed method closely matches the original data in both the time domain and frequency domain. The time–frequency characteristics of the acceleration data can be accurately reconstructed, demonstrating the effectiveness of the reconstructed signals in data analysis. By comparing with typical neural network methods, it is found that the proposed method has higher reconstruction accuracy.