Structural floor acceleration recorded by the safety network of buildings is essential for detecting and assessing the state of structural damage after earthquake disasters. However, the deployment of high-quality sensors on each floor is not always practical. An effective solution is the deployment of low-quality sensors using denoising methods to suppress the measured noisy signal. In traditional filtering-based denoising methods, careful manual selection of filter parameters is required, which is not only complex to implement, but also incapable of dealing with records with high-level noise. To address this problem, a Generative Adversarial Network (GAN) denoising method called DeGAN is proposed. The results for the testing set revealed that DeGAN was more efficient at denoising high-level noise compared to the Discrete Wavelet Transform (DWT)-based method. Furthermore, a new dataset which contains simulation noise and real noise, and a set of shaking table experimental data were utilized to evaluate the denoising performance and robustness of DeGAN. The results demonstrated that DeGAN outperformed the DWT-based method, UNET method and ResNet method in terms of the SNR of the denoised data.
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