In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the applicability of SHM techniques, such as condition assessment and damage identification. The current mainstream data-driven method, generative adversarial networks (GAN), suffers from convergence difficulty, limiting the accuracy and efficiency of response reconstruction. In this study, a conditional diffusion model with data fusion (DF-CDM) is proposed for structural dynamic response reconstruction. The original unsupervised diffusion model is improved by introducing the conditional input and modifying the deep denoising neural network to achieve supervised learning. Besides, data fusion is developed to further utilize the frequency-domain information and improve the reconstruction quality of the time-domain signals. The proposed model is validated on a three-span continuous bridge. Results show that the diffusion model with data fusion achieves the highest accuracy on the test set with , RMSE and MAE equaling 0.821, 0.0053 and 0.0042 respectively. Compared with the state-of-the-art GAN model, the diffusion model without the adversarial modules has a more stable training process and better reconstruction performance in both time and frequency domains. The modal identification and robustness analysis further verify the effectiveness of the proposed model. The proposed diffusion model with data fusion achieves high-quality structural response reconstruction, guaranteeing the applicability and reliability of subsequent response-based structural analysis.

中文翻译：

---

DF-CDM：用于结构动态响应重建的数据融合条件扩散模型


在结构健康监测（SHM）系统中，不可避免地会发生数据丢失，并降低了结构健康监测技术的适用性，例如状态评估和损伤识别。当前主流的数据驱动方法生成对抗网络（GAN）存在收敛困难，限制了响应重建的准确性和效率。在本研究中，提出了一种用于结构动态响应重建的数据融合条件扩散模型（DF-CDM）。通过引入条件输入并修改深度去噪神经网络对原有的无监督扩散模型进行改进，实现监督学习。此外，还发展了数据融合以进一步利用频域信息并提高时域信号的重构质量。所提出的模型在三跨连续桥上进行了验证。结果表明，数据融合的扩散模型在测试集上达到了最高的准确率，RMSE 和 MAE 分别等于 0.821、0.0053 和 0.0042。与最先进的 GAN 模型相比，没有对抗模块的扩散模型在时域和频域上具有更稳定的训练过程和更好的重建性能。模态识别和鲁棒性分析进一步验证了所提模型的有效性。所提出的数据融合扩散模型实现了高质量的结构响应重建，保证了后续基于响应的结构分析的适用性和可靠性。